From movement to cognitive maps: recurrent neural networks reveal how locomotor development shapes hippocampal spatial coding
Authors: Marco P Abrate
Laurenz Muessig
Joshua P Bassett
Hui Min Tan
Francesca Cacucci
Thomas Joseph Wills
Caswell Barry
Venue: ICLR 2026 Oral (2026)
Keywords: recurrent neural network
spatial representations
hippocampus
development
locomotion
rats
Primary area: applications to neuroscience & cognitive science

Abstract: The hippocampus contains neurons whose firing correlates with an animal's location and orientation in space. Collectively, these neurons are held to support a cognitive map of the environment, enabling the recall of and navigation to specific locations. Although recent studies have characterised the timelines of spatial neuron development, no unifying mechanistic model has yet been proposed. Moreover, the processes driving the emergence of spatial representations in the hippocampus remain unclear (Tan et al., 2017). Here, we combine computational analysis of postnatal locomotor development with a recurrent neural network (RNN) model of hippocampal function to demonstrate how changes in movement statistics -- and the resulting sensory experiences -- shape the formation of spatial tuning. First, we identify distinct developmental stages in rat locomotion during open-field exploration using published experimental data. Then, we train shallow RNNs to predict upcoming visual stimuli from concurrent visual and vestibular inputs, exposing them to trajectories that reflect progressively maturing locomotor patterns. Our findings reveal that these changing movement statistics drive the sequential emergence of spatially tuned units, mirroring the developmental timeline observed in rats. The models generate testable predictions about how spatial tuning properties mature -- predictions we confirm through analysis of hippocampal recordings. Critically, we demonstrate that replicating the specific statistics of developmental locomotion -- rather than merely accelerating sensory change -- is essential for the emergence of an allocentric spatial representation. These results establish a mechanistic link between embodied sensorimotor experience and the ontogeny of hippocampal spatial neurons, with significant implications for neurodevelopmental research and predictive models of navigational brain circuits.

Published as a conference paper at ICLR 2026
FROM MOVEMENT TO COGNITIVE MAPS: RECURRENT NEURAL NETWORKS REVEAL HOW LOCOMOTOR DE- VELOPMENT SHAPES HIPPOCAMPAL SPATIAL CODING
Marco Abrate', Laurenz Muessig*, Joshua P Bassett', Hui Min Tan Francesca Cacucci', Thomas J Wills".1, Caswell Barryl.t
Dept. of Cell and Developmental Biology; University College London
Dept. of Neuroscience, Physiology, and Pharmacology; University College London Animal Concerns Research & Education Society of Singapore Equua contribution.
Correspondence: fmarco. abrate, t.wills, caswell.. heteel..barryy@ucc .cc .uk
ABSTRACT
The hippocampus contains neurons whose firing correlates with an animal's 's loca- tion and orientation in space. Collectively, these neurons held to support cog- nitive map of the environment, enabling the recall of and navigation to specific lo- cations. Although recent studies have characterised the timelines of spatial neuron development, no unifying mechanistic model has yet been proposed. Moreover, the processes driving the emergence of spatial representations in the hippocampus remain unclear (Tan et al. 2017). Here, we combine computational analysis of postnatal locomotor development with a recurrent neural network (RNN) model of hippocampal function to demonstrate how changes in movement statistics and the resulting sensory experiences shape the formation of spatial tuning. First, we identify distinct developmental stages in rat locomotion during open-field explo- ration using published experimental data. Then, we train shallow RNNs to predict upcoming visual stimuli from concurrent visual and vestibular inputs, exposing them to trajectories that reflect progressively maturing locomotor patterns. Our findings reveal that these changing movement statisties drive the sequential emer- gence of spatially tuned units, mirroring the developmental timeline observed rats. The models generate testable predictions about how spatial tuning proper- ties mature predictions we confirm through analysis of hippocampal recordings. Critically, we demonstrate that replicating the specific statistics of developmen- tal locomotion rather than merely accelerating sensory change is essential for the emergence of an allocentric spatial representation. These results establish a mechanistic link between embodied sensorimotor experience and the ontogeny of hippocampal spatial neurons, with significant implications for neurodevelopmen- tal research and predictive models of navigational brain circuits.
