Computational single-neuron mechanisms of visual object coding in the human temporal lobe

Domain: neuroscience
Published: 2026-02-01
Editorial outcome: minor_revision

Abstract
Understanding how the human brain encodes visual objects involves deciphering the neural computations and circuits in the temporal lobe. Here, we recorded intracranial EEG from the human ventral temporal cortex (VTC) and medial temporal lobe (MTL), as well as single-neuron activity in the MTL, to investigate the computational mechanisms of neural object coding. The VTC exhibited axis-based feature coding, and a neural feature space could be constructed using VTC neural axes, within which visual objects clustered according to high-level categorical relationships. Importantly, MTL neurons encoded receptive fields within this VTC neural feature space, exhibiting selective responses to objects that shared perceptual and conceptual similarities. This computational framework, therefore, explains how dense, feature-based representations in the VTC are transformed into sparse, high-level representations in the MTL. We further validated our findings using an additional dataset with different stimuli. Notably, we uncovered the physiological basis of this computational framework by demonstrating VTC-MTL interactions at multiple levels. Together, our neural computational framework provides a mechanistic understanding of the neural processes underlying object recognition.

Introduction
Human effortlessly recognize everyday objects within a fraction of a second, despite tremendous visual variations in appearance1,2. The ability to rapidly extract object identity is fundamental to various high-level cognitive functions essential for survival3. Understanding how the human brain achieves object recognition has garnered significant attention over the past three decades due to its importance2,4–7. However, we still lack a mechanistic understanding of this process, which underlies a complex computational feat8–10.

Object recognition is primarily supported by the ventral visual pathway, which spans the ventral temporal cortex (VTC) and extends into the medial temporal lobe (MTL), including the amygdala and hippocampus11,12. Within this pathway, neurons encode objects at different levels of abstraction, transitioning from lower-level, feature-based representations in the VTC to higher-level, conceptual representations in the MTL. Specifically, the VTC represents objects using axis-based coding, where neural responses are organized along key feature dimensions such as shape, texture, and curvature13,14. Each neural axis represents a fundamental direction in this high-dimensional feature space, capturing variations in object appearance. Non-human primate studies15–18 have shown that axis-based coding enables efficient and flexible object representation, allowing different objects to be distinguished based on their position along multiple neural axes. Axis-based coding in the VTC results in dense coding, meaning many neurons participate in encoding the fine-grained details of an object’s visual features. This requires a broad and distributed population of neurons19–22, with each neuron responding to multiple objects that share specific visual features. In contrast, the MTL employs sparse coding, where only a small subset of neurons respond selectively to specific object categories23–26, abstracting away lower-level visual details25,26. The transformation from dense, feature-based coding in the VTC to sparse, category-based coding in the MTL is critical for efficient memory storage and retrieval. However, the precise neural computational mechanisms underlying this transformation remain unclear.

To address this question, our recent work suggests that MTL neurons encode “receptive fields” (i.e., coding regions) within a visual feature space, which is constructed using deep neural network (DNN) features (rather than neural responses), for faces27 and objects28—a phenomenon we refer to as region-based coding. Rather than tracking individual visual features, these neurons selectively respond to specific regions (i.e., receptive fields) within the visual feature space that contain objects with similar visual features. Critically, region-based coding can provide a potential link between visual feature processing in the VTC and conceptual representations in the MTL (Fig. 1). A neural feature space can be constructed from VTC neural axes using principal component analysis (PCA). Notably, the dimensions of this space reflect principal variations in object categories (e.g., ranging from natural to artificial objects or from animate to inanimate objects; see Methods)18,29; and within this space, objects with similar high-level abstract features cluster together, demonstrating clustering that reflects their perceptual and conceptual relationships. If MTL neurons exhibit receptive fields (i.e., coding regions) within this neural feature space and respond to stimuli that fall into these regions and share similar high-level properties, they will exhibit category-selective responses. Therefore, this neural computational framework can explain how the brain transforms feature-based representations in the VTC into more abstract and integrative representations in the MTL.

To test this hypothesis, we simultaneously recorded microscopic single-neuron activity in the human MTL and mesoscopic intracranial electroencephalography (iEEG) in both the VTC and MTL while neurosurgical patients viewed 500 naturalistic object images spanning diverse categories. Specifically, we hypothesized that (1) the human VTC exhibits axis coding of visual features similar to that observed in non-human primates18,30 (i.e., iEEG responses parametrically vary as a function of DNN features; Fig. 1 bottom left), (2) VTC neural axes collectively form a neural feature space after dimensionality reduction using PCA (Fig. 1 bottom middle), and (3) the human MTL exhibits region coding within the VTC neural feature space (i.e., MTL neurons selectively respond to objects that occupy similar positions within this space; Fig. 1 bottom right). To further support these hypotheses, we investigated inter-areal functional connectivity between the VTC and MTL at multiple levels, including iEEG phase-locking, Granger causality, and spike–field phase consistency. Additionally, we validated and generalized our findings using an additional dataset with different stimuli. Together, this study provides critical insights into the neural computational mechanisms of how the brain transforms detailed visual feature information into abstract, category-specific representations of visual objects.

Results
Neural response to visual objects in the VTC and MTL
Fourteen neurosurgical patients (9 females; Supplementary Table 1) implanted with depth electrodes participated in this study. Participants viewed 500 naturalistic ImageNet object images from 50 different categories31 while performing a one-back task (Fig. 2a). They performed well in the task, with an average accuracy of 86.38% ± 11.57% (mean ± SD), suggesting that they were paying attention to the task. This task has been shown to effectively maintain participants’ attention while efficiently presenting a large number of images in a single session27,32. We recorded iEEG from 1074 channels: 692 in the VTC (n=278 for the fusiform gyrus [FG] and n=414 for the inferior temporal gyrus [ITG]; Fig. 2b left panel) and 382 in the MTL (n=100 for the posterior hippocampus [PH], n=105 for the anterior hippocampus [AH], and n=177 for the amygdala; Fig. 2b right panel). We used high-gamma power (HGP; 70–170 Hz) from the iEEG signals as the response, which reflects the average neuronal firing of local neural populations33–35.

We first examined the visual responsiveness of the iEEG channels across different brain areas (Fig. 2c, e). A significant percentage of visually responsive channels was observed in each subregion of both the VTC (FG: n=201, 72.30%, binomial test against 5% chance level: P<10−20; ITG: n=141, 34.06%, P<10−20) and MTL (PH: n=34, 28.57%, P<10−20; AH: n=47, 32.87%, P<10−20; amygdala: n=78, 42.86%, P<10−20), with the FG containing a significantly higher proportion of visually responsive channels compared to all other regions (Fig. 2c; χ2 test: Ps < 0.0001 for all comparisons between the FG and other regions). The results remained consistent using a linear mixed-effects model where patient index was included as a random variable (Percentage ~ Region + 1|Patient; Ps < 0.05 for all comparisons between the FG and other regions). Comparable percentages of visually responsive channels were observed in both the left and right hemispheres for each region of interest (ROI; Fig. 2c).

We next analyzed the object category that elicited the greatest HGP for each visually responsive channel (Fig. 2d). We found that the right FG primarily encoded animal/face stimuli (e.g., cat, dog, young mammal, and gymnast [people]; shown in red colors in Fig. 2d) whereas the left FG and the bilateral ITG and MTL regions exhibited more heterogeneous response profiles, with maximal responses to various categories across different channels (Fig. 2d). Specifically, compared with the left FG (n=29, 25.22%), visually responsive channels in the right FG (n=44, 51.16%; χ2 test: P=0.0002) had a significantly higher proportion of maximal responses to animal stimuli; and this was particularly the case for people/human faces (Fig. 2d; right FG: n=33, 38.37%; left FG: n=10, 8.70%; P=3.85×10−7), consistent with findings from neuroimaging and lesion studies36–38 (see Supplementary Results and Supplementary Fig. 1 for detailed analyses of object category selectivity).

To investigate the temporal dynamics of object encoding across the VTC and MTL, we estimated the response onset latency for each ROI using a trial-by-trial analysis (see Methods). The response onset latency in the FG (138.5 ± 32.0 ms [median ± SD]) was significantly shorter than in all other regions (ITG: 150.5 ± 21.7 ms; PH: 146.5 ± 14.9 ms; AH: 148.5 ± 15.6 ms; all Ps < 0.05; Fig. 2e, f), while the response onset latency in the amygdala (158.5 ± 20.5 ms) was significantly longer than in all other regions (all Ps < 0.05; Fig. 2e, f). The results remained consistent when using a linear mixed-effects model with patient index included as a random variable (Latency ~ ROI + 1|Patient; all Ps < 0.05). Similar results were also derived using response peak latency. Interestingly, given comparable latency, the ITG (primarily anterior ITG in our case) maybe involved in visual object processing in parallel with the MTL, despite its anatomical proximity to the FG.

Together, our results delineate the encoding of naturalistic visual objects along the ventral visual pathway.

Axis-based feature coding in the VTC
Neurons in the macaque inferotemporal cortex have been shown to represent visual faces or objects by parametrically encoding specific visual features15,17,18, exhibiting axis-based feature coding. To determine whether the human brain also exhibits axis coding, we extracted visual features from each stimulus image using a pre-trained deep neural network (DNN) and modeled the neural responses of each visually responsive channel to these features using partial least squares (PLS) regression30,32,39 (see Methods for details; see Supplementary Fig. 2a, b for selection and control analyses of different DNN layers).

Indeed, we observed strong axis coding in the bilateral FG (left: n=47, 40.86%, binomial test: P<10−20; right: n=38, 44.19%, P<10−20; see Fig. 3a and Supplementary Fig. 2c for representative channels) and left ITG (n=18, 24.66%, P<10−20; Fig. 3b). The preferred axes of these channels were highly interpretable. For instance, a channel in the FG encoded changes from natural to artificial objects (Fig. 3a), while a channel in the ITG encoded changes along the animacy axis (Fig. 3b; see Methods for the distinction of these axes). Interestingly, the left ITG had a significantly higher percentage of axis-coding channels than the right ITG (Fig. 3c; χ2 test: P=0.002), indicating laterality in axis coding. Notably, we observed that axis-coding channels were predominantly distributed in the medial VTC rather than the lateral VTC (Fig. 3d). On the other hand, the MTL exhibited minimal axis coding (all binomial Ps > 0.05, except for the left AH [n=4, 18.18%, P=0.004] and right AH [n=4, 16.00%, P=0.007]; Fig. 3c, d), consistent with results from human single-neuron recordings that showed an absence of axis coding in the MTL27,28. All of the above results were further confirmed using quantitative measures of axis-coding strength (Fig. 3d, e; see Methods). Notably, across all visually responsive channels, the axis model explained significantly more variance compared to the category model in both the FG (axis: 0.63 ± 0.038 [mean ± SD]; category: 0.17 ± 0.087; two-tailed paired t-test: t(200)=91.94, P<10−20, d=6.75, 95% CI=[0.44, 0.46]) and ITG (axis: 0.62 ± 0.030; category: 0.12 ± 0.046; t(73)=63.34, P<10−20, d=12.86, 95% CI=[0.49, 0.50]; Supplementary Fig. 2d; similar results were obtained when controlling for the input dimensionality of the axis model and the number of object categories used in the category model).

To further characterize the encoded feature axes of the axis-coding channels and compare them with known object dimensions from previous studies18, we performed PCA on the object visual features to derive orthogonal feature dimensions (principal components [PCs]) from our stimuli. Visualization of the PCs revealed the well-known natural–artificial, spiky–stubby, and inanimate–animate dimensions (i.e., the first three PCs; Supplementary Fig. 3a), consistent with prior studies (see also Methods for the distinction of these dimensions)18,29. We then examined how the tuning axis of each axis-coding channel aligned with these feature dimensions using Pearson correlation. We found that across axis-coding brain areas (i.e., FG, ITG, and AH), the vast majority of the tuning axes were correlated with the first three feature dimensions (Supplementary Fig. 3b). In particular, the first PC, representing the natural–artificial dimension, was most frequently (FG: 60.00%; ITG: 56.52%; AH: 75.00%; Supplementary Fig. 3b) and most strongly (Supplementary Fig. 3c) aligned with the extracted neural axes (see Supplementary Fig. 3d for individual channels), followed by the third PC, representing animacy (FG: 28.24%; ITG: 21.74%; AH: 25.00%). Unlike findings from the monkey IT cortex18, axis-coding channels in the human VTC were less likely—and less strongly—to encode the spiky–stubby dimension (the second PC; FG: 11.76%; ITG: 21.74%; AH: 0%; Supplementary Fig. 3b, c). These results were further replicated using a separated dataset (Supplementary Fig. 4; see below).

To investigate the temporal dynamics of visual feature encoding along the ventral visual pathway, we measured the response latency of each axis-coding channel. Consistent with visual responsiveness, axis coding began earliest in the FG (151 ms; see Fig. 3f for each ROI in the left hemisphere and Supplementary Fig. 2e for the right hemisphere), followed by the ITG (231 ms) and AH (491 ms). Notably, the onset latency of axis coding varied as a function of the y-coordinate in the MNI space within the FG (Fig. 3g), suggesting a local hierarchical processing of visual objects. To control for differences in axis-coding strength across channels (as stronger axis coding may lead to earlier onset), we normalized each channel’s axis-coding strength to its maximum before calculating the onset latency of axis coding and obtained similar results.

