Title: Localist Topographic Expert Routing: A Barrel Cortex-Inspired Modular Network for Sensorimotor Processing
Venue: NeurIPS 2025
Accepted: yes
Keywords: barrel cortex

expert system

biologically constrained model

brain-inspired intelligence

tactile sensation
Abstract: Biological sensorimotor systems process information through spatially organized, functionally specialized modules.  A canonical example is the rodent barrel cortex, in which each vibrissa (whisker) projects to a dedicated cortical column, forming a precise somatotopic map. This anatomical organization stands in stark contrast to the architectures of most artificial neural networks, which are typically monolithic or rely on globally routed mixture-of-experts (MoE) mechanisms. In this work, we introduce a brain-inspired modular architecture that treats the barrel cortex as a biologically constrained instantiation of an expert system. Each module (or “expert”) corresponds to a cortical column composed of multiple neuron subtypes spanning vertical cortical layers. Sensory signals are routed exclusively to their corresponding columns, with inter-column communication restricted to local neighbors via a sparse gating mechanism. Despite these anatomical constraints, our model achieves competitive, state-of-the-art performance on challenging 3D tactile object classification benchmarks. Columnar parameter sharing provides inherent regularization, enabling 97\% parameter reduction with improved training stability. Notably, constrained localist routing suppresses inter-module activity correlations, mirroring the barrel cortex's lateral inhibition for sensory differentiation, while suggesting MoE's potential to reduce expert redundancy through collaborative constraints. These results demonstrate how cortical principles of localist-expert routing and topographic organization can be translated into machine learning architectures, providing a step toward next-generation expert systems that bridge neuroscience and artificial intelligence. Code is available at https://github.com/fun0515/MultiBarrelModel.

Localist Topographic Expert Routing: A Barrel Cortex-Inspired Modular Network for Sensorimotor Processing

Abstract

Biological sensorimotor systems process information through spatially organized, functionally specialized modules. A canonical example is the rodent barrel cortex, in which each vibrissa (whisker) projects to a dedicated cortical column, forming a precise somatotopic map. This anatomical organization stands in stark contrast to the architectures of most artificial neural networks, which are typically monolithic or rely on expert-isolated mixture-of-experts (MoE) mechanisms. In this work, we introduce a brain-inspired modular architecture that treats the barrel cortex as a biologically constrained instantiation of an expert system. Each module (or 'expert') corresponds to a cortical column composed of multiple neuron subtypes spanning vertical cortical layers. Sensory signals are routed exclusively to their corresponding columns, with inter-column communication restricted to local neighbors via a sparse gating mechanism. Despite these anatomical constraints, our model achieves competitive, state-of-the-art performance on challenging 3D tactile object classification benchmarks. Columnar parameter sharing provides inherent regularization, enabling 97% parameter reduction with improved training stability. Notably, constrained localist routing suppresses inter-module activity correlations, mirroring the barrel cortex's lateral inhibition for sensory differentiation, while suggesting MoE's potential to reduce expert redundancy through collaborative constraints. These results suggest how cortical principles of localist expert routing and topographic organization could potentially be translated into machine learning architectures. Code is available at https://github.com/fun0515/MultiBarrelModel .

1 Introduction

One of the hallmarks of biological intelligence is its use of modular, topographically structured systems to process sensorimotor information [21, 45, 20]. In rodents, the barrel cortex [37, 44] epitomizes this principle: each whisker maps one-to-one onto a dedicated cortical column, enabling localized, efficient processing of tactile input (Fig. 1A). This precise sensor-to-column mapping supports robust spatial discrimination and energy-efficient computation. Extensive neuroscientific studies have characterized both the vertical microcircuitry [12, 36, 48, 14] within each column-organized across layer 2 (L2) through L6-and the horizontal connections that support lateral integration between neighboring columns [32, 12, 23]. However, the implications of this biologically modular and anatomically constrained design for artificial intelligence remain underexplored.