INTRODUCTION
The hippocampal formation is critical structure in the mammalian brain, necessary to support spatial memory and navigation. The spatially modulated neurons it contains, which provide rep- resentation of self-location and orientation, are widely thought to be the basis of a cognitive map supporting flexible navigation through the environment [Tolman) 1948) D'keefe & Nadel 1978). Perhaps best known of these, place cells pyramidal neurons of hippocampal regions CA3 and CA1 which fire selectively when an animal occupies particular locations. These cells provide a sparse, allocentric (i.e., world-centred and independent of the observer's of the environment that persists even when individual environmental cues are altered or removed, suggesting they encode spatial position rather than individual sensory features (O'Keefe & Dostro- vsky 1971 Keeeee & Conway 1978 Complementing this positional code, head direction cells
position) mapping
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found primarily in the subicular complex and anterior thalamus signal orientation, firing when the animal faces specifie allocentric directions (Taube et al. 1990). Within this navigational network, additional spatial cell types contribute to the brain's ' representation of space. Soundaar-reesonsive cells respond when the animal is at specific distances and directions from borders (Savelli et al.) [2008] [Solstad et al.) 2008 [ever et al. 2009), while grid cells in the medial entorhinal cortex exhibit distinctive hexagonal firing patterns that systematically tile the environment (Hafting et al. 2005). Neurons with properties of multiple spatial coding categories have also been observed throughout the hippocampal formation, highlighting the complex and distributed nature of spatial processing.
The post-natal development of these neurons in rats follows a specific timeline that has been well characterised in recent studies (Wills et al. 2010; Langston et al.] 2010;; TTn et al.] 2017). Head direction cells emerge earliest and can be detected in an immature form as early as postnatal days (P) 11 and 12 (Tan et al. 2015. [Bjerkes et al. 2015), with adult-like cells emerging at P15 (Wills) jet al. 2010| Langston et al. 2010). Spatially-localised firing can be observed in place cells as early as P14 (Muessig et al.) 2015), ) but this is less spatially specific and stable than that observed in adults. These characteristics then slowly mature over the next 3-4 weeks (Scott et al. 201T). Grid cells are the last spatially- moddulated neurons to emerge in the rat, with stable and periodic grid firing first observable at P20 (Wills et al. (2100). and the transition to adult-like grid patterns occurs remarkably rapidly- - over approximately 24 hours (Wills et al.) 2012). The mechanisms driving the maturation of spatially tuned neurons in the hippocampus and the relative timing of their emergence remain unclear (Tan et al., 2017). In this study, we hypothesise that the sequential nature of sensory and motor experiences fundamentally influences the emergence and maturation of spatial representations in the hippocampus.
The postnatal development of locomotion in laboratory rats involves the maturation of both postural and locomotor skills. According to Altman Sudarshan (1975). until P8 uncoordinated hindlimbs result in forelimb-driven pivoting movements. Between P8 and P14, quadrupedal walking begins to emerge, although hindlimbs still lag behind. From P14, this raised posture becomes dominant, with locomotor speeds and distances travelled continuing to increase throughout the third week of life (Gerrish & Alberts 1996). The temporal alignment between locomotor and spatial neuron development is striking, suggesting a potential causal relationship. However, manipulations of the postnatal motor system that could test our hypothesis are difficult to implement and often require invasive procedures such as tenotomy, neurotomy, and nerve crushing (Lowrie & Vrbova 1992). An alternative approach is computational modelling, which allows the relationship between behaviour, sensory sampling of the environment, and neuronal representations to be more easily manipulated.