Together, our results reveal axis-based feature coding in the human VTC for visual object processing, aligning with findings from non-human primates. Latency analysis further suggests a hierarchical progression of axis coding, starting in the FG and moving to the ITG and AH. Importantly, representational similarity analysis (RSA) of the neural pairwise distance of visual objects (see Methods) showed that visual objects were similarly represented in axis-coding channels in the VTC and category-coding channels in the MTL (Fig. 3h; see Supplementary Fig. 5 for individual dissimilarity matrices), indicating information flow between these brain areas. We next focused on the mechanisms underlying the transition of visual object coding from the VTC to the MTL.

Single neurons in the human MTL are tuned to regions in the VTC neural feature space
We have demonstrated that single neurons in the human amygdala and hippocampus exhibit region-based feature coding. Specifically, these neurons respond to faces or objects that occupy a particular region of the visual feature space, constructed using DNN unit activations27,28. DNN units represent linear combinations of visual features, analogous to the neural axes in the VTC (Fig. 3). Therefore, we hypothesized that MTL neurons encode a tuning region / receptive field (i.e., they become selective to stimuli within this tuning region / receptive field) in the VTC neural feature space, with the axes of this space encoded by VTC neurons (Fig. 1). This provides a computational framework linking VTC and MTL representations, allowing us to understand how perceptual representations in the VTC are translated into abstract conceptual representations in the MTL.

To test this hypothesis, we first constructed the VTC neural feature space using axis-coding channels from the VTC (see Methods). We extracted the most preferred tuning axis for each axis-coding channel (i.e., the first PLS component; see Fig. 3a, b for examples) and then pooled these axes across channels (Fig. 1). PCA was applied to these axes to create a two-dimensional neural feature space, representing the primary visual information encoded in the VTC (Fig. 4a; see also Supplementary Fig. 6). Indeed, the VTC neural feature space exhibited an organized structure (Fig. 4a; Supplementary Fig. 6), where the horizontal dimension represented the transition from natural objects to human-made artificial objects, and the vertical dimension reflected changes in animacy. Images from the same object category were clustered together.

To identify MTL region-coding neurons in the VTC neural feature space, we projected an MTL neuron’s response to each object onto the VTC neural feature space (i.e., multiplying the firing rate for each object to its position in the VTC neural feature space to create a response-weighted 2D feature map; Fig. 4b, c middle). This revealed that a subset of MTL neurons was selective for objects clustered in specific regions of the VTC neural feature space (Fig. 4b, c middle). We refer to these MTL neurons as exhibiting region-based feature coding in the VTC neural feature space. To formally quantify this tuning (see Methods), we estimated a continuous spike density map within the 2D neural feature space (Fig. 4b, c upper right) by smoothing the discrete firing rate map (Fig. 4b, c middle) using a 2D Gaussian kernel and applied a permutation test (1000 runs; Fig. 4b, c lower right) to identify regions with significantly higher spike densities than expected by chance (outlined and visualized in Fig. 4b, c middle panels; significant pixels were selected with a permutation P<0.01 and a cluster size threshold). These regions indicate the parts of the VTC neural feature space to which an MTL neuron was tuned.

We recorded from 928 single neurons in the MTL of 20 patients (34 sessions) during the same task as the VTC recordings. We found that 106 out of 928 MTL neurons (11.42%, binomial P=2.55×10−15) exhibited region coding in the VTC neural feature space (see Fig. 4d for the percentage of selected neurons in each MTL ROI). The number of object categories (Fig. 4e) and object images (Fig. 4f) covered by each tuning region indicates the size of the receptive field of a region-coding neuron within the VTC neural feature space. On average, these regions encompassed 10 to 15 categories and 20 to 30 images, suggesting that some tuning regions may contain only a subset of images from different categories. This image-level, rather than category-level, encoding also indicates that some region-coding neurons are tuned to continuous visual features shared across images. The tuning region of individual neurons spanned approximately 1.73%–7.12% of the neural feature space (Fig. 4g–i), while the total neural population we sampled covered around 33.44%–58.87% of the neural feature space. Additionally, we tested region coding using signals from iEEG channels in the MTL. However, fewer region-coding channels (16/511, 3.31%) were detected than expected by chance, indicating that region coding did not occur at the mesoscopic level.

Together, our results reveal region-based feature coding of MTL neurons within the VTC neural feature space, providing a computational framework for translating perceptual processing in the VTC into conceptual processing in the MTL.

Validation with an additional dataset
We further validated our findings using an additional dataset comprising Microsoft COCO images40. Here, we included 10 object categories but 50 images per category to better examine the neural coding of objects within a category. First, we confirmed axis-based coding in the VTC (including both the FG and ITG; see Fig. 5a for an example and Supplementary Fig. 7 for group results), consistent with our observations from the ImageNet dataset. Notably, the COCO dataset revealed similar feature axes encoded by the axis-coding channels (Fig. 5a right), suggesting that the VTC represents a general visual feature space that does not depend on specific image characteristics.

Importantly, in a subset of 8 patients (13 sessions), we recorded iEEG signals for the COCO images immediately after the ImageNet images, allowing for cross-validation between the datasets. We trained a PLS regression model using the ImageNet dataset and predicted neural responses to the COCO images (see Methods for details). Indeed, we observed a significant number of channels in the bilateral FG (left: n=32, 34.78%, binomial test: P<10−20; right: n=23, 50%, P<10−20; Fig. 5b, c), ITG (left: n=15, 26.79%, P<10−20; right: n=13, 23.64%, P<10−20), and left AH (n=5, 26.32%, P=0.0002), whose responses could be significantly predicted across datasets. Therefore, axis coding in the VTC was generalizable across datasets.

We next constructed the VTC neural feature space (Fig. 5d) using axis-coding channels selected from the above cross-validation procedure (Fig. 5b, c) and tested region coding in MTL neurons (n=487) using the COCO dataset (Fig. 5e–g; similar results were obtained when using neural axes derived solely from the COCO dataset [Supplementary Fig. 7]). Once again, we observed robust region coding. Interestingly, some MTL neurons encoded specific regions containing a subset of images from one or a few categories (e.g., Fig. 5e), indicating that region coding was not dependent on object categories but rather on VTC neural features. At the population level, we found a significant percentage of region-coding neurons in all three subregions of the MTL (Fig. 5f), with our sampled neurons covering 29.14% to 60.73% of the VTC neural feature space (Fig. 5g).

Together, we validated our findings on axis coding in the human VTC and region coding of MTL neurons within the VTC neural feature space using an additional dataset with different stimuli.

Functional connectivity between the VTC and MTL supports the coding transition mechanism
What is the physiological basis of the above computational framework? To address this question, we conducted a series of functional connectivity analyses between the VTC and MTL at both the neural network and circuit levels.

First, using phase-locking value (PLV) on bipolar re-referenced channel pairs between the VTC (FG and ITG) and MTL (PH, AH, and amygdala; see Methods for details), we observed significant synchronization (primarily in the lower frequency band [<20 Hz]) between VTC visually responsive channels and MTL category-selective channels in all pairs of VTC-MTL regions (Fig. 6a). A subset of VTC axis-coding channels (59.26%) exhibited significant phase-locking with MTL category-selective channels (Supplementary Fig. 8a). These results suggest that the VTC is functionally connected with the MTL for visual category coding.

In particular, while both VTC axis-coding and non-axis-coding channels increased synchronization with MTL channels in the low-frequency range after stimulus onset, VTC axis-coding channels exhibited significantly stronger synchronization compared to VTC non-axis-coding channels (Fig. 6b), suggesting that they engage the MTL more during object processing. The difference in PLV between the VTC and PH was primarily in the theta (3–8 Hz; two-tailed two-sample t-test across channel pairs: t(115)=2.22, P=0.029, d=0.48, 95% CI=[0.01, 0.24]; Fig. 6b) and alpha (7–13 Hz; t(115)=2.24, P=0.027, d=0.49, 95% CI=[0.017, 0.28]; Fig. 6b) frequency ranges, the difference in PLV between the VTC and AH was primarily in the theta frequency range (t(432)=4.56, P=6.62×10−6, d=0.45, 95% CI=[0.057, 0.14]; Fig. 6b), and the difference in PLV between the VTC and amygdala was primarily in the delta frequency range (1–3.5 Hz; t(253)=6.15, P=3.05×10−9, d=0.79, 95% CI=[0.098, 0.19]; Fig. 6b). Furthermore, by separately analyzing each pair of VTC and MTL subregions (Supplementary Fig. 9), we found that the results were largely driven by the FG, consistent with the findings shown in Fig. 3h.

Importantly, we further demonstrated that the strength of axis coding was significantly correlated with the strength of VTC-MTL connectivity specifically for axis-coding channels (Fig. 6c; VTC-AH: r(160)=0.32, P=4.32×10−5; VTC-amygdala: r(96)=0.26, P=0.010), but this was not the case for non-axis-coding channels (Fig. 6c; VTC-AH: r(270)=−0.094, P=0.12; VTC-amygdala: r(155)=0.035, P=0.66). Therefore, the stronger interaction between axis-coding channels and the MTL during visual processing, as well as the parametric relationship between axis coding and VTC-MTL connectivity, support the neural mechanism by which the VTC delivers visual feature information to the MTL for its subsequent processing. In addition, we obtained similar results when using ipsilateral channels only (Supplementary Figs. 8b, c and 9b) and when using an equal number of channels.

Second, feedforward and feedback processing may rely on neural oscillations at different frequencies41,42. To determine the direction of information flow between the VTC and MTL during visual object processing, we calculated Granger causality (GC) for each VTC (visually responsive)-MTL (category-selective) channel pair. Consistent with the results from the PLV analysis, we observed significant GC in all pairs of VTC-MTL regions (Fig. 6d; see Supplementary Fig. 8d for analysis using ipsilateral channels only). Bidirectional interactions between the VTC and MTL were observed, suggesting that object coding requires both feedforward and feedback processing. Feedforward communication (VTC to MTL) peaked at a lower frequency, whereas feedback communication (MTL to VTC) peaked at a higher frequency (Fig. 6d), consistent with previous studies42. Importantly, we further showed that axis-coding channels had a stronger feedforward GC from the VTC to the AH in the theta frequency range compared to non-axis-coding channels (Fig. 6e), confirming the feedforward information flow (i.e., providing structured feature information from the VTC to support higher-level processing in the MTL) as shown by the PLV analysis.

Lastly, we investigated whether region coding in the MTL is related to visual processing in the VTC. In 9 patients (13 sessions), we recorded iEEG in the VTC and single neurons (n=189) in the MTL simultaneously, which allowed us to directly test the functional connectivity between MTL region-coding neurons and VTC channels. Specifically, we calculated the pairwise phase coherence (PPC; see Methods) between region-coding neurons (n=35) in the MTL and their corresponding VTC channels (n=48), resulting in a total of 912 pairs. Indeed, we found that MTL region-coding neurons fired spikes that were phase-locked to gamma oscillations in the VTC (see Fig. 7a, b for examples; 12/35 phase-locked to the FG and 22/35 phase-locked to the ITG; for the entire population: 87/189 phase-locked to the FG and 118/189 phase-locked to the ITG; see Supplementary Fig. 10a–d for analysis in the theta frequency range). Similar to the entire population, the majority of MTL region-coding neurons fired close to the flanks of the gamma frequency oscillation (i.e., 90° or 270°; Fig. 7c) in both the FG and ITG.

Notably, AH region-coding neurons exhibited significantly stronger phase-locking to VTC axis-coding channels than to VTC non-axis-coding channels (Fig. 7d), suggesting that the AH was more tuned to input from the axis-coding channels in the VTC. We further demonstrated that this effect was primarily driven by the FG (Supplementary Fig. 10e), which not only aligns well with the VTC-MTL representational similarity profiles (Fig. 3h) but also further supports a hierarchical processing structure within the VTC-MTL pathway. Furthermore, the PPC of the MTL spike–ITG iEEG pairs was significantly greater for in-region stimuli compared to out-region stimuli in the gamma frequency range (Fig. 7e; two-tailed paired t-test across spike–iEEG pairs: t(503)=2.01, P=0.045, d=0.09, 95% CI=[0.0001, 0.01]; a similar trend was observed for the MTL spike–FG iEEG pairs), but not in the theta frequency range (Supplementary Fig. 10d; t(911)=1.73, P=0.083, d=0.06, 95% CI=[−0.0004, 0.0062]), suggesting that objects encoded by MTL region-coding neurons had more synchronized activity, specifically with gamma oscillations in the VTC.

To illustrate the correspondence between region coding and axis coding, we visualized the in-region stimuli of MTL region-coding neurons along the most-preferred axis of their corresponding phase-locked VTC axis-coding channels (Fig. 7f, g). Interestingly, the in-region stimuli tended to cluster at a specific point along the tuning axis of the phase-locked iEEG channels. This finding suggests that MTL region-coding neurons are tuned to visual features that are parametrically encoded by phase-locked VTC feature-coding channels, providing a physiological basis for functional coupling and supporting our proposed computational framework.