B

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Pyr

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By contrast, contemporary artificial neural networks generally do not impose local specialization. Architectures such as convolutional neural networks (CNNs) [26, 46] and Transformers [49, 8] are predominantly global in their computational flow, lacking explicit modular substructures tied to specific sensory channels. The recent success of Mixture-of-Experts (MoE) models [11, 34, 7, 29] has revived interest in modular computation in artificial intelligence. These models use a gating mechanism to activate a sparse subset of sub-networks ('experts') per input, improving both efficiency and scalability. Nevertheless, the routing strategy in artificial MoEs fundamentally differs from that of the brain. Artificial MoEs focus on task allocation-not enforced inter-expert collaboration and communication. In contrast, the brain utilizes topographic and localist routing, where each expert (e.g., cortical column) processes only its corresponding sensory signal and communicates primarily with spatially adjacent modules. This contrast raises a key question: can we construct a MoEs-like model based on the biologically grounded routing constraints observed in the brain?

In this work, we propose a barrel cortex-inspired modular expert network for sensorimotor learning. The architecture consists of 39 interacting modules, each corresponding to a tactile sensor (whisker), faithfully replicating the somatotopic organization of the rodent barrel cortex. Each module processes its designated sensory stream using an internal structure composed of multiple biologically inspired neuron subtypes, mirroring cortical L2 through L6 (Fig. 1B). All modules share a common architecture

Target subtypes

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and parameter set, reflecting the repeated structure observed across cortical columns, and enabling substantial reductions in parameter count. To enable local integration while preserving spatial structure, we introduce a topographic gating mechanism: each module exchanges information only with its immediate neighbors, reproducing the localized lateral connectivity found in the barrel cortex. This results in sparse, spatially constrained expert activation, in sharp contrast to the global routing strategies used in classical MoE frameworks. Our key contributions are summarized as follows:

Biologically constrained modular architecture: We introduce a modular neural network whose submodules (barrels) replicate the canonical microcircuitry and sensor-mapping of the rodent barrel cortex. Each module integrates multiple neuron subtypes and strictly processes signals from its designated sensor, enabling biologically faithful modular computation.

Localist expert routing: We implement a spatially grounded gating mechanism that enables communication between adjacent modules only, contrasting with current MoEs that neglect inter-expert routing. Experimental results demonstrate that localist connection suppress activity correlations between modules while reducing functional connection distances.

Empirical validation on tactile tasks: Our biologically inspired model achieves stateof-the-art performance on challenging 3D tactile classification datasets [16]. The model reduces parameter usage by 97% through shared columnar weights, while improving training stability. These results demonstrate the viability of embedding strong biological priors into scalable machine learning systems.

Our work positions the barrel cortex as a neurobiologically constrained realization of an expert system, providing a conceptual and empirical bridge between cortical computation and modular artificial intelligence. By integrating neuroscience-inspired modularity with sparse local interactions, we offer a new perspective on the design of efficient, interpretable, and high-performing artificial systems. We argue that principles such as topographic routing, localist specialization, and structural sparsity-long favored by evolution-can inform the development of next-generation machine learning architectures with more brain-like sensorimotor intelligence.

2 Related work

Barrel cortex model: Due to its highly specialized structure and unique tactile functions, the rodent barrel cortex has become a preferred model system for neuroscientists studying sensory perception. Through techniques such as immunohistochemistry, microscopic imaging, and neuronal reconstruction, considerable knowledge has been accumulated on its cellular distribution patterns [36, 48, 42] and vertical [44, 12, 31]/horizontal [37, 23, 13, 3] circuit organization. Based on these anatomical data, computational neuroscientists have developed local circuit models [2, 22, 25] and detailed dendritic models [28] to investigate network dynamics. However, these brain simulation models lack learning capabilities, and few incorporate multi-barrel column architectures. Recently, a trainable superficial layer model that maintains biological plausibility while demonstrating tactile processing functions was proposed [59], though it remains limited to single-barrel representation without incorporating 'whisker-barrel' topographic mapping.