Animals perceive their environment through sensory organs that provide inherently egocentric (i.e., self-centred) information what is seen, heard, or felt from the animal's is current position and ori- entation. The transformation of egocentric information into a coherent world-centred (allocentric) representation is crucial to support flexible navigation strategies such as taking shorteuts or plan- ning routes to unseen locations [Tolman 1948). The predictive learning framework positions the hippocampal formation as the system that compares incoming sensory stimuli with memory derived predictions (Eichenbaum] 2004 Levy 1989). and it is supported by observations that hippocampal neurons encode not just current location but trajectories of possible future positions (Johnson & Redish 2007 Kay et al.) 2020 Ujfalussy & Orbán 2022). The integration of egocentric sensory information within this framework represents a fundamental computational challenge in spatial cog- nition: how to construct a stable internal model of space from sequence of observations (Stachen- feld et al. 2017 Buzsáki & Tingley] 2018). Among sensory modalities, vision appears particularly important for this process in many mammals, providing rich spatial information that extends beyond immediate physical contact. Further, the influence of visual cues on spatial representations is evident in numerous studies where manipulating visual landmarks directly affects the firing of hippocampal spatial cells OO'Keefe & Conway| 1978: Jeffery & O O'Keefe, 1999] Acharya et al.) 2016). Indeed, place cells remap in darkness, and grid cells lose their characteristic hexagonal firing patterns with- out visual inputs (Chen et al. 2016 Féroz-Escobar et al.) 2016, Waaga et al. 2022). highlighting vision's role in maintaining stable spatial representations while accommodating the integration of
other sensory modalities.
Recent computational approaches have demonstrated considerable success in modelling this ego- to-allocentric transformation using recurrent neural networks (RNNs) trained on self-supervised predictive tasks. These models spontaneously develop spatially tuned units reminiscent of bio- logical spatial cells. Various implementations have explored different aspects of spatial learning.
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including reinforcement learning frameworks (Stachenfeld et al. (017). navigation through abstract
spaces (Whitiinttnn et al. 2020). prediction from visual information (Uria et al. 2022; Levenstein
jet al. 2024] Gornet & Thomson 2024), integration of vestibular imputs (Cueva & Wei 2018). or
a sparse combination of multiple sensory modalities (Recanatesi et al.) 2021). Moreover, Cueva &
Weil (2018) examined the emergence order of spatial cells across training epochs, observing head direction cells first, then border cells, and finally grid cells - in rough accordance with experimental findings. These studies suggest that predictive learning anticipating future sensory inputs based on current and past observations may be fundamental computational principle underlying the emergence of spatial representations in both artificial systems and biological brains.
While previous models have demonstrated that spatial representations can emerge from predictive learning, they have not addressed how developmental progression in sensorimotor experience influ- ences spatial coding ontogeny. In this paper, we provide computational framework for understand- ing how developmental changes in locomotion shape spatial representations in the hippocampus. We first classify locomotor development through computational analysis of experimental data, revealing distinct developmental stages, Using RNNs trained to predict visual inputs from concurrent visual and vestibular information, we demonstrate that developmental changes in movement patterns drive the emergence of spatially tuned units that recapitulate biological developmental timelines. Impor- tantly, our model makes novel prediction, namely the appearance of directional selectivity in place cells- - finding we validate through analysis of previously collected hippocampal recordings from developing rats.
2 COMPUTATIONAL ANALYSIS OF POSTNATAL LOCOMOTOR DEVELOPMENT
Altman & Sudarshan (975) identified clear locomotion stages in rats through manual labelling of movement speed during open field foraging. We introduce a computational method to characterise developmental locomotor stages (briefly described hereafter, details in Appendix C We aggregated data from multiple previous studies (Wills et al.. 2010: Tan et al. 2015 Muessig al. 2015 Bassett ctt al. 2018 Muessig et al. 2019) where rats explored familiar square open field environments.
For rats aged P11 to P25, we computed probability density functions of speed and rotational speed, along with transition probabilities between spatial bins. We quantified locomotor differences across ages using censen-Shannon distances between these distributions and transition matrices, then ag- gregated results into a correlation matrix (Figure [T]). Analysing individual rats rather than age- grouped cohorts captured individual variability in locomotor maturation allowing rats of the same age to cluster differently based on their developmental stage. Two dimensional t-SNE visualisation (Van der Maaten & Hinton (008)) of the correlation matrix revealed distinct clusters of movement statistics (Figure [b). Gaussian mixture modelling identified three optimal clusters through min- imising the Bayesian information criterion (Schwarz, 1978) (Figure corresponding to rats with median ages of P13.5, P16, and P20. We designate these developmental stages as crawl, walk, and run, respectively. An additional group included adult rats aged 3-6 months. The movement pat- terns characterised by each group provide the basis for generating synthetic trajectories used to train separate RNNs at each developmental stage.