Together, our results revealed robust and bidirectional VTC-MTL interactions during object coding. In particular, VTC axis-coding channels exhibited enhanced synchronization with MTL channels, and notably, this synchronization was parametrically modulated by the strength of axis coding, suggesting that VTC axis-coding channels provide processed visual feature information (i.e., neural axes) to the MTL. On the other hand, MTL region-coding neurons displayed phase-locking to gamma oscillations in the VTC, with stronger phase-locking observed for axis-coding channels and encoded objects. These results establish the physiological basis of our computational framework of neural object coding.

Methods
Participants
We recorded intracranial electroencephalography (iEEG) and/or single-neuron activity from neurosurgical patients with pharmacologically intractable epilepsy. All participants provided written informed consent under procedures approved by the Institutional Review Boards of Washington University in St. Louis (WUSTL) and West Virginia University (WVU).

For the ImageNet task, we recorded iEEG from 14 patients (9 females; 21 sessions in total; Supplementary Table 1). In 19 of these sessions, we simultaneously recorded single-neuron activity in the amygdala and hippocampus along with iEEG. In an additional 7 patients (15 sessions), we recorded only single-neuron activity in the amygdala and hippocampus.

For the Microsoft COCO task, we recorded iEEG from 11 patients (7 females; 16 sessions in total). In all sessions, we simultaneously recorded single-neuron activity along with iEEG. In an additional 5 patients (5 sessions), we recorded only single-neuron activity in the amygdala and hippocampus. Notably, 13 iEEG sessions from 9 patients were recorded immediately following the ImageNet task.

Data acquisition and preprocessing
We recorded iEEG signals using clinical depth macro-electrodes (Ad-Tech, PMT, or DIXI), with each electrode containing 4–18 channels, and used a Nihon Kohden recording system. Data were analog-filtered above 0.01 Hz and digitally sampled at 2000 Hz during acquisition. Preprocessing was performed using EEGLAB69. First, the data were visually inspected, and channels contaminated by epileptic or artifactual activity were excluded from further analysis. Second, a 0.5 Hz high-pass filter and a common average reference were applied, followed by a notch filter to remove line noise (60 Hz and its harmonics at 120 Hz and 180 Hz). High-amplitude noise events, as well as interictal discharges, were identified on a trial-by-trial basis using a threshold set according to the procedure described in ref. 70. We then extracted high-gamma power (HGP) by applying a band pass filter (70–170 Hz) and taking the absolute value of the Hilbert transform of the resulting signal. For connectivity analysis between iEEG channels, we used bipolar re-referencing instead of a common average reference to avoid artifacts introduced by the common reference. All other preprocessing procedures remained the same for all analyses. For further analysis, the data were segmented into event-related epochs around stimulus onset (−0.5 to 1.5 seconds relative to stimulus onset), and we extracted the average neural response for each object image from 0.1 to 0.6 seconds after stimulus onset.

We recorded single-neuron signals using microwires embedded in the hybrid depth macro-electrodes (Ad-Tech Behnke-Fried electrodes) implanted in the amygdala and hippocampus. From each microwire, we recorded the broadband extracellular signal (0.1–9000 Hz) with a sampling rate of 32 kHz, and the data were stored continuously for offline analysis using either a Blackrock (WUSTL) or Neuralynx (WVU) system71. For sessions with concurrent recordings, the iEEG and single-neuron signals were time-synchronized using a photodiode patch attached to the stimulus screen. The raw single-neuron data were filtered with a zero-phase lag 300–3000 Hz bandpass filter, and spikes were sorted using a semi-automatic template matching algorithm as previously described72. For each neuron, the mean firing rate within 0.25 to 1.25 seconds after stimulus onset was extracted for each object for further analysis.

Electrode localization
We estimated electrode locations based on pre-operative high-resolution T1-weighted MRI scans and post-operative CT scans. The CT images were co-registered with the MRI scans for patient-specific electrode localization using the VERA software suite (https://github.com/neurotechcenter/VERA). Each channel was labeled with a specific anatomical area derived from Freesurfer’s automatic segmentation73. Electrode locations were then normalized to the MNI space through nonlinear co-registration for visualization.

It is worth noting that the channels in the current study covered the anterior FG and ITG, brain areas that are under-explored due to severe BOLD signal distortion in fMRI studies52,74. Therefore, our results complement neuroimaging studies by revealing the functional organization of the anterior FG, ITG, and MTL areas.

Stimuli
We used two sets of stimuli. For each set of stimuli, we used the same images for all participants. The choice of object categories was guided by the need for a diverse and representative set encompassing a broad range of categorical types and visual properties.

For the ImageNet stimuli31, we selected 50 categories of objects with 10 images for each object category. The object categories included arachnid, battery, bark, beverage, board, bread, brier, building, car, cat, collection, crustacean, dainty, dog, electrical device, electronic device, equipment, fare, fern, fish, flower, frog, fruit, fungus, furniture, game bird, gymnast, herb, hole, insect, light, man clothing, moped, musical instrument, needlework, nest, plate, reptile, ridge, rock, rodent, star, sugar maple, support, tool, utensil, vegetable, vessel, weapon, and young mammal. These categories were selected to sample broadly across multiple conceptual dimensions, including animacy (e.g., animate: dog, frog, rodent; inanimate: car, plate, tool) and naturalness (e.g., natural: tree bark, fruit, rock; artificial: electronic device, musical instrument, clothing). By including both animate and inanimate stimuli as well as natural and man made objects, the stimulus set allowed us to disentangle these two key but often confounded dimensions of object representation (Supplementary Fig. 3). In addition, the categories varied in terms of taxonomic class (e.g., animals, plants, artifacts), functional role (e.g., tools, furniture, clothing), and perceptual properties (e.g., shape complexity, texture, material composition). Importantly, these dimensions align with known object dimensions reported in prior studies18,29, including animacy versus inanimacy, natural versus artificial, and shape-based features such as spikiness versus stubby/rounded forms. This alignment provides a principled basis for interpreting neural responses along perceptually and conceptually meaningful axes (see Supplementary Fig. 3), as established in previous work on object representation in the brain18,29.

For the Microsoft COCO stimuli40, we selected 10 object categories, with 50 images per category. The selected categories were: airplane, apple, bear, bird, car, chair, dog, elephant, person, and zebra. These categories were chosen to form a well-controlled stimulus set that maintained diversity across both animate and inanimate as well as natural and artificial dimensions. Specifically, the set includes animate natural entities (e.g., bear, elephant, person, zebra), inanimate artificial objects (e.g., airplane, chair, car), and inanimate natural objects (e.g., apple). This selection again allowed us to sample from key conceptual and perceptual dimensions known to shape object representation in the brain (see Supplementary Fig. 4)18,29. By incorporating both categorical and perceptual variation, the stimulus set supports analyses of how neural activity reflects structured dimensions of real-world object space.

Together, the diversity in both datasets ensured that our findings were not biased toward a specific subset of object categories and that the computational principles we identified were generalizable across different domains of object recognition. Additionally, our category choices aligned with previous studies investigating object representations in the higher visual cortex, allowing for meaningful comparisons with prior literature18,29.

Experimental procedure
Stimuli were presented on a flat monitor screen in front of the patients, who sat comfortably on the bed in a patient room. We used a one-back task for each set of stimuli. In each trial, a single object image was presented at the center of the screen for a fixed duration of 1 second, with a uniformly jittered inter-stimulus interval (ISI) of 0.5 to 0.75 seconds. Each image subtended a visual angle of approximately 10°. Patients pressed a button if the current image was identical to the immediately previous image, with one-back repetitions occurring in 9% of trials. Each image was shown once unless repeated in one-back trials; and responses from one-back trials were excluded to ensure an equal number of responses for each image. This task kept patients engaged with the images while avoiding potential biases from focusing on specific image features32.

Extraction of visual features
We employed two well-known deep neural networks (DNNs), ResNet-10175 and AlexNet76, to extract visual features for each ImageNet and COCO image. Following the same procedure27,28, we fine-tuned the top layer of each DNN to confirm that the pre-trained models could discriminate between objects and to ensure their suitability as feature extractors. We also used the fine-tuning accuracy to determine the most appropriate model for feature extraction. Additionally, to construct a common feature space using images from both the ImageNet and COCO datasets, we extracted features for 1000 images using AlexNet. It is worth noting that neither feature extraction nor the construction of feature spaces utilized any information from MTL neurons; therefore, the clustering of neurally encoded categories in feature spaces was not by construction.

Visually responsive iEEG channels
We used the mean HGP in the time window from 0.1 to 0.6 seconds after stimulus onset as the neural response to each stimulus. A channel was considered visually responsive if it showed a significantly different response compared to the baseline (0.5 to 0.02 seconds before stimulus onset).

Category-selective coding
We selected iEEG channels that demonstrated category-selective coding among those responsive to at least one category. Specifically, we first used a one-way ANOVA to identify channels with significantly different responses to object categories. Next, we applied an additional criterion to identify the selected categories: the neural response to a category had to be 1.5 standard deviations (SD) above the mean of neural response from all categories during baseline. These object categories, whose responses stood out from the global mean, were considered the encoded categories. We refer to channels encoding a single object category as single-category (SC) channels and those encoding multiple categories as multiple-category (MC) channels. Our previous study demonstrated that this procedure effectively identifies category-selective coding27,28.

Axis-based feature coding
To identify iEEG channels that demonstrated axis-based feature coding (i.e., encoding a linear combination of visual features), we employed partial least squares (PLS) regression using DNN feature maps. Axis-coding channels were selected from among the visually responsive channels, as this method assumes the encoding of visual features. The PLS method has been shown to effectively study neural responses to DNN features17,30. We used 4 components for each layer, which explained at least 80% of the variance and were determined through 10-fold cross-validation to minimize prediction error. The most preferred tuning axis for each axis-coding channel (i.e., the first PLS component) was obtained using all images.

To determine statistical significance, we used a permutation test with 1000 runs to assess whether a channel encoded a significant axis-coding model. In each run, we randomly shuffled the object labels and used 50% of the objects as the training dataset. We constructed a model by deriving regression coefficients using the training dataset, predicted responses for each object in the remaining 50% of objects (i.e., test dataset), and computed the Pearson correlation between the predicted and actual responses in the test dataset. The distribution of correlation coefficients computed from the shuffled data (i.e., the null distribution) was then compared to the correlation from the unshuffled data (i.e., the observed response). If the correlation coefficient of the observed response was greater than 95% of those from the null distribution, the axis-coding model was considered significant. This procedure has been shown to be highly effective in selecting neurons with significant axis-coding models15. The correlation coefficient also indicated the model’s predictability, which is the strength of axis coding, and could thus be compared across different channels.

To cross-validate axis-coding models across datasets, we extracted visual features using the same DNN (i.e., AlexNet) for both ImageNet and COCO images. For each channel, we trained a PLS regression model using all images from the ImageNet dataset and used the obtained regression coefficients to predict neural responses to the COCO images. We then computed the Pearson correlation between the predicted and actual responses to the COCO images and used the permutation procedure (1000 runs) described above to assess statistical significance and strength of axis coding. The most preferred tuning axis for each axis-coding channel (i.e., the first PLS component) was obtained using images from the combined ImageNet and COCO datasets (n=1000 images).

To determine the temporal characteristics of axis-based feature coding, we fitted the PLS model to the time series of axis-coding channels. For computational efficiency, the raw time series were first binned using a sliding window of 100 ms with a 20 ms overlap. We then computed the strength of axis coding by fitting the PLS model at each time point using a 10-fold cross-validation procedure. This process was repeated twice, and the strength of axis coding was averaged across repetitions for each time point. The resulting time course of axis coding was then z-scored relative to the pre-trial baseline (−0.5 to −0.02 seconds) for each channel. The significance of axis coding at each time point was compared to the mean baseline performance using paired t-tests across channels (Fig. 3f). We retained only consecutive significant data points lasting longer than 5 bins (100 ms).

Region-based feature coding in the VTC neural feature space
We first constructed a neural feature space using axis-coding channels from the VTC. For each axis-coding channel, we extracted its most preferred tuning axis (derived from the PLS regression; see above) and pooled these axes across channels from a brain area. Next, we applied PCA on these axes to obtain a two-dimensional neural feature space, which represented the primary visual information encoded in that brain area. In Fig. 4, we included all axis-coding channels from the FG, as axis coding was most prominent there (Fig. 3c–e). However, we obtained similar results when including axis-coding channels from both the FG and ITG.

To select MTL neurons demonstrating region-based feature coding in the VTC neural feature space, we first estimated a continuous spike density map in the neural feature space by smoothing the discrete neural response map with a 2D Gaussian kernel. The kernel size was proportional to the number of clusters (i.e., images from the same category were grouped together) within each feature space, the feature space dimension, and an empirical scaling factor (sq) estimated for each feature space (ImageNet: sq = 0.021; COCO: sq = 0.05). We then assessed statistical significance for each pixel using permutation testing: in each of 1000 runs, we randomly shuffled the object labels. The p-value for each pixel was calculated by comparing the observed spike density to the null distribution derived from the permutations. A mask was applied to exclude edge and corner pixels of the spike density map where no objects were present, as these regions were prone to false positives. We selected the regions containing significant pixels (permutation P<0.01, cluster size >2.5% of pixels within the mask). If an MTL neuron contained a region with significant pixels, it was considered to demonstrate “region-based feature coding” in the VTC neural feature space. Our previous studies showed that this procedure is effective in identifying neurons with region-based feature coding27,28.