Columnar machine learning models: The uniform yet distinctly layered structure of the neocortex, widely reported in neuroscience, has also drawn attention in machine learning. Several studies [19, 18, 40] have mimicked the columnar organization of the neocortex by designing analogous basic units that exhibit hierarchical feature abstraction and local-to-global integration. However, their information pathways are governed by simplistic rules, resulting in limited performance. Recent columnar models [55, 24] have prioritized hardware-friendly physical implementations to improve computational efficiency. However, these models overlook the fact that cortical column functionality is based on complex and diverse neural circuits [21], and still fall short of matching the performance of contemporary machine learning systems. Meanwhile, loosely modular architectures like MoE [11, 7, 34, 29] in large-scale models are gaining prominence. A promising future direction lies in the simulation of the collaborative paradigm of neocortical regions to construct versatile expert systems capable of multifunctional integration.

3 Methodology

3.1 Biologically constrained columnar modules

To enable scalable yet biologically plausible modular replication, we model barrel columns at the neural pathway-level. While prior single column models [4, 6, 33] achieved one-to-one biological fidelity through massive neuron counts (requiring cluster computing), our architecture strategically incorporates 8 excitatory neuron subtypes and 37 documented projection pathways from barrel cortex studies [37, 44, 12, 14, 31, 39, 27, 58], including projections from Layer 4 (L4) to L2/3 neurons and feedback connections from L5/6 to L4 neurons, among others (Fig. 1B). To enhance scalability, we: (1) represent inhibitory effects through negative weights rather than explicit interneurons, and (2) implement each subtype with 32 cells (256 neurons/barrel), a configuration that maintains biological plausibility while enabling efficient scaling to multiple barrels. Detailed information on the eight summarized neuronal subtypes is provided in the supplementary materials.

Neuronal dynamics follow an adaptive Leaky Integrate-and-Fire (aLIF) model [54] with background currents. The membrane potential V update equation is defined as follows:

, where R m and τ m represent the membrane resistance and time constant, respectively. The input currents I are categorized into four distinct components: I e denotes external input currents, I r represents intra-barrel synaptic currents, I agg corresponds to aggregated inter-barrel currents from neighboring columns, and ϵ denotes the background noise sampled from a standard normal distribution. When the membrane potential V exceeds the firing threshold θ , the neuron emits a spike S . The neuronal firing threshold undergoes an adaptive elevation through spike-triggered accumulation, governed by the following dynamics:

, where θ init and τ adp denote the resting firing threshold and adaptation time constant, respectively, with β being a constant parameter set to 1.8. The recurrent current I r received by a postsynaptic neuronal population from other upstream populations within the home column can be computed as:

, where W j and S ( t ) j denote the connection weight matrix and the spike vector of the j -th upstream population in the presynaptic set P , respectively. The computation processes for I agg and I e in Eq. 1 are elaborated in the following two sections.

3.2 Localist inter-barrel routing

Beyond vertical intra-barrel pathways, the barrel cortex employs horizontal inter-barrel connections for sensory integration. Neuroanatomical evidence indicates that lateral connections follow an adjacency-priority principle [32, 12], with inter-barrel signaling predominantly originating from spatially adjacent columns and minimal contributions from distant ones. We implemented this biologically observed connectivity through K-nearest neighbor (KNN) spatial mapping coupled with sparse gating mechanisms.

For a 32-neuron subtype in our model, input signals from neighboring barrels follow these governing equations. First, the most relevant neighboring subtypes indices T are dynamically selected based on gating weights:

, where K denotes the number of spatially adjacent barrels (nearest neighbors). γ represents the fraction of total ( K · 8 ) neuronal subtypes to be routed, giving Γ selected subtypes. I ( t ) e and ϕ correspond to external input currents and trainable parameters, respectively. TopK ( · ) returns a binary mask ∈ { 0 , 1 } K × 8 with exactly Γ nonzeros. The spatial distance between barrels is computed based on the sensor coordinates provided in the tactile dataset [16]. K and γ were assigned values of 4 and 0.2, respectively.