2.1 SIMULATION OF NOVEL TRAJECTORIES CORRESPONDING TO DEVELOPMENTAL LOCOMOTION STAGES
We simulated novel rodent trajectories using the open-source toolbox RatInABox (George et al., 2024). The software implements an CensteinUUhlebbed process continuous random walk with tendency to return to central drift value. We tuned key parameters to match each of the three devel- opmental locomotion clusters and the adult one observed experimentally: the mode of the Rayleigh distribution from which agent speed was sampled, the timescale over which speed decorrelated, the standard deviation of rotational velocity, and thigmotaxis strength - the tendency to stay near walls. Simulations generated agent position, head direction, speed, and rotational speed at Hz temporal resolution (Figure [T.) We designed a landmark rich virtual arena to replicate experimental condi- tions (Figure lt). To emulate rat vision (Hughes) 1979), captured low-resolution black and white frames (32 x 16 pixels) using a panoramic camera with 240 horizontal and 120 vertical fields of view positioned according to the agent's location and head direction (Figure TT).
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Clustering analysis of developmental locomotion stages and trajectory simulation. Correlation matrix of locomotion metrics across rats aged P11-P25 (cold/warm colours indicate low/high correlations). b t-SNE visualisation of the correlation matrix. Marker shapes indicate locomotion clusters, colours show rat ages. Bayesian information criterion (BIC) values for Gaus- sian mixture model with varying cluster numbers (lower values indicate better fit, minimum at clusters). Top view of the virtual arena with landmarks, used for simulations. e Example simu- lated trajectory showing agent's past positions (blue, fading with time), current position (red), head direction 0, velocity v, and rotational velocity w. Visual frame showing the simulated rat's eye view (panoramic camera).
Figure 1:
RESULTS
We used single-layer recurrent neural network (RNN) our artificial hippocampus model (Figure 21). This network acts as a functional approximation of the hippocampal formation as a whole - captures the fundamental computational principle of recurrent dynamics enabling the maintenance of internal states that predict future sensory input, and it is not intended as a literal circuit model of any specific subregion. The recurrence in our model abstracts over multiple sources of temporal integration in the biological system, including CA3 recurrent collaterals, the hippocampal-enntrhinng loop, and broader hiticc-aippocampall interactions. Moreover, our framework assumes inductive biases analogous to genetic programs through its architecture, input format, and learning objective. Indeed, hippocampal pyramidal neurons are generated between embryonic days (E) 16-21 (Bayer) 1980), establishing the architecture upon which experience operates.
The network was composed of N₁ 500 hidden units and it was trained to predict upcoming visual stimuli from concurrent visual and vestibular inputs. To emulate the information available to navigating rats, at each timestep I,, the flattened frame Y, E RR-y (where N, = 16-31 -2 is the number of pixels in frame), the velocity vector (x and components) vi, and the rotational speed w, were concatenated to form the input X, concat (Y, w)) E +3)+ The hidden state H R.M and the predicted frame Ye41 were computed as:
H = (X,W H,W,)
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Figure 2: Recurrent neural network (RNN) model of hippocampal function. RNN schematic. At each timestep I, the network received a concatenation of the agent's visual input Y,, velocity vector Vi, and rotational velocity Wr- For the adult locomotion stage, additional variant was trained -hhere the hidden layer was initialised to linear projection Wa of population of grid cells at trajectory onset and every 90 timesteps. The predicted frame Y++1 is decoded from the hidden state with a linear projection W. and compared to the observed frame Y++1 using the distance. b,c,d RNN trained simulated trajectories belonging to the adult locomotion stage. Train (blue) and validation (orange) loss trends throughout training. e Example predicted frame (top) and its ground truth comparison (bottom), sampled from the validation set. Subset of rate maps in (x.y) space (warmer colour indicate higher activity) of hidden units classified as place cells and corresponding SI, values (left), and subset of polar maps in polar space of hidden units classified as head direction cells and corresponding SIa values (right).