It is worth noting that if the encoded objects are not distributed homogeneously (or uniformly) in the neural feature space, a systematic pattern (e.g., a peak) may appear in the permuted distribution. However, we have shown that our procedure reliably identifies coding regions even under heterogeneous distributions27.

Depth of selectivity (DOS) index
To summarize the response of category-selective channels, we quantified the depth of selectivity (DOS) for each channel: DOS = (n/(n-1)) * (1 - (sum(rj)/rmax)), where n is the number of categories (n=50), rj is the mean response to category j, and rmax is the maximal mean response across all categories. DOS varies from 0 to 1, with 0 indicating an equal response to all categories and 1 exclusive response to one category, but not to any of the other categories. Thus, a DOS value of 1 is equal to maximal sparseness of category coding. The DOS index has been used in many prior studies investigating visual selectivity77–79.

Response latency
We estimated the onset latency of task-induced HGP using a trial-by-trial method established in previous studies80,81. To avoid potential confounds from temporal smoothing, we conducted the analysis on unsmoothed and non-downsampled data. The signal was first binned using a sliding window of 30 ms (60 sample points) with a 4-ms step size. For each trial, responsive data points were identified by thresholding the binned data at the average pre-trial baseline (−0.3 to −0.02 seconds) across all trials plus one standard deviation. We retained only consecutive responsive data points lasting longer than 25 bins (100 ms). The response onset latency was defined as the first responsive data point in each trial, and we estimated the median latency across trials for each channel. Trials that failed to detect the latency (i.e., did not meet the 25 consecutive responsive bins criterion) were excluded. Only channels with more than 50 trials that could obtain onset latency were included in the final analysis.

We used a similar procedure to compute the latency for category-selective coding. For each category-coding channel, we performed a one-way ANOVA across 50 categories on the binned data and identified significant time points based on the p-value (P<0.05). We retained only consecutive significant data points that lasted longer than 25 bins (100 ms), and the first significant data point was defined as the category-selective onset latency for the given channel.

Similarly, the latency for axis coding was estimated using the p-value from the t-test, with a threshold of 7 consecutive bins (140 ms) set for each axis-coding channel. To test whether the latency of axis coding is related to anatomical location, we normalized the latency of each channel to the best-performing channel within each session. This procedure effectively controlled for individual differences in response latency across participants81. The latency was then correlated with the y-coordinate values in the MNI space across channels. Note that this analysis was restricted to the FG, as other significant axis-coding ROIs had very few channels (ITG: n=18; AH: n=4) with detectable onset latency.

We acknowledge that the estimation of latency is subject to imprecisions introduced by temporal smoothing related to the binned HGP signal. Therefore, the absolute latency value should be interpreted with caution, as different methods may lead to heterogeneous values43.

Representational similarity analysis (RSA)
We employed RSA32,82,83 to compare the neural representations in the VTC and MTL areas. For a given ROI, we constructed an image-by-image dissimilarity matrix (DM) using the pairwise Euclidean distances between object images based on the responses of the selected channels. Specifically, for each pair of images, we calculated the Euclidean distance of the neural responses to these two images across all axis-coding or category-coding channels in the ROI. The representational similarity between two ROIs was then computed by correlating the two DMs (e.g., the FG axis-coding DM and the AH category-coding DM) using Spearman correlation, which does not assume a linear relationship. We employed a Mantel test84 to determine the statistical significance of the RSA. Specifically, we estimated a null distribution by permuting the rows and columns of one DM prior to correlation, repeating the procedure 1000 times.

Time-frequency analysis
We performed a time-frequency analysis to estimate the mean event-related power spectrum (1–250 Hz) across trials within the time window from −1 to 2 seconds relative to stimulus onset. Specifically, we applied a Morlet wavelet transform, using 2 cycles for the lowest frequency, with the subsequent frequency increasing linearly by 0.5 cycles/Hz. The estimated event-related power was then normalized to the pre-trial baseline (−0.5 to 0 seconds relative to stimulus onset; Fig. 6a).

Inter-areal phase synchrony
We quantified the strength of inter-areal neural synchrony with the phase-locking value (PLV)85, which measures the degree of consistency in phase between two channels, independent of their absolute phases and amplitudes, with values ranging from 0 to 1. The PLV for each channel pair was calculated as: PLV = |(1/N) * sum(exp(i*(θa - θb)))|, where we first computed the phase (θ) differences between channels a and b and then averaged them across trials for a given frequency. We calculated the PLV across frequencies (2–250 Hz) for VTC-MTL channel pairs, with each pair containing one visually responsive channel in the VTC and one category-selective channel in the MTL. For each trial, we extracted the mean phase within 0 to 1.5 seconds after stimulus onset. For each session, we selected channel pairs that included the channel with the best axis-coding performance in the FG and ITG, and computed the time-frequency-resolved PLV using a sliding window of 100 ms, stepped by 25 ms.

We conducted a permutation test to determine the statistical significance of the PLV by estimating a null distribution through random shuffling of the trial labels for each channel pair and computing the corresponding PLV. This procedure was repeated 1000 times for each pair, and the observed PLV that exceeded the 95th percentile of the surrogate data was considered significant. We compared the observed mean PLV with the mean null distribution of all channel pairs for group statistics. For the time-resolved PLV, we applied an additional cluster-based criterion, retaining only clusters consisting of continuous significant pixels with a cluster size larger than 2.5% of the total number of pixels. The time-resolved PLV results were smoothed using a cubic spline interpolation method (spline.m function in MATLAB) for visualization purposes.

Granger causality analysis
To test the directionality of the interaction between the VTC and MTL, we calculated spectral Granger causality, which quantifies the prediction error of the signal in the frequency domain by introducing another time series. The time series of each channel was first low-pass filtered at 85 Hz and downsampled to 250 Hz. We then normalized the signal within each trial to eliminate amplitude differences and improve signal stationarity. Before fitting the normalized signal to the multivariate autoregressive model, we determined the model order for each session using the Multivariate Granger Causality (MVGC) Toolbox86, based on the Akaike information criterion (AIC). The Granger causality index was computed within 0 to 1.5 seconds after stimulus onset for both directions (VTC to MTL, MTL to VTC). We applied the same permutation procedure to determine statistical significance as in the PLV analysis.

Spike–field pairwise phase consistency (PPC) between the MTL and VTC
We quantified the spike–field pairwise phase consistency (PPC) using established methods87–89. The PPC measures the average cosine similarity (i.e., in-phaseness) of any pair of spikes from the same neuron in the iEEG phase domain, reflecting the degree of synchronization between the firing of individual MTL neurons and the phase of oscillatory components in VTC channels. For every frequency f, we determined the iEEG phase at the time of each spike by computing the Fourier spectrum of the iEEG around the spike using the complete iEEG trace from 0.25 to 1.25 seconds after stimulus onset with a multi-taper method. Specifically, we used discrete prolate spheroidal sequences [DPSS] tapers, which are a set of Slepian tapers, with a taper number of 5 and a taper length corresponding to the number of sample points for 3 cycles per frequency (e.g., the sequence length and time-half-bandwidth product of tapers for frequency 20, 22, 24, 26 and 100 are: 151/3.02, 137/3.014, 125/3, 115/2.99, and 31/2.48, respectively). We focused on the gamma frequency band, as gamma phase-locking is suggested to reflect the activation of local networks for encoding specific identities90. The phase similarity was quantified using the PPC, calculating the average phase difference across all possible spike pairs within a time window from 0.25 to 1.25 seconds after stimulus onset. We calculated the PPC for each region-coding neuron in the MTL with all possible simultaneously recorded VTC channels. While the PPC corrects for sample-size bias, its estimates can be highly variable for units with a low spike count87. Therefore, we included only neurons with at least 20 spikes after stimulus onset to reduce variance in the group average87.