Then, the final aggregated neighborhood current I ( t ) agg is computed by applying the selected subtypes indices from T ( t ) (mask) to the spiking states S ( t ) g , flattening the results to a vector in R Γ · 32 , and performing a trainable linear transformation:

, where W agg ∈ R 32 × (Γ · 32) denotes the trainable weight matrix mapping the flattened neighborhood activity vector to the 32 neurons of the target barrel, and S ( t ) g = [ s ( t ) 1 , s ( t ) 2 , ..., s ( t ) K × 8 ] ∈ R ( K · 8) × 32

Training loss

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represents the tensor composed of spiking states from all neuronal subtypes in K neighboring barrels at timestep t .

Fig. 2B-C illustrates the temporal state evolution in our multi-barrel model under optogenetic-like stimulation [1, 15], achieved through constant current activation of the central sensorimotor barrel. The results reveal that incorporating inter-barrel current coupling induces propagated activation from the stimulated barrel to neighboring regions, progressively diffusing across the entire array. This spatiotemporal propagation pattern aligns with cortical barrel dynamics reported in empirical optogenetic studies of the 'whisker-barrel' somatosensory system [23, 13, 3] .

3.3 Empirical validation on tactile datasets

We aim to both develop a barrel cortex-inspired architecture and validate the 'whisker-barrel' system as a localist expert processor. Below, we describe the public datasets used and our differentiable readinreadout implementation, which bridges biological computation with machine learning optimization through effective gradient propagation.

Two tactile datasets similar to whisker systems, EvTouch-Objects and EvTouch-Containers [16], were employed to benchmark our model against artificial neural networks. These datasets feature temporal signals recorded from independent NeuTouch sensor arrays [47] during interactions with diverse 3D objects, requiring the model to predict object categories based on dynamic tactile inputs. Each data sample has a shape of [39 , 2 , T ] , representing two channel signals recorded from 39 sensors over T timesteps. Appendix A.1 provides complete tactile dataset specifications.

Wetreat each sensor as a rodent whisker (Fig. 1B), where brainstem-thalamic signal preprocessing [41, 5] is simulated via 2 one-dimensional dilated convolutional layers [17]. A linear layer then serves as the thalamic signal relay. The external current I e received by a neuronal subtype in barrel cortex is computed as:

, where X and ⊛ denote the input data and dilated convolution operation, respectively. W c 1 and W c 2 represent the first- and second-layer convolution kernels. σ is the activation function. The convolution operates exclusively along the temporal dimension, preventing inter-sensor information leakage. The output tensor dimension becomes [39 , 64 , T ′ ] , where T ′ = ⌊ T -d ( k -1) -1 s ⌋ +1 with d , k and s being the dilation rate, kernel size and stride, respectively.

B

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Given the dominant role of L5/6 neurons in driving subcortical projections within barrel cortex [12, 44], we read-out the state of our model's L5/6 neuronal populations through: (1) spatiotemporal integration via a two-dimensional convolution layer, followed by (2) final classification prediction through a multilayer perceptron (MLP). The formula is expressed as follows:

, where ⊖ denotes the two-dimensional convolution operation. W c 3 and W mlp represent the convolution kernel and MLP parameters, respectively. S L 5 / 6 denotes the spiking states of four neuronal ensembles (32 neurons each) in L5/6 across 39 barrels over T ′ timesteps. The model employs standard cross-entropy loss, with spiking neuron gradients computed via the Gaussian surrogate function: N ( V ( t ) | θ ( t ) , σ 2 ) , where σ is set to 0.5.

Given that our multi-barrel model is extended from a single barrel as introduced in Sec. 3.1, a natural question arises: whether these barrels share training parameters or maintain their own independent parameters. Excluding the shared readin and readout pathways, in our 39-barrel configuration, the parameter count of the column modules with shared parameters is reduced by approximately 97% compared to those with independent parameters. In practice, their parameter counts are 59,104 and 2,305,056, respectively, a reduction of two orders of magnitude. This significant decrease in parameters is crucial not only for computational efficiency, but also serves as a form of regularization. In the experimental section, we investigate the tactile task performance of both model variants.