Where the sigmoid function, and W₂ R)N Wa RN,,N,, and W. R.N, (N are the learnable weight matrices applied to the input, the previous hidden state, and the current hidden state, respectively. The loss was calculated as the average absolute distance between the predicted frame Y1+1 and the ground truth Y(+1-
We trained the network on movement data corresponding to each developmental stage (Section 2}. beginning with the youngest rats and continuing through maturation. To emulate continuous learning during development, each network inherited weights from the previous stage. Namely, we trained networks sequentially: starting from scratch on crawl (youngest), then fine-tuning on walk, run, and finally on adult. For each locomotion stage, we trained models on 17 sequences of 720 frames (12,240 samples total, equivalent to 3.5 hours of simulated experience) of simulated trajectories (Section P. We employed back-propagation through time with teacher forcing on 9- timestep windows, and trained for 1,500 epochs which was enough to reach convergence (Figure 2p). Validation of each model used 4 simulated sequences (2,880 frames, 1 hour of experience).
When training on simulations of developing rats, the hidden layer was simply initialised to zero at the start of each trajectory. For the adult locomotion stage specifically investigate the functional role of grid cells we also trained a variant of our RNN which included grid cell input. This addition is justified by previous studies showing grid cells mature only in adulthood (Wills al.) 2010 Bjerknes et al. (2014). In this variant, we trained the network from scratch and initialised the hidden layer trajectory onset and every 90 timesteps (1.5 minutes) a linear learnable projection of 25 grid cells G, with varying grid scales (specifying firing field spacing and size):
G.WT Bq.
when 15690 = 0 otherwise
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We analysed hidden unit activity with respect to agent position and head direction using 9 test sequences (3,600 frames, 2 hours of experience). To quantify spatial selectivity, computed rate maps two-dimensional representations of neural firing rates across spatial locations. The use of rate maps is standard practice in neuroscience for analysing place cell recordings (Muller & Kubie, 1989). We divided the arena into a 25 x25 grid and calculated rate map R unit l by averaging its activity within each spatial bin:
Σ, 10t 1 prEBin (1, B2) (1,..., Bp)
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Figure 3: Randomly selected rate maps (a-e) and polar maps (f-j) of units classified place cells or head direction cells from RNNs trained at the different locomotion stages (Model), and corre- sponding neurons with the highest correlation (shown hippocampal CAI recordings (Real). Rate maps from a crawl (no comparison with real data), walk, e run, adult, and adult with grid cell input. Polar maps from crawl (no comparison with real data), g walk, h run, adult, and adult with grid cell input.
the top right of each matched cell) from
where (k) is the activity of hidden unit p is the agent's position vector and y components), and B, is the number of bins along each direction. Both numerator and denominator were smoothed using a Gaussian filter (a 0.75 bins). We quantified spatial selectivity using spatial information (SI,, definition in Appendix E computed on min-max normalised rate maps following the stan- dard approach of subtracting the minimum and dividing by the range (difference between maximum and minimum values).
For directional selectivity, we calculated rate maps polar space (polar maps) by binning head direction into 60 angular bins. We measured directional tuning using the resultant vector length which quantifies the concentration of the directional preference and spatial information a polar map (SLz), both defined in Appendix E Following standard neuroscience elassification criteria (Muller & Kubie 1989; Taube et al. 1990). units with SI, > 0.3 were classified as place cells, and units with resultant vector length
0.3 or SI 0.2 head direction cells.
3.1 MATURATION OF LOCOMOTION EXPLAINS HIPPOCAMPAL SPATIAL CELL DEVELOPMENT
When training completed, the RNN successfully learned the visual prediction task, as evidenced by decreasing validation loss and accurate frame reconstructions (Figure p.c). When trained with adult locomotion, hidden units developed clear spatial and directional tuning (Figure We next exam- ined how spatial representations emerge through locomotor development, extracting latent represen- tations from models trained on each stage. We compared our networks' hidden state to hippocampal CA1 recordings (Wills et al. 2010 Muessig et al.) 2015) 2019) from developing rats (P14-P25) and adults during open-field foraging Figure shows qualitative comparison of subset of neurons. These experimental trajectories were part of the data used in our clustering analysis (Section No rat with hippocampal recording part of the crawl stage, consequence of the fact that younger

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rats do not have sufficient position coverage to asses two dimensional firing correlates. Our compar- isons focused on CAI because this is the region for which developmental data was available and - as the anatomical output of the hippocampal formation CAA responses are indicative of the system's 's final processed output.
We quantified spatial selectivity by computing spatial information from rate maps (SI,) of units in the network's 's latent space and of hippocampal neurons (Figure 4