For each neuron-channel pair, the PPC was estimated separately for in-region and out-region stimuli. To balance the spike count across conditions (as the number of in-region trials was less than out-region trials), we randomly subsampled the same number of spikes from out-region trials for each neuron–channel pair. The results remained consistent when we used bootstrapping and subsampled the out-region trials 100 times. To test statistical significance, we compared the mean PPC between the two conditions within our frequency range of interest (30–80 Hz) across all neuron–channel pairs using a paired t-test.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The IEEG data and single cell results from human temporal cortex reported by Cao et al are invaluable. In general such data are very much in demand as it becomes increasingly clear that fMRI data suffer from a very serious temporal resolution problem. More specifically, the authors report axis tuning in human FG, IT gyri and MTL extending results from non-human primates, as well as a very neat tuning of MTL single neurons in the feature space defined by the feature axes observed in the iEEG temporal leads. Both are very nice findings.\nYet the manuscript in its present from includes several severe flaws\n1 the basic model outlined in fig 1 is not proven: the authors insist that area in feature space coding can only detected with single cell recording, not iEEG. As there are no single cell recordings in FG nor IT gyri, we do not know whether such tuning does not already exist in FG or IT cortex. Thus it might be that the area in feature space is constructed in IT cortex and then simply transmitted to MTL ( and perhaps other regions such as the temporal pole; (TP); this is the more a problem that at iEEG level little difference between TE and MTL was reported, if anything leads were more responsive and selective in TE;\nOther shortcomings are:\n6 methods used to derive links between areas are relatively unspecific and global, although effort were made to show they are linked to axis/ category selectivity of leads or preferred categories of neurons. Phase locking between leads not very specific (see ter Wal et al Neuroimage 2018); the much superior Granger connectivity was calculated on filtered data ( <85HZ) and was restricted to lower frequency bands. Using the broad gamma band (50-150Hz) which reflect neuronal activity, Tiesinga et al 2023 obtained very specific links between particular leads in TE and TP. A more specific pattern of connection may be observed between FG and MTL with this method.\n7 the choice of object categories is completely arbitrary in the main and control studies.\nOther weaknesses:\n8 in the result section on MTL single neurons there is too much reasoning on single cell coding ( as in early H & W papers). Now we know that what matters is a population of tuned neurons which can retrieve the identity of object category;\n9 overstatements in text: eg in results section it is claimed that the iEEG data delineate responsive temporal cortex, but this requires that also non responsive areas are reported; in the discussion a topographic organization is mentioned, but it is unclear to which result this relates.\n10 were any differences in latencies between subjects observed, as was reported by Tiesinga et al 2023?\nGuy Orban", "reviewer_2": "Here, Cao take advantage of a unique data set capturing intracranial EEG (iEEG) from the ventral temporal cortex and single unit recordings from the medial temporal lobe as participants perform a simple one-back task in which they are viewing hundreds of images drawn from different categories. The authors build on prior work in NHPs demonstrating that activity in the VTC appears to be organized along a set of coding axes, where each axis represents the projection along a weighted combination of object features. They then compare this coding to the coding of individual neurons in the MTL, and find that the neuronal spiking responses in the MTL are organized within regions of this VTC coding space. This is a highly interesting and relevant study, as it begins to address the important question of how the human brain transforms information from densely distributed coding in the temporal lobe ventral cortex to sparse spiking activity in the medial temporal lobe. The analyses are well constructed, and the data are clearly presented. There are several suggestions, however, that might help strengthen the overall claims of the study.\nOne of the main claims of the manuscript is that feature coding occurs in the VTC whereas region coding within that feature space occurs in the MTL. In particular, they examine how individual neurons respond to the features defined by a PCA of the VTC feature axes. However, in one of the earlier results, the authors show that indeed MTL neuronal activity (specifically the hippocampus, Fig 3e) also encode the object feature space. As such, it would be helpful to understand how the region coding within the feature space differs from the direct feature coding observed in the anterior hippocampus. What is the benefit of performing this extra manipulation.\nWhen examining region coding, the authors use the feature space defined only by the fusiform gyrus. Yet there are other regions of the VTC that also capture this feature space, as shown by their data. Why restrict the region coding to only the FG feature space? Moreover, in subsequent analyses examining phase locking and Granger causality, rather than just focus on the FG, they then expand their analyses to examine the entire VTC. Why would the difference between the two analyses.\nThe authors present an analysis in which they compare feature based coding in the VTC to category based coding in the MTL using representational similarity analysis. It would be helpful if they could clarify exactly how this analysis was conducted and what was compared. Reading through the methods did not clarify this point, but this seems like it would be a nice segue into subsequent analyses on region coding.\nThe authors demonstrate significant phase locking in lower frequency bands between the VTC and MTL. But the analysis if Granger causality demonstrates connectivity at much higher frequencies. Could the authors provide some insight as to why the discrepancy? Moreover, when examining the locking of individual neurons to gamma phase, the authors find a subset that indeed exhibit significant phase locking. Here the reported numbers, it appears, are n=9, and n=14. Are these meaningful?\nOn a related note, it would probably be helpful if the authors were a little cautious in the interpretation of the connectivity results. Specifically, they claim that their analyses demonstrate that VTC coding channels provide more relevant information to the hippocampus during coding. Without an explicit measure of information and a clear analyses showing how coding is tied to this connectivity, the authors may want to consider tempering this claim.", "reviewer_3": "Summary of the Study\nThis study investigates the neural mechanisms underlying object representation in the human ventral temporal cortex (VTC) and medial temporal lobe (MTL) using intracranial EEG (iEEG) and single-unit recordings. The authors propose a computational framework in which the VTC encodes visual features via axis-based feature coding, which is then transformed into region-based feature coding in the MTL, supporting semantic representation. Through functional connectivity analyses, they demonstrate bidirectional interactions between these regions and provide evidence that axis- coding in the VTC is linked to semantic coding in the MTL. This work builds on prior findings in non-human primates and extends them to human recordings, offering novel insights into the transition from perceptual to semantic representations in the brain. However, several aspects of the analysis require further clarification, particularly regarding alternative coding schemes, the interpretation of functional connectivity, and the temporal dynamics of processing. Additionally, refining the presentation of figures and ensuring consistency in methodological justifications would further strengthen the paper.\nMajor Strengths\n• The study addresses an important question in visual neuroscience: how object representations transition from perceptual encoding in the VTC to conceptual encoding in the MTL.\n• The combination of iEEG and single-unit recordings provides a unique and powerful dataset, allowing for a detailed analysis of neural coding at multiple scales.\n• The use of partial least squares (PLS) regression with deep neural network (DNN) feature representations is methodologically rigorous and aligns with recent advances in computational neuroscience.\n• Functional connectivity analyses using phase-locking value (PLV) and Granger causality (GC) strengthen the argument for dynamic interactions between the VTC and MTL.\n• The validation with an additional dataset (COCO) enhances the generalizability of the findings.\nMajor Points for Revision\n1. Evidence for axis coding in the VTC\n• The significance of PLS regression alone is insufficient to establish axis coding as the dominant encoding strategy in the VTC. While the regression’s significance partially controls for exemplar-based coding, the relatively low R² values suggest that axis coding may not be the most explanatory model.\n• The authors should directly compare axis coding with other encoding strategies, particularly category-based encoding, which is mentioned at the beginning of the manuscript. It is essential to demonstrate that axis coding explains the neural responses in the VTC better than alternative models.\n• One approach would be to fit a regression model that includes both axis coding and category-based coding variables and compare their contributions. Alternatively, a correlation-based analysis or Representational Similarity Analysis (RSA) could be used to construct dissimilarity matrices based on both axis coding and category-based coding. The authors should examine whether the dissimilarity matrix derived from axis coding better aligns with the neural responses in the VTC.\n• Additionally, the demonstration of cross-dataset generalizability and consistency of the axes is a major strength of the study. However, the investigation of object space remains insufficient. Identifying the nature of the coding axes is crucial to further validate the axis-based feature coding in the VTC. Specifically, more detailed characterization of the extracted axes (e.g., how they align with known dimensions such as animate vs. inanimate) would strengthen the argument for axis-based encoding.\n2. Region-based feature coding in the MTL relative to alternative coding schemes\n• The finding that MTL receptive fields exhibit region-based feature coding within the VTC feature space is highly interesting. However, it is necessary to evaluate whether this coding scheme provides a better explanation of the data compared to alternative models.\n• Specifically, the authors should compare region-based coding with category-based coding and localized axis coding to determine whether region-based feature coding is the most informative representation.\n• A direct comparison could involve fitting models that incorporate different coding schemes and assessing their relative explanatory power. Alternatively, the authors could perform RSA using dissimilarity matrices based on these different coding schemes to test which model best aligns with the neural responses in the MTL.\n• Clarifying this distinction would strengthen the argument that region-based coding is the preferred computational strategy for transforming VTC feature space into MTL representations.\n3. Relationship between face-selective channels and axis coding in the VTC\n• Previous ECoG studies (e.g., McCarthy et al. Cereb Cortex (1999) , Matsuo et al, Cereb Cortex (2015)) have demonstrated that face-selective channels are organized in patches within the VTC. Given this prior work, it is crucial to examine the relationship between face-selective channels and axis-coding channels in the current study.\n• The authors should explicitly test whether face-selective channels exhibit axis coding or whether they represent a distinct form of category-selective coding. This is particularly relevant for the fusiform face area (FFA), where category selectivity has been well-documented.\n• A key question is whether face selectivity in the human FFA, as defined in this study, corresponds to axis coding as seen in non-human primate (NHP) face patches.\n• If face-selective channels do align with axis-coding channels, this should be explicitly demonstrated with supporting analyses. Conversely, if they do not align, this should be discussed in terms of differences between category-selective and axis-based feature coding.\n• A possible approach would be to compare the locations of face-selective channels with axis-coding channels and test whether the neural responses in face-selective regions are better explained by axis coding or category-based models.\n4. Relationship between regional coding and semantic coding\n• The relationship between regional coding and semantic coding is not entirely clear. While the study suggests that regional coding in the MTL represents a transition from perceptual to semantic representations, regional coding could, in principle, occur at lower levels of the visual hierarchy as well. For example, in early visual processing, simple cells combine to form complex cells, which also exhibit localized feature selectivity.\n• The definition of regional coding in this study is based on a high-dimensional feature space derived from DNNs, which emphasizes complex feature representations. However, this does not necessarily imply a direct link to semantic representations.\n• The authors should clarify how regional coding in the MTL differs from lower-level feature encoding and why it should be considered a semantic transformation rather than an extension of perceptual processing.\n• One possible approach would be to compare regional coding in the MTL with a measure of semantic similarity (e.g., word embeddings or conceptual clustering) to determine whether it aligns better with abstract category representations rather than purely visual feature spaces.\n• Additionally, the theoretical framework for how regional coding bridges perceptual and semantic representations should be more explicitly described to strengthen its validity.\nMinor Points\n1. Fig. 1: If single-neuron recordings were not performed in the VTC, the plotted circles should not be depicted in a way that suggests they represent firing rates. The figure should be adjusted to accurately reflect the type of data collected (e.g., high-gamma power from iEEG rather than spike rates).\n2. Fig. 2b: The color scheme for the fusiform gyrus (FG) and inferior temporal gyrus (IT) appears to be reversed.\n3. Fig. 2e, f: The latency calculation may be influenced by response amplitude, as larger responses often lead to shorter latencies. This pattern appears to be present in the current data. If hierarchical processing is a key discussion point, it would be beneficial to verify whether the observed latency differences are genuinely reflective of processing order rather than differences in response magnitude. One way to address this is to normalize response amplitudes across regions (e.g., through subsampling or amplitude-matched comparisons) and then re-evaluate the latency differences. Alternatively, latency can be measured using different thresholds, such as relative change rate or peak time, to verify whether the results remain consistent.\n4. Fig. 3d: A thin boundary line or shading, along with a corresponding label in the figure legend, would make the figure more informative.\n5. Fig. 3f: The delayed onset of axis coding in the anterior hippocampus (AH) is an interesting finding. However, it raises the question of whether this signal reflects true axis coding or whether it is confounded by region coding. Since region coding inherently involves encoding localized receptive fields in the VTC feature space, it may manifest with similar temporal characteristics as late-onset axis coding. The authors should discuss whether the observed AH activity aligns more closely with the characteristics of axis coding or region coding.\n6. Fig. 3g: The results in Fig. 3(g) could be influenced by response amplitude, potentially making it harder to distinguish whether the observed effects are due to differences in coding strength or simply variations in signal magnitude. To improve clarity, the authors could normalize response amplitudes across regions or examine whether the patterns remain consistent when analyzing only channels with similar response magnitudes.\n7. Fig. 3h: If possible, the original representational dissimilarity matrices used to compute the correlation values in Fig. 3h should be shown, either in the main figure or as a supplementary figure. This would help readers better understand the structure of the neural representations being compared and how axis coding in the VTC relates to category coding in the MTL. Additionally, even in statistically significant comparisons, the absolute correlation values appear to be quite low. The authors should discuss what this implies about the strength of the relationship between these representational spaces. Possible interpretations include: Neural representations in the VTC and MTL are related but remain largely distinct. The transformation from axis coding to category coding introduces substantial variability, resulting in lower similarity. Other factors, such as noise in the data or methodological limitations, may contribute to the low correlation. If the authors can provide additional insights into why these correlation values are low despite statistical significance, it would improve the clarity of the findings.