4 Experimental results

4.1 Implementation details

Our multi-barrel model was trained for 200 epochs on both EvTouch-Objects and EvTouch-Containers datasets [16] using an AdamW optimizer [30] with 0.1 weight decay. The initial learning rate of 0.0008 decayed by a factor of 0.8 every 10 epochs. The loss function employed solely the standard cross-entropy criterion. All experiments were conducted on an 80GB NVIDIA A100 GPU.

4.2 Performance comparison with baseline methods

First, we evaluated our multi-barrel model against reported baselines on both EvTouch-Objects and EvTouch-Containers datasets, achieving state-of-the-art (SOTA) classification accuracy (Tab. 1). Our model outperforms the previous best-performing method GGT-SNN [53], achieving accuracy improvements of 2.1% on EvTouch-Objects and 11.7% on EvTouch-Containers. While prior work employed graph neural networks [16, 52, 53] to process sensor topology, our biologically constrained architecture attains comparable performance through simple 'whisker-to-barrel' mapping. Notably,

collapsing the columnar organization into a single barrel reduces accuracy by 5.6% (EvTouch-Objects) and 16.7% (EvTouch-Containers), demonstrating the functional importance of modular architecture.

Furthermore, we compared two model variants from Sec. 3.3: parameter-shared (39 barrels) versus independent-parameter configurations. The shared-parameter variant showed superior performance (2.1% higher on EvTouch-Objects, 1.7% on Containers) with greater training stability, whereas independent-parameter model occasionally failed to train (Fig. 3A). Loss landscape analysis reveals this dichotomy: shared parameters create smooth basins enabling robust convergence, whereas independent parameters yield rugged terrain with local fluctuations (Fig. 3B). This likely occurs because independent parameters per barrel generate conflicting gradient updates during training, thereby complicating optimization. In contrast, shared parameters naturally enforce cross-barrel regularization, effectively mitigating stochastic gradient variations. These results validate the functional advantages of biologically uniform organization and modular replication in artificial expert systems.

4.3 Ablation analysis of synaptic connectivity

Then, we systematically assess synaptic importance in our multi-barrel model through ablation experiments, isolating and blocking each connection while quantifying performance degradation. Taking the EvTouch-Containers dataset as an example, inter-barrel aggregated currents show the greatest impact, reducing classification accuracy by approximately 11.7% (Fig. 4A), while some vertical connections exhibit minimal impact.

Fig. 4B demonstrates laminar-specific effects through directional analysis of 37 intra-barrel vertical projections. The four L4 to L2/3 connections collectively contributed 13.3% to performance, whereas two reciprocal L2/3 to L4 connections showed weaker impacts but stronger L5 modulation. We identified four dominant pathways: L4-L2/3, L4-L6, L2/3-L5, and L6-L4 (Fig. 4C). These pathways align with established neuroanatomical principles: the L4-L2/3-L5 microcircuit represents a canonical barrel cortex circuit [37, 31], and L6 cortico-thalamic pyramidal (CTP) cells receive dendritic inputs from L4 pyramidal neurons [39, 44] and project reciprocally to L4 boundaries [27, 58]. This alignment may suggest that cortical microcircuits represent an evolutionarily optimized architecture.

Besides, deactivating specific synaptic connections (e.g., STP to L2P) does not compromise model performance, suggesting the existence of redundant pathways. This functional resilience likely results from substitution by connections with similar directional properties. Consequently, more precise characterization is required to delineate the distinct contributions of specific neuronal subtypes.

4.4 Analysis of localist routing patterns

Next, we examined the localist routing behavior of our multi-barrel model. In contrast to the decentralized, globally routed structure of conventional MoE systems, each expert (i.e., barrel column) in our system communicates only with its immediate neighbors. As illustrated in Fig. 5A, the KNN gating constraints introduced in Sec. 3.2 yields predominantly short-range horizontal connections in both the initial and the trained models; long-range projections spanning multiple barrels are entirely absent.