\n8. Fig. 4a: The current visualization is difficult to interpret due to overlapping stimuli. To improve readability, the authors should adjust the presentation to reduce visual clutter. One effective approach would be to highlight examples of stimuli located near category boundaries. This would help illustrate that while the visual features are continuous, a clear semantic boundary exists in the representational space. It is important to ensure a clear and representative example of such a case.\n9. Fig. 4b right lower: In permuted data, systematic patterns should typically disappear. However, a response peak is still observed here. This suggests that some structural bias (e.g., imbalance in sample size, spatial non-uniformity) may be influencing the results. Do you have any insights or considerations regarding this observation?\n10. Fig. 4e and f: The interpretation of these results is unclear. A direct comparison with VTC data would likely provide valuable context, but given that single-unit data from the VTC is not available, this may not be possible. The authors should clarify how these results support their conclusions about region-based coding in the MTL. If the authors acknowledge that the lack of direct VTC comparison limits the interpretability of these results, they should discuss this limitation in the discussion section. Additionally, explaining why these results are important for understanding the broader hypothesis of the study (e.g., how they support the concept of region-based coding in the MTL) would help readers grasp the significance of these findings.\n11. Fig. 4 and related text: The statement, \"fewer region-coding channels were detected than expected by chance, indicating that region coding did not occur at the mesoscopic level\", would be more informative if the exact percentage of detected region-coding channels was provided. This result raises an important methodological concern: does the absence of region coding in iEEG recordings simply reflect differences in recording resolution rather than a true absence of region coding at the mesoscopic level? Given that single-unit recordings in the MTL detected region coding, the authors should discuss whether this discrepancy arises from the nature of iEEG signals, which pool activity over larger populations of neurons, potentially masking fine-scale region-based tuning. If region coding does not manifest at the iEEG level in the MTL, this raises the question of whether iEEG recordings in the VTC might also fail to detect certain types of coding. Could this methodological limitation affect the comparisons between VTC and MTL? A more detailed discussion may be necessary to clarify these points. Providing additional context or explanations could help improve the interpretability of the results and address potential concerns.\n12. Fig. 5g: The results should be presented using the same scale as Fig. 4g to ensure consistency in data visualization. Aligning the scales (ranges) would make it easier for readers to directly compare the validation dataset results (Fig. 5g) with the original dataset results (Fig. 4g).\n13. Fig. 6a: The PLV results in Fig. 6a provide important insights into the functional connectivity between the VTC and different MTL subregions. Given these findings, it is essential to carefully examine how they correspond to the representational similarity results shown in Fig. 3h. The fact that FG–AH shows coupling in the high-frequency range is particularly intriguing. This pattern suggests a unique role for AH in processing visual information received from the FG. The authors should explicitly analyze whether the strength of PLV in specific frequency bands correlates with the representational similarity results from Fig. 3h. If stronger functional connectivity (PLV) aligns with greater representational similarity, this would provide additional support for the proposed computational framework. If stronger functional connectivity (PLV) aligns with greater representational similarity, this would provide additional support for the proposed computational framework.\n14. Fig. 6b–c: To better understand the role of different MTL subregions, it would be beneficial to analyze Fig. 6(b–c) by separating the results for each subregion (e.g., anterior hippocampus (AH), posterior hippocampus (PH), and amygdala). If the sample sizes are sufficient for certain subregions, those should at least be analyzed separately to provide a more fine-grained view of the connectivity differences. This is particularly important in relation to Fig. 3h, where different subregions may have shown different representational similarity with the VTC. If the PLV results (Fig. 6) and representational similarity results (Fig. 3h) align in a region-specific manner, this would provide additional support for the proposed coding transition between the VTC and MTL.\n15. The finding that FG and IT exhibit stronger feedforward GC to AH in the <10Hz range is particularly interesting, as it suggests a low-frequency mechanism for information flow from VTC to the MTL. However, this effect does not appear as prominently in Fig. 6f. This discrepancy could be due to differences in the way the analysis is conducted (e.g., spectral GC in Fig. 6d vs. time-resolved GC in Fig. 6f), or subregion-specific variations within the MTL-perhaps only AH exhibits this effect, while PH or the amygdala do not, or the effect being too weak when examined in the time domain rather than the frequency domain. To clarify this, the authors should analyze GC separately for each MTL subregion if sample sizes permit. If AH uniquely exhibits this low-frequency feedforward GC, this should be explicitly discussed, as it would suggest a distinct role for AH in integrating visual information from the VTC. Additionally, the authors should consider how these connectivity results relate to Fig. 3h, which examined representational similarity. If stronger connectivity at <10Hz corresponds to higher representational similarity, this would provide additional support for the proposed functional link between the regions.\n16. Fig. 7a, b: The authors focus on gamma oscillations (30–80 Hz) for spike-field coupling analysis, which is well-justified in the context of visual processing. However, lower frequency oscillations, particularly in the theta range (3–8 Hz), are also critically involved in memory and semantic processing. Given that Fig. 6 identified strong low-frequency coupling (especially in the <10 Hz range), it is important to investigate whether spike-field phase locking occurs at these frequencies as well. In the lower right plot of Fig. 7b, there appears to be some phase locking in low-frequency bands. This suggests that meaningful coupling may also be present outside the gamma range. The authors should expand their analysis to include theta-band phase coupling and compare its strength to gamma coupling. If strong theta-band locking is also observed, this could suggest a multi-frequency communication mechanism between the VTC and MTL, with gamma frequencies supporting feature encoding and theta frequencies supporting integration into memory/semantic networks. If theta coupling is weak or absent, this would reinforce the idea that high-frequency oscillations dominate in this particular task context. In either case, a brief discussion on why gamma was chosen and how the findings align (or contrast) with known roles of theta oscillations in MTL processing would be beneficial.\n17. Fig. 7(d): The observed spike-field coupling appears primarily after 400ms post-stimulus, which is quite late for a purely visual response. This raises an important question: how does this timing align with the broader interpretation of axis coding and VTC-MTL interactions? The main analysis window (0.1–0.6s) used elsewhere in the study does not fully capture this late effect, suggesting a potential misalignment between different analyses. One possible explanation is that the coupling observed in Fig. 7d reflects a later stage of processing related to higher-level integration (e.g., memory retrieval or semantic association) rather than initial visual encoding. Interestingly, the timing of this effect aligns well with the late-onset axis coding observed in the AH in Fig. 3f. This suggests that the coupling in Fig. 7d could be closely related to the delayed axis coding observed in the AH. The authors should explicitly discuss whether these findings indicate a multi-stage processing hierarchy, where early responses (0.1–0.4s) are dominated by direct visual feature encoding and later responses (~400ms onward) reflect semantic or associative processing. If possible, an analysis directly comparing the time course of axis coding strength in the AH (Fig. 3f) with the timing of phase locking in Fig. 7d would help clarify this relationship. If this delayed timing is a key feature of MTL processing, it would be useful to examine whether similar late-onset effects appear in other aspects of the data, such as representational similarity or functional connectivity. Ultimately, a clearer discussion of why the observed coupling occurs so late and how it fits into the broader computational framework would improve the interpretation of these results\n18. Fig. 7 additional: if possible, the authors should separate the VTC into IT and FG when analyzing phase coupling, as this would allow for better alignment with the findings from Fig. 3(h) on representational similarity. Given the functional differences between FG and IT, examining them separately might reveal distinct connectivity patterns with the MTL. If IT and FG exhibit different coupling characteristics with the MTL, this would provide additional support for a hierarchical processing structure within the VTC-MTL pathway. Visualizing the stimuli that drive these responses could further strengthen the paper's conclusions. Since this study includes both iEEG and single-neuron recordings, this is highly valuable data, showcasing example stimuli that selectively engage region-coding neurons in the MTL and their corresponding VTC channels would enhance clarity. if the authors can illustrate how specific visual features or object categories are represented in both regions, this would make the interpretation of functional connectivity results more intuitive. In any case, since this is highly valuable data, illustrating the visual responses of the circuits identified from the responses could help better convey the significance of the statement, \"Lastly, we investigated whether region coding in the MTL is related to visual processing in the VTC. In 9 patients (13 sessions), we recorded iEEG in the VTC and single neurons in the MTL simultaneously, which allowed us to directly test the functional connectivity between MTL region-coding neurons and VTC channels.\"", "reviewer_4": "In the present study, Cao et al. conducted intracranial EEG (iEEG) recordings together with single-neuronal recordings in the ventral temporal cortex (VTC) and the medial temporal lobe (MTL) in epileptic patients observing large number of visual objects. The authors analyzed neuronal responses to the presentation of visual objects using DNN-based visual feature extraction, as well as principal component analysis, and found that the VTC exhibits axis-based feature encoding, and that the MTL neurons encode receptive fields within the VTC neural feature space. They also conducted functional connectivity analysis with Granger causality and spike-LFP coherence analysis revealing bidirectional interaction between the VTC and the MTL.\nThe authors have accomplished a lot of work, the experiments and analysis have been performed properly, and the results are reliable and important for understanding the neuronal computational mechanisms underlying object recognition in humans. However, more comprehensive description and clarification of conceptual questions are needed before the paper can be accepted for publication.\nIntroduction does not clearly explain the computational framework of the study, which prevents it from conveying the importance of the study. The second paragraph states the key questions and hypotheses of the study. However, they are very difficult to understand, especially for the general reader outside the field of cognitive neuroscience. In particular, it will be difficult to understand the meaning and relationships among terms such as axis-based coding, neural axis, region-based coding, sparse coding, dense coding, receptive field, and neural feature space. The third paragraph and Fig. 1 are also difficult to understand and confusing. It is difficult to understand how the neural feature space is constructed from axis coding in the VTC. Also, it is difficult to understand the relationships between the feature dimension (Fig. 1, eft panel) and the neural axes (Fig. 1, right panel). This is because Introduction and Fig.1 omitted that principal component analysis was used to construct the neuronal feature space from the VTC neural axes. This process is not explained until the Results and Methods section, which can lead to misunderstanding and confusion for the readers. A sentence in Fig. 1 legend, “MTL neurons receive processed visual input from the VTC and encode a receptive field (i.e., encoding region) within the neural feature space…”. Readers may have difficulty understanding what is a receptive field within the neural feature space because the term receptive field usually means a receptive field in the visual field. Overall, the authors should expand and rewrite Introduction, as well as improve Fig.1 and its legends.\nIn Abstract, the authors stated: “The VTC exhibited axis-based feature coding, and by constructing a neural feature space using VTC neural axes, we observed that MTL neurons encode a receptive field within the VTC neural feature space. This computational framework explains how dense feature-based representations in the VTC are translated into sparse semantic-based representations in the MTL.” These are the central finding and implication of the study. However, since the neural feature space is composed of features based on the ‘visual’ axis, the finding that MTL neurons encode receptive fields in the neural feature space alone is unlikely to explain how representations are converted to representations based on ‘semantics’. The authors should clarify the logic supporting the claim that this computational framework explains how feature-based representations in the VTC are translated into semantic-based representations in the MTL.\nResults\nIn Figure 3c, the PH did not show an above-chance rate of axial feature coding, while the AH showed an above-chance rate of axial feature coding bilaterally. This may suggest the presence of a posterior-anterior gradient of axis-based feature coding in the hippocampus. Do the authors have any thoughts on this finding?"}, "author_response": "Reply to comments from Reviewer 1\nThe IEEG data and single cell results from human temporal cortex reported by Cao et al are invaluable.\nIn general such data are very much in demand as it becomes increasingly clear that fMRI data suffer\nfrom a very serious temporal resolution problem. More specifically, the authors report axis tuning in\nhuman FG, IT gyri and MTL extending results from non-human primates, as well as a very neat tuning\nof MTL single neurons in the feature space defined by the feature axes observed in the iEEG temporal\nleads. Both are very nice findings.\nYet the manuscript in its present from includes several severe flaws\n1 the basic model outlined in fig 1 is not proven: the authors insist that area in feature space coding can\nonly detected with single cell recording, not iEEG. As there are no single cell recordings in FG nor IT\ngyri, we do not know whether such tuning does not already exist in FG or IT cortex. Thus it might be\nthat the area in feature space is constructed in IT cortex and then simply transmitted to MTL ( and\nperhaps other regions such as the temporal pole; (TP); this is the more a problem that at iEEG level\nlittle difference between TE and MTL was reported, if anything leads were more responsive and selective\nin TE;\nWe thank Dr. Orban for the expert and constructive comments.\nThis is an important question. Dr. Orban is correct that the present study does not include single-neuron\nrecordings in the fusiform gyrus (FG) or inferior temporal gyrus (ITG). However, in a separate study\ninvestigating the same computational framework for faces (using the identical task but with faces only),\nwe obtained cellular recordings from a monkey’s inferotemporal cortex (ITC; analogous to the human\nVTC). Specifically, we recorded from 53 multi-unit activity (MUA) channels. By applying the same\nprocedure to assess axis coding on these MUA channels, we observed robust axis coding of visual\nfeatures in the monkey’s ITC (n = 53, 100%; strength of axis coding = 0.48 ± 0.13 [mean ± SD]). In\ncontrast, only 16 out of 53 monkey ITC neurons (30.19%; binomial P = 4.34×10−10) exhibited region\ncoding, a significantly lower percentage than that of axis coding (100%; χ2-test: P = 4.73×10−14).\nNotably, the significant proportion of ITC neurons exhibiting region coding was likely driven by their\nstrong axis coding, as an elevated response at the border of the feature space can drive both axis and\nPage 1 of 63\nA-PDF Comment Collector Demo. Purchase from www.A-PDF.com to remove the watermark\nregion coding 1. Therefore, unlike the MTL, the ITC/VTC primarily exhibited axis coding rather than\nregion coding. This suggests that region coding in the MTL was not simply transmitted from the VTC.\naddressed here is visual object identity (important for recognition), even if stimuli we organized in\narbitrary categories, not the properties of concepts typical of semantic memory. There is considerable\nevidence that semantic processing occurs in TP (Lambon Ralph et al Nat NS Rev 2017, Tiesinga et al\nScientific reports 2023), even if MTL neurons have been labelled concept cells to state that they are\nsensory invariant.\nWe thank Dr. Orban for pointing this out. First, we would like to clarify that we did not mention or make\nany arguments regarding episodic memory. Our present results pertain solely to visual object processing.\nSecond, Dr. Orban is correct that our focus is on visual object identity, which is crucial for recognition.\nWe observed category-selective MTL cells, which have long been regarded as fundamental building\nblocks of semantic memory 2-5—one component of declarative memory, alongside episodic memory.\nThird, we agree with Dr. Orban that a significant amount of semantic processing occurs in the temporal\npole 6,7. However, extensive literature has also demonstrated semantic processing in MTL neurons 2,8-13.\nLastly, in the revised manuscript, we have ensured that all statements regarding memory (which appear\nin only three instances) are accurate.\nand single cell level is at least as strong in Amygdala as in PH or AH (fig 4 and 5). Yet these structures\nare supposed to have very different behavioral functions, a finding not discussed. In fact such results\nsupport the view that the single cell selectivity is broad cast from a common input, eg FG or IT cortex.\nWe thank Dr. Orban for pointing this out. In this study, we analyzed three subregions of the MTL\n(amygdala, AH, and PH) separately. Dr. Orban is correct that responses and selectivity at both the iEEG\nand single-neuron levels were similar across these subregions (especially for region coding). While these\nMTL subregions are associated with distinct functions, numerous studies have shown similar responses\nacross them for visual processing of faces and objects 2,8-13. In our previous studies, we specifically\nPage 2 of 63\ncompared the visual coding of faces 1 and objects 13 between the amygdala and hippocampus and\nobserved qualitatively similar results in both regions. Therefore, our present findings are highly\nconsistent with the literature.\nFurthermore, we agree with Dr. Orban that the similarity between MTL subregions may arise from a\ncommon input, such as the FG and/or ITG. Notably, this possibility is supported by the similar\nfunctional connectivity of the amygdala and hippocampus with the VTC, as demonstrated in the present\nstudy (Fig. 6; Fig. 7).\nWe have included the following discussion in the revised manuscript:\n“We observed comparable region coding across MTL subregions (PH, AH, and amygdala). While these\nregions are associated with distinct cognitive and affective functions, extensive literature has shown that\nthey share commonalities in the visual processing of faces and objects 2,8-13. Our previous studies\ndirectly comparing face 1 and object 13 coding in the amygdala and hippocampus also found qualitatively\nsimilar results between these regions, making our present findings highly consistent with the literature.\nThis similarity may stem from a common input, such as the FG and/or ITG, as supported by the similar\nfunctional connectivity of the amygdala and hippocampus with the VTC (Fig. 6; Fig. 7).”\nlatencies, it is likely that leads from different patients were included with numbers varying between\nareas. In this case mixed effects models with patients as random variable may be more appropriate than\nthe chi square or unreported statistical tests: see Yu et al Neuron 2022 and Tiesinga et al 2023.\nWe agree with Dr. Orban that the number of channels contributed by different patients should be\nconsidered in between-area comparisons. As Dr. Orban suggested, we applied a linear mixed effects\nmodel to compare the percentage of visually responsive channels and latencies across brain areas, with\npatient ID as a random variable. The results remained consistent (see Revision Fig. 1 for details). We\nhave included these control analyses in the revised manuscript:\nPage 3 of 63\n“The results remained consistent using a linear mixed effects model where patient index was included as\na random variable (Percentage ~ Region + 1|Patient; Ps < 0.05 for all comparisons between the FG and\nother regions).”\n**\n**\n*\n****\n100\n80\n60\n40\n20\ndala\nRevision Fig. 1. Percentage of visually responsive channels for each brain region. Asterisks indicate a\nsignificant difference in the percentage using a linear mixed effects model. *: P < 0.05, **: P < 0.01,\n***: P < 0.001, and ****: P < 0.0001. FG: fusiform gyrus. ITG: inferior temporal gyrus. AH: anterior\nhippocampus. PH: posterior hippocampus. Each circle represents a patient, and the error bars denote\n±SEM across patients.\nAnd:\n“The response onset latency in the FG (138.5 ± 32.0 ms [median ± SD]) was significantly shorter than in\nall other regions (ITG: 150.5 ± 21.7 ms; PH: 146.5 ± 14.9 ms; AH: 148.5 ± 15.6 ms; all Ps < 0.05; Fig.\n2e, f), while the response onset latency in the amygdala (158.5 ± 20.5 ms) was significantly longer than\nin all other regions (all Ps < 0.05; Fig. 2e, f). The results remained consistent when using a linear mixed\neffects model with patient index included as a random variable (Latency ~ ROI + 1|Patient; all Ps <\n0.05).”\nslennahC\nevisnopseR\nyllausiV\n%\nPage 4 of 63\nOther shortcomings are:\nthat described originally in monkey TE by the Tsao group: 1) in monkey 4 axes have been reported, not 2\nand 2) neurons tuned for these axes are localized in different parts of TE ( here no clear segregation was\nreported); 3) axis tuning was measured with single cells ( here iEEG, and results between these methods\ncan differ); 4) in monkey the features for which axis tuning was observed, were shape parameters,\nextrapolated from earlier single cell studies, here features were derived deep learning and thus\nundefined although a coarse correspondence with the properties of animacy and man-made/natural\nobjects, was stated.\nWe thank Dr. Orban for pointing this out. First, while the number of axes used could be considered\narbitrary (we selected two axes to facilitate the region coding analysis), we would like to clarify that in\n14, “cells were clustered into four networks according to the first two components of their preferred axes,\nforming a map of object space”. Therefore, the monkey study also identified two primary axes.\nHowever, we acknowledge that these axes may represent different stimulus variations. In our study, we\nobserved a similar animate-inanimate axis as in 14 (representing animacy and artificiality), but we\nobserved weaker encoding of the stubby–spiky axis compared to what was reported in 14 (see our new\nSupplementary Fig. 3 and Supplementary Fig. 4). This discrepancy is likely due to differences in the\nstimuli used across studies. Second, we agree with Dr. Orban that neurons tuned to different axes can be\nlocalized in distinct regions of the ITC, and we have discussed this in the revised manuscript. Third, we\nacknowledged the extension of axis coding to the mesoscopic iEEG level (our study) from the\nmicroscopic neuronal level in monkey studies 14. Fourth, it is worth noting that deep neural networks\nwere employed for feature extraction and PCA was used for dimensionality reduction (14 used for raw\nfeatures, while we used it for neural axes) in both cases. The primary differences in tuned axes may be\nattributed to the differences in stimuli, with the monkey study using simple, isolated objects, whereas\nour study used natural scene objects with rich backgrounds.\nWe have included the following discussion in the revised manuscript:\nPage 5 of 63\n“Importantly, as one of the first studies, we revealed axis-based feature coding of objects in the human\nVTC (including both the FG and ITG), as previously reported in non-human primates 14-17. Notably, this\nrobust axis coding was observed at the mesoscopic neural population level (in contrast to the\nmicroscopic neuronal level shown in primate studies 14), indicating that the representation of visual\nfeatures may involve the coordinated activity of larger groups of neurons. The preferred axes of these\nneural populations represented visual changes related to variations in animacy and artificiality\n(Supplementary Fig. 3; Fig. 4a), consistent with primate studies 14. However, we observed weaker\nencoding of the stubby–spiky dimension compared to what was reported in 14 (Supplementary Fig. 3;\nSupplementary Fig. 4; Fig. 4a), a discrepancy likely due to differences in the stimuli used across\nstudies (see also Supplementary Fig. 3 vs. Supplementary Fig. 4). Specifically, the primate study used\nsimple, isolated objects, whereas our study examined natural scene objects with rich backgrounds. It is\nworth noting that DNNs were employed for feature extraction and PCA was used for dimensionality\nreduction (14 used for DNN features, while we used it for neural axes) in both cases. Furthermore,\nextending findings from primate studies 14, our time-resolved feature coding analysis revealed the\ntemporal dynamics of neural coding across different brain areas. It also demonstrated the propagation of\nvisual feature encoding along the ventral pathway, consistent with the hierarchical processing of visual\nobjects 18,19. Additionally, our recordings covered the anterior FG and ITG—areas that have been less\ndiscussed due to challenges in obtaining reliable signals 20. Lastly, it is worth noting that axis-coding\nchannels from different brain regions may encode distinct visual features, as primate neurons tuned to\ndifferent axes can be localized in distinct regions of the ITC 14.”\n6 methods used to derive links between areas are relatively unspecific and global, although effort were\nmade to show they are linked to axis/ category selectivity of leads or preferred categories of neurons.\nPhase locking between leads not very specific (see ter Wal et al Neuroimage 2018); the much superior\nGranger connectivity was calculated on filtered data ( <85HZ) and was restricted to lower frequency\nbands. Using the broad gamma band (50-150Hz) which reflect neuronal activity, Tiesinga et al 2023\nobtained very specific links between particular leads in TE and TP. A more specific pattern of\nconnection may be observed between FG and MTL with this method.\nPage 6 of 63\nWe thank Dr. Orban for pointing this out and appreciate these insightful comments. First, as Dr. Orban\nmentioned, we conducted specific analyses for axis-coding channels and region-coding neurons. Second,\nwe acknowledge the concern regarding potential volume conduction effects on phase-locking value\n(PLV) measurements, which could lead to spurious increases in synchronization and reduced specificity.\nTo minimize these artifacts, we implemented two procedures: (1) applying bipolar referencing to reduce\ncommon noise sources, and (2) determining statistical significance using a permutation procedure, in\nwhich trial labels were shuffled, and the observed PLV was compared to the 95th percentile of the\nsurrogate distribution. Furthermore, multivariate Granger causality (GC) is less susceptible to this issue.\nAs suggested by Dr. Orban, we performed GC analysis on signals that included broadband gamma\n(2-170 Hz). We found that bidirectional synchronization between the VTC and MTL was significant\nspecifically at lower frequencies (<20 Hz; Revision Fig. 2), consistent with the results from the PLV\nanalysis.\n0.015\n0.01\n0.005\n0\n20 40 60 80 100120140160\nFrequency [Hz]\nRevision Fig. 2. Bidirectional Granger causality (GC) between the VTC and MTL. Shaded area denotes\n±SEM across channel pairs. The dashed lines indicate the 95th percentile of the null distribution\nestimated through permutation. Purple: feedforward (from the VTC to the MTL) Granger causality.\nCyan: feedback (from the MTL to the VTC) Granger causality. Dots on the top indicate significant\nGranger causality at that frequency (P < 0.05 across 5 consecutive frequencies after FDR correction 21).\nytilasuaC\nregnarG\nFG-AH (n = 136)\n0.025\n0.02\n0.015\n0.01\n0.005\nytilasuaC\nregnarG\nFG-Amygdala (n = 108)\n0.01\n0.008\n0.006\n0.004\n0.002\nytilasuaC\nregnarG\nITG-AH (n = 94)\n0.03\n0.02\n0.01\nytilasuaC\nregnarG\nFeedforward\nFeedback\n95th Percentile Null\n20 40 60 80 100120140160\nITG-Amygdala (n = 72)\n20 40 60 80 100120140160 20 40 60 80 100120140160\nPage 7 of 63\n7 the choice of object categories is completely arbitrary in the main and control studies.\nWe thank Dr. Orban for pointing this out. Our selection was not completely arbitrary; rather, it was\nguided by the need for a diverse and representative set of object categories that span a broad range of\nsemantic and visual properties. Specifically, for the ImageNet stimuli, we selected 50 object categories\nto encompass a wide variety of natural and artificial objects, ensuring a broad representation across\ntaxonomic (e.g., animals, plants, artifacts) and functional (e.g., tools, furniture, clothing) domains. These\ncategories were chosen to include different levels of visual complexity (e.g., simple geometric objects\nlike plates vs. complex natural forms like crustaceans) and structural properties (e.g., rigid objects like\nbuildings vs. deformable objects like ferns). The selection also aimed to capture distinctions along\nknown perceptual and semantic dimensions, such as animacy (e.g., rodents vs. vehicles) and material\ncomposition (e.g., metallic tools vs. organic fruits). For the Microsoft COCO stimuli in the control task,\nwe selected 10 categories with 50 images each to provide a separate, well-controlled stimulus set that\nstill maintained diversity across natural and artificial categories. The chosen categories cover a range of\nanimate (e.g., bear, elephant, person) and inanimate (e.g., airplane, chair, car) objects, ensuring the\npresence of both living and non-living entities. Both datasets are widely used in computer vision\nresearch, and these categories are among the most commonly represented, providing a well-balanced set\nfor comparison. Together, the diversity in both datasets ensures that our findings are not biased toward a\nspecific subset of object categories and that the computational principles we identify are generalizable\nacross different domains of object recognition. Additionally, our category choices align with previous\nstudies that have investigated object representations in high-level visual cortex, allowing for meaningful\ncomparisons with prior literature.\nWe have included the following justification in the revised manuscript:\n“The choice of object categories was guided by the need for a diverse and representative set spanning a\nbroad range of semantic and visual properties.”\nAnd:\n“These categories encompassed a wide variety of natural and artificial objects, ensuring a broad\nrepresentation across taxonomic (e.g., animals, plants, artifacts) and functional (e.g., tools, furniture,\nPage 8 of 63\nclothing) domains. We aimed to capture distinctions along known perceptual and semantic dimensions,\nsuch as animacy (e.g., rodents vs. vehicles) and material composition (e.g., metallic tools vs. organic\nfruits), while also including objects with different levels of visual complexity and structural properties.”\nAnd:\n“These categories were chosen to provide a well-controlled stimulus set that maintained diversity across\nnatural and artificial categories. The selection ensures the presence of both living and non-living entities,\ncovering a range of animate (e.g., bear, elephant, person) and inanimate (e.g., airplane, chair, car)\nobjects.”\nAnd:\n“Together, the diversity in both datasets ensured that our findings were not biased toward a specific\nsubset of object categories and that the computational principles we identified were generalizable across\ndifferent domains of object recognition. Additionally, our category choices aligned with previous studies\ninvestigating object representations in the higher visual cortex, allowing for meaningful comparisons\nwith prior literature.”