To quantify the functional impact of these horizontal connections, we analyzed the correlations of barrel-column activity on the testsets, considering both full-trial and sliding-window time scales (Fig. 5B). Introducing inter-barrel currents decreased global mean correlations by 0.07 on EvTouchObjects and 0.01 on EvTouch-Containers; windowed (local) correlations fell by 0.03 and 0.01, respectively (Fig. 5E). Reduced synchrony suggests that horizontal currents promote richer, more specialized activity patterns across columns, thereby improving the discriminability of complex inputs. This effect echoes the lateral-inhibition mechanism reported in the biological barrel cortex, where activated columns transiently suppress their neighbours to sharpen sensory contrast [38, 35, 10].

The spatial signature of these effects is equally striking. Inter-barrel currents shortened the average distance between strongly correlated column pairs by 0.06 grid units on EvTouch-Objects and 0.17 on EvTouch-Containers; for the top-5 most-correlated pairs, the reductions were even larger-1.8 and 3.48 units, respectively (Fig. 5E). As depicted in Fig. 5C-D, the top-correlation pairs overwhelmingly involved neighbouring barrels once horizontal gating was enabled. Together, these findings show that localist inter-barrel currents simultaneously strengthen short-range functional connectivity and amplify activity differentiation across columns. The results are consistent with long-standing neuro-

A

Initial network

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Initial network

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（EvTouch-Objects ）

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physiological principles [32, 12] and may inform the design of future MoE systems with enforced inter-expert communication to reduce information redundancy [50, 57].

5 Conclusion

In this work, we focus on the rodent barrel cortex as a localist expert system in the brain, developing a multi-barrel model that strictly adheres to the one-to-one 'whisker-barrel' somatotopic mapping. Our architecture faithfully replicates the laminar and columnar organization of barrel cortex, achieving state-of-the-art performance on two tactile datasets while successfully balancing biological plausibility and behavioral performance. Experimental results demonstrate that the cortex's uniform modular

architecture facilitates parameter sharing to enhance training stability, while localist routing reduces functional connection distances and suppresses inter-barrel activity correlations. Inter-barrel currents may sharpen perception and reduce redundancy. This work reveals the potential of brain's native expert systems to inspire next-generation machine learning architectures.

Limitations and future work: On one hand, the redundant synaptic connections observed in Fig. 4B necessitate more refined neuronal subtype differentiation in modeling, which requires deeper anatomical knowledge integration. On the other hand, our multi-barrel model remains distinct from standard MoE architectures and has not yet been validated on mainstream machine learning benchmarks. Developing brain-inspired MoE architectures that emulate cortical coordination mechanisms presents an exciting research direction. Another promising future direction involves embedding our biologically constrained barrel cortex model into realistic real-time interactive environments. This framework would leverage biologically constrained models to explore performance in complex sensory tasks (e.g., navigation), thereby significantly advancing both our understanding of brain operating principles and potential applications.

Acknowledgments

This work was financially supported by the STI 2030-Major Projects (2021ZD0201002), and National Natural Science Foundation of China grants (T2122015, 32471149).

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A Technical Appendices and Supplementary Material

A.1 Tactile task introduction

This section details our tactile perception task. Both employed datasets, EvTouch-Objects and EvTouch-Containers [16], were acquired through an identical protocol: the NeuTouch [47] sensor array engaged in several-second tactile interactions with 3D objects, followed by object category prediction based on the recorded sensor signals (Fig. 6A). The NeuTouch system integrates 39 uniformly configured sensor units, each generating two-channel spiking signals, with their spatial arrangement depicted in Fig. 6B. The datasets were categorized into EvTouch-Objects (720 samples, 36 categories) and EvTouch-Containers (300 samples, 20 categories) based on object taxonomy, with both datasets split into training and test sets at an 8:2 ratio for standardized evaluation.