\nOther weaknesses:\n8 in the result section on MTL single neurons there is too much reasoning on single cell coding ( as in\nearly H & W papers). Now we know that what matters is a population of tuned neurons which can\nretrieve the identity of object category;\nWe thank Dr. Orban for pointing this out. First, because region coding is a novel coding mechanism,\nwe believe it is important to provide a clear introduction to its principles. Reviewer 4 also suggested that we\nelaborate on this aspect for clarity. Second, while we acknowledge that a population of tuned VTC\nneurons is critical for constructing a neural feature space and retrieving object identity, single-neuron\ncoding remains fundamental. Individual MTL neurons can exhibit distinct selectivity and tuning\nproperties, which contribute to the overall population dynamics. Understanding these individual\nproperties is essential for fully characterizing the neural representation of object categories.\nPage 9 of 63\nof categories are impossible to read\nWe thank Dr. Orban for pointing this out. We have added color codes (i.e., object labels) to Fig. 5e and\nreformatted the feature spaces containing a large number of images into a color-text format to improve\ncategory legibility. Please refer to our revised Fig. 4 and Fig. 5.\n9 overstatements in text: eg in results section it is claimed that the iEEG data delineate responsive\ntemporal cortex, but this requires that also non responsive areas are reported; in the discussion a\ntopographic organization is mentioned, but it is unclear to which result this relates.\nWe thank Dr. Orban for pointing this out. We agree that non-responsive areas are important. To clarify,\nnon-responsive areas were also reported in Fig. 2b, which depicts our full coverage. We have revised the\nmanuscript to clarify the discussion on topographic organization:\n“In this study, we first demonstrated the topographical organization of visual representations for a large\nset of object categories at a fine-grained scale (Fig. 2b-d), as illustrated in primate physiology 22 and\nhuman fMRI studies 23,24.”\n10 were any differences in latencies between subjects observed, as was reported by Tiesinga et al 2023?\nWe thank Dr. Orban for the question. We performed an analysis of covariance (ANCOVA) to test\nwhether response latency differed across subjects within each brain area while controlling for the\ncoordinates (x, y, z) of the channels (Latency ~ Patient + x + y + z). Indeed, visual response latencies\nwere significantly different across subjects within the FG (P = 1.72×10−7), ITG (P = 0.017), amygdala (P\n= 0.001), and AH (P = 0.002). These results are thus consistent with 7.\nPage 10 of 63\nReply to comments from Reviewer 2\nHere, Cao take advantage of a unique data set capturing intracranial EEG (iEEG) from the ventral\ntemporal cortex and single unit recordings from the medial temporal lobe as participants perform a\nsimple one-back task in which they are viewing hundreds of images drawn from different categories. The\nauthors build on prior work in NHPs demonstrating that activity in the VTC appears to be organized\nalong a set of coding axes, where each axis represents the projection along a weighted combination of\nobject features. They then compare this coding to the coding of individual neurons in the MTL, and find\nthat the neuronal spiking responses in the MTL are organized within regions of this VTC coding space.\nThis is a highly interesting and relevant study, as it begins to address the important question of how the\nhuman brain transforms information from densely distributed coding in the temporal lobe ventral cortex\nto sparse spiking activity in the medial temporal lobe. The analyses are well constructed, and the data\nare clearly presented. There are several suggestions, however, that might help strengthen the overall\nclaims of the study.\nOne of the main claims of the manuscript is that feature coding occurs in the VTC whereas region\ncoding within that feature space occurs in the MTL. In particular, they examine how individual neurons\nrespond to the features defined by a PCA of the VTC feature axes. However, in one of the earlier results,\nthe authors show that indeed MTL neuronal activity (specifically the hippocampus, Fig 3e) also encode\nthe object feature space. As such, it would be helpful to understand how the region coding within the\nfeature space differs from the direct feature coding observed in the anterior hippocampus. What is the\nbenefit of performing this extra manipulation.\nWe thank the reviewer for the expert and constructive comments.\nThe reviewer raised an important question. We focused on the ventral temporal cortex (VTC) to\nconstruct a neural feature space because of the vast literature indicating that this brain area encodes\nvisual features using an axis code (i.e., it could have axes for the neural feature space). While the\nanterior hippocampus (AH) contained a significant number of axis-coding channels (Fig. 3c, e), which\ncould lead to an organized neural feature space (Revision Fig. 3a) and showed a significant correlation\nwith the VTC (i.e., fusiform gyrus [FG]) neural feature space (Revision Fig. 3b), we observed only 47\n(5.06%, binomial P = 0.43) region-coding neurons within this space. This was likely because the AH\nneural feature space was not as organized as the VTC neural feature space, making it less effective in\nPage 11 of 63\nmeaningfully representing visual objects. The AH may carry over visual feature information from the\nVTC, but this information could be degraded.\n0.8\n0.6\nITG 0.7503 1 0.4374\n0.4\n0.2\nAH 0.7655 0.4374 1\nAH Neural Feature Space Axis 1\nRevision Fig. 3. Comparison of neural feature spaces. (a) Neural feature space constructed using axis-\ncoding channels from the anterior hippocampus (AH). (b) Representational similarity between axis-\ncoding channels in the fusiform gyrus (FG), inferior temporal gyrus (ITG), and AH. The similarity was\ncalculated using Spearman’s correlation between the representational distances among neural\npopulations in each area. Statistical significance was determined using the Mantel test, where the\nobserved correlation was compared to a null distribution generated by shuffling the matrices’ values\n1000 times. All correlations were significant (Bonferroni correction for multiple comparisons).\nWhen examining region coding, the authors use the feature space defined only by the fusiform gyrus.\nYet there are other regions of the VTC that also capture this feature space, as shown by their data. Why\nrestrict the region coding to only the FG feature space? Moreover, in subsequent analyses examining\nphase locking and Granger causality, rather than just focus on the FG, they then expand their analyses\nto examine the entire VTC. Why would the difference between the two analyses.\nWe thank the reviewer for pointing this out. We used the neural feature space derived from the FG to\nprobe region coding because it was the most robust in axis coding, and the organization of the resultant\nneural feature space appeared the most meaningful. However, we were able to reliably replicate our\n2\nsixA\necapS\nerutaeF\nlarueN\nHA\na b\nPage 12 of 63\nfindings using combined axis-coding channels from both the FG and ITG (i.e., the entire VTC; Revision\nFig. 4). We have clarified this in the revised manuscript.\n“Principal component analysis (PCA) was applied to these axes to create a two-dimensional neural\nfeature space, representing the primary visual information encoded in the VTC (Fig. 4a; note that we\nused axis-coding channels in the FG for subsequent analysis because the FG exhibited the most robust\naxis coding, but we obtained similar results using combined axis-coding channels from both the FG and\nITG).”\n15\n10\n5\nRevision Fig. 4. Region-based feature coding of MTL neurons in the VTC neural feature space. Neural\nfeature space was constructed using combined fusiform gyrus (FG) and inferior temporal gyrus (ITG)\naxis-coding channels. (a) Percentage of region-coding neurons in each MTL ROI. (b) The number of\ncategories encoded by region-coding neurons. (c) The number of objects encoded by region-coding\nneurons (i.e., the number of object images that fell within the tuning region of a region-coding neuron).\n(d) The population aggregated tuning region in each MTL ROI. Color bars show the counts of overlap\nbetween individual tuning regions. Numbers in the density maps show the percentage of the VTC neural\nfeature space covered by the tuning regions of the total observed region-coding neurons.\negatnecreP\nLeft\nRight\n20\n15\n10\n5\nseirogetaC\ndedulcnI\nfo\nrebmuN\n80\n60\n40\nsegamI\ndedulcnI\nfo\nrebmuN\nPH AH Amygdala\nPH\n60\n40\n20\n0\n-20\n-40\n-50 0 50\nVTC Neural Space Axis 1\n2\nsixA\necapS\nlarueN\nCTV\nAH\n4\n60\n3 40\n20\n2\n0\n1\n-20\n0 -40\n-50 0 50\nVTC Neural Space Axis 1\n2\nsixA\necapS\nlarueN\nCTV\nAmygdala\n4\n60\n3 40\n20\n2\n0\n1\n-20\n0 -40\n-50 0 50\nVTC Neural Space Axis 1\n2\nsixA\necapS\nlarueN\nCTV\na b c\nd\n12\n27.05% 36.05% 46.88%\n10\n8\n6\n4\n2\nThe authors present an analysis in which they compare feature based coding in the VTC to category\nbased coding in the MTL using representational similarity analysis. It would be helpful if they could\nclarify exactly how this analysis was conducted and what was compared. Reading through the methods\ndid not clarify this point, but this seems like it would be a nice segue into subsequent analyses on region\ncoding.\nWe thank the reviewer for pointing this out. We have clarified this analysis as follows (note that we have\nalso included a new Supplementary Fig. 5 to better illustrate the image-by-image dissimilarity for the\nrepresentational similarity analysis [RSA]):\n“Importantly, representational similarity analysis (RSA) of the neural pairwise distance of visual objects\n(see Methods) showed that visual objects were similarly represented in axis-coding channels in the VTC\nand category-coding channels in the MTL (Fig. 3h; see Supplementary Fig. 5 for individual\ndissimilarity matrices), indicating information flow between these brain areas.”\nAnd in Methods:\n“For a given ROI, we constructed an image-by-image dissimilarity matrix (DM) using the pairwise\nEuclidean distances between object images based on the responses of the selected channels. Specifically,\nfor each pair of images, we calculated the Euclidean distance of the neural responses to these two\nimages across all axis-coding or category-coding channels in the ROI. The representational similarity\nbetween two ROIs was then computed by correlating the two DMs (e.g., the FG axis-coding DM and the\nAH category-coding DM) using Spearman correlation, which does not assume a linear relationship.”\nThe authors demonstrate significant phase locking in lower frequency bands between the VTC and MTL.\nBut the analysis if Granger causality demonstrates connectivity at much higher frequencies. Could the\nauthors provide some insight as to why the discrepancy? Moreover, when examining the locking of\nindividual neurons to gamma phase, the authors find a subset that indeed exhibit significant phase\nlocking. Here the reported numbers, it appears, are n=9, and n=14. Are these meaningful?\nPage 14 of 63\nWe thank the reviewer for pointing this out. The difference in frequency ranges observed between phase-\nlocking and Granger causality likely arises from the distinct neural mechanisms captured by these two\nmethods. Specifically, phase-locking at lower frequencies (e.g., delta, theta, and alpha bands) between\nthe VTC and MTL suggests that these regions engage in synchronized oscillatory activity, potentially\nsupporting integrative processes such as memory consolidation or long-range communication. Lower-\nfrequency oscillations are known to facilitate large-scale network interactions and may reflect\ncoordinated neural dynamics over broader temporal windows. In contrast, our Granger causality analysis\nrevealed directed connectivity at higher frequencies (e.g., beta and gamma bands), which may be\nassociated with fast-paced information transfer and local computations. Higher-frequency oscillations\ntend to be more transient and localized, supporting rapid processing of visual information and neural\nencoding in VTC-MTL interactions. Thus, the discrepancy between phase-locking and Granger causality\nfrequencies likely reflects complementary aspects of VTC-MTL interactions: lower-frequency\nsynchrony may underlie broad-scale coordination, while higher-frequency connectivity supports\nefficient and rapid information transfer. We have clarified this distinction in the revised manuscript.\n“In this study, we observed phase-locking at lower frequencies, while Granger causality was present at\nhigher frequencies. This difference in frequency ranges may arise from the distinct neural mechanisms\ncaptured by these two methods. Specifically, phase-locking at lower frequencies (e.g., delta, theta, and\nalpha bands) between the VTC and MTL suggests that these regions engage in synchronized oscillatory\nactivity, potentially supporting integrative processes such as memory consolidation or long-range\ncommunication. In contrast, our Granger causality analysis revealed directed connectivity at higher\nfrequencies (e.g., beta and gamma bands), which may be associated with fast-paced information transfer\nand local computations. Therefore, the discrepancy between phase-locking and Granger causality\nfrequencies likely reflects complementary aspects of VTC-MTL interactions: lower-frequency\nsynchrony may underlie broad-scale coordination, while higher-frequency connectivity supports\nefficient and rapid information transfer. Future studies are needed to further differentiate these neural\nprocesses.”\nWe apologize for the confusion regarding the small number of individual neurons showing gamma\nphase-locking. To clarify, these numbers represent neurons exhibiting both region coding and phase-\nlocking. The small count is due to the fact that there were only 35 region-coding neurons out of a total of\n189 neurons to begin with (notably, the percentage of region-coding neurons was consistent with our\nPage 15 of 63\nprevious studies 1,13). It is important to note that among 154 non-region-coding neurons, 124 neurons\nwere phase-locked to gamma oscillations in the VTC (80.52%; 75 phase-locked to the FG and 96 phase-\nlocked to the ITG). This indicates a substantial population of MTL neurons exhibiting significant\ngamma phase-locking to the VTC. Notably, the phase distribution of region-coding neurons was similar\nto that of non-region-coding neurons (Revision Fig. 5). We have clarified these points in the revised\nResults and figure legends:\n“Indeed, we found that MTL region-coding neurons fired spikes that were phase-locked to gamma\noscillations in the VTC (see Fig. 7a, b for examples; 12/35 phase-locked to the FG and 22/35 phase-\nlocked to the ITG; for the entire population: 87/189 phase-locked to the FG and 118/189 phase-locked to\nthe ITG; see Supplementary Fig. 9a-d for analysis in the theta frequency range). Similar to the entire\npopulation, the majority of MTL region-coding neurons fired close to the flanks of the gamma frequency\noscillation (i.e., 90° or 270°; Fig. 7c) in both the FG and ITG.”\nMTL-ITG\n15\n10\n5\n0\n0 100 200 300\nAngle (°)\nRevision Fig. 5. Distribution of the preferred phase of MTL non-region-coding neurons that were phase-\nlocked to VTC gamma oscillations (MTL-FG: n = 75; MTL-ITG: n = 96). The red line indicates the\nphase notation used.\nLastly, we would like to clarify that our previous analysis used matched in-region and out-region trials.\nHowever, in the current analysis, we included all trials, which allowed us to identify more phase-locking\nneurons.\nsnorueN\nfo\nrebmuN\nMTL-FG\n8\n6\n4\n2\n0\n0 100 200 300\nAngle (°)\nPage 16 of 63\nOn a related note, it would probably be helpful if the authors were a little cautious in the interpretation\nof the connectivity results. Specifically, they claim that their analyses demonstrate that VTC coding\nchannels provide more relevant information to the hippocampus during coding. Without an explicit\nmeasure of information and a clear analyses showing how coding is tied to this connectivity, the authors\nmay want to consider tempering this claim.\nWe thank the reviewer for pointing this out. We agree with the reviewer and have removed this argument\nin the revised manuscript. We have also revised the next sentence to be “Notably, AH region-coding\nneurons exhibited significantly stronger phase-locking to VTC axis-coding channels than to VTC non-\naxis-coding channels (Fig. 7d), suggesting that the AH was more tuned to input from the axis-coding\nchannels in the VTC.”\nPage 17 of 63\nReply to comments from Reviewer 3\nSummary of the Study\nThis study investigates the neural mechanisms underlying object representation in the human ventral\ntemporal cortex (VTC) and medial temporal lobe (MTL) using intracranial EEG (iEEG) and single-unit\nrecordings. The authors propose a computational framework in which the VTC encodes visual features\nvia axis-based feature coding, which is then transformed into region-based feature coding in the MTL,\nsupporting semantic representation. Through functional connectivity analyses, they demonstrate\nbidirectional interactions between these regions and provide evidence that axis-coding in the VTC is\nlinked to semantic coding in the MTL. This work builds on prior findings in non-human primates and\next round of reviewer comments... (truncated for brevity)"}]

Decision letter
Peer Review File
Computational single-neuron mechanisms of visual object
coding in the human temporal lobe
Corresponding Author: Dr Shuo Wang
This file contains all reviewer reports in order by version