As described in Sec. 3.3, we modeled each of the 39 tactile sensors as a rodent whisker, with separate barrel modules processing each sensor's signals independently. Each raw sample in the tactile dataset has a shape of [39, 2, T], representing two-channel signals from 39 sensors over T timesteps, where T equals 250 for EvTouch-Objects and 325 for EvTouch-Containers. We first applied two 1D dilated convolutions along the temporal dimension to simulate brainstem and thalamic preprocessing of whisker signals [41, 5], including delay and integration effects [17]. Dilated convolutions were exclusively applied along the time axis to prevent cross-whisker signal leakage (Fig. 6C). The processed timesteps computed as T ′ = ⌊ T -d ( k -1) -1 s ⌋ +1 , where d , k and s represent the dilation rate, kernel size, and stride respectively. Then, the preprocessed whisker signals were independently propagated to their corresponding barrel columns. Finally, a standard 2D convolution operation integrated the barrel-wise L5/6 neuronal states across the entire array, followed by an MLP to generate the final prediction output.

NeuTouch sensors

B

11

25

35

18

7

30

2

12

20

29

37

Temporal

33

1D dilated

34

39

24

31

convolution

Touch

14

22

28

38

8

19

32

X

36

13

26

[39, 2, T]

[39, 64, T’]

X

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a barrel cortex-inspired modular network with localist topographic expert routing for sensorimotor processing. The architecture strictly adheres to biological constraints (one-to-one \"whisker-barrel\" mapping, vertical/horizontal microcircuits) and achieves SOTA performance on 3D tactile tasks with 97% parameter reduction. While biologically innovative and technically sound, broader validation and theoretical grounding are needed for stronger impact.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. Novel Bio-Inspired Architecture:\nFaithfully translates rodent barrel cortex principles (somatotopic organization, laminar microcircuits) into a modular MoE-like system (§3.1–3.2).\n\n2. Localist routing via KNN gating effectively suppresses inter-module correlations, mirroring cortical lateral inhibition (§4.3, Fig. 5).\n\n3. 97% parameter reduction via columnar weight sharing (§3.3) while improving accuracy over baselines (+2.1–11.7% on EvTouch datasets, Table 1).\n\n4. Loss landscape analysis confirms enhanced training stability (§4.2, Fig. 3B).\n\n5. Spatiotemporal activation patterns align with optogenetic studies (§3.2, Fig. 2C-D).\n\n6. Ablation studies validate neuroanatomical principles (L4→L2/3 dominance, Fig. 4C).\n\nWeaknesses \n1. Limited Generalizability (Critical):\nEvaluated only on tactile tasks (EvTouch). Unclear if the architecture generalizes to other modalities (e.g., vision/audio) or non-sensorimotor domains.\n\n2. Insufficient Comparison with MoE Baselines:\nLacks direct comparison to standard MoEs (e.g., Switch Transformer) to quantify benefits of biological constraints (routing efficiency, expert diversity).\n\n3. Methodological Clarity Issues:\nUndefined tensor dimensions (e.g., Eqs in §3.2) and incomplete figure annotations (Fig. 2 temporal evolution).\n\n4. Inadequate explanation for why biological constraints improve performance beyond empirical results.\n\nQUESTIONS:\n1. Can the architecture maintain its efficiency/performance advantages on non-tactile tasks?\nexample: \nTest on two new task types:\n(a) Sensorimotor: Audio event classification (e.g., DCASE datasets) using spectrogram \"topography\"\n(b) Non-sensorimotor: Image classification (e.g., CIFAR-10) with spatial patch → expert mapping\n\n2. Do biological constraints provide tangible benefits over standard MoEs?\nexample:\nAdd 3 baselines with matched params:\n(a) Switch Transformer (global routing)\n(b) Top-k MoE with fixed neighbors (non-adaptive)\n(c) Dense model (all experts active)\n\n3. Which constraints are essential vs. redundant?\nexample: \nRun ablations:\n(a) Remove topographic routing → random neighbor connections\n(b) Remove vertical microcircuits → replace with MLP\n(c) Remove parameter sharing → independent weights\nMeasure performance drop on EvTouch-Containers\n\n4. Does performance degrade with scaled sensor arrays?\nexample: \nSimulate larger arrays (78/156 sensors) via:\n(a) Sensor duplication + noise\n(b) Zero-padding unused inputs\n\nLIMITATIONS:\nThe authors should more systematically explore the conditions under which their method works effectively and where it may fail. This enhances understanding and application of the model by other researchers and guides future research. For instance, experimenting with different input data types can analyze the model's robustness and adaptability. Also, studying the model's performance under extreme conditions or with abnormal inputs can reveal its potential limitations.\n\nRegarding the computational overhead from biological constraints, the authors should conduct an in - depth analysis of the increased computational overhead from the biological constraints in the model design. This includes evaluating their impact on computational resource requirements and model efficiency during training and inference. To boost the model's practicality, methods to optimize computational processes and reduce resource consumption while maintaining biological plausibility should be explored.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper proposed the Modular Network using Localist Topographic Expert Routing, which is inspired by Rodent Barrel Cortex . The method achieved state-of-the-art performance on challenging 3D tactile 13 object classification benchmarks, which shows the effectiveness of this method. The biologically inspired design is novel and interesting, and the experimental results have also demonstrated the effectiveness of the proposed method.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The biologically inspired design is novel. 

2. Routing within and between modules can effectively improve computational efficiency, and the experimental results also demonstrate advantages in performance.

3. It demonstrates how cortical principles inspire machine learning, which is interesting and illuminating.

Weakness:
1. There seems to be no further introduction on how the modules within the functional columns are designed.

2. The article mentions MoE multiple times, but there is no comparison with simple MoE. This could have better demonstrated the effectiveness of the biologically inspired routing.

QUESTIONS:
Question：

Are the different modules within a functional column designed differently, or are they designed identically with each module merely corresponding to the concept of a biological structure?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 3
DATE: 2025-07-02

comments: SUMMARY:
This paper describes a localist MoE architecture based on sensory neuroscience, specifically rodent whisker-barrel cortex. Each expert receives input from a unique sensory channel and experts communicate with their neighbors in the topology induced by the input space. Each expert is a spiking NN modeled after laminar cortex. The model shows SOTA performance on two haptic object classification tasks.

STRENGTHS AND WEAKNESSES:
Strengths:

Novel localist MoE architecture

Impressive experimental results

Weaknesses:

It's unclear how scalable the approach is. The current implementation using spiking neurons and recurrence. The ablation study is a bit concerning in this regard as it suggests recurrence is essential to the results.

QUESTIONS:
I realize the ML-brain analogy is intended to go only so far, but is it plausible for cortical columns to share learnable synaptic weights? Assuming no, is there a different solution available to the brain?

For the goal of developing scalable ML systems based on this architecture, it would help to know whether the spiking dynamics and within-column recurrence are essential. Have you investigated your framework in FF analog networks?

It would also help to know the relative importance of the localist MoE architecture versus the biologically based organization within each expert (e.g., differentiated cell types and cortical layers with predefined circuitry).

My understanding of lateral connections in the brain is that they are primary inhibitory for computing local contrast. Fig 2c seems to show the opposite. What patterns do the learned inter-barrel weights show in the main experiments?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 4
DATE: 2025-07-02

comments: SUMMARY:
- This paper proposes a barrel cortex-inspired modular neural network architecture for sensorimotor processing
- Each module corresponds to a cortical column and input sensory signals are routed exclusively to their specific columns/modules. 
- The authors achieve state-of-the-art performance on tactile object classification while reducing parameters by 97% through columnar weight sharing

STRENGTHS AND WEAKNESSES:
strengths:
- Well explained biologically-inspired architecture that faithfully replicates established neuroscientific principles
- Achieves high parameter efficiency while maintaining competitive performance

weaknesses:
- The authors claim that this work “provides a step toward next-generation expert systems that bridge neuroscience and artificial intelligence”, but it is not clear to me how this work really pushes forward either community. More performant and lower parameter count models could be built if there were no biological constraints as there are numerous non-biological ways of reducing model parameter counts. From the neuroscience perspective mouse barrel cortex organization is already well understood and the authors don’t offer any suggestion on how this new model could be used to explore testable hypotheses of neuroscientific interest.

QUESTIONS:
- What audience do the authors think will resonate with this work and how can it be built upon?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 1
ORIGINALITY: 2
DATE: 2025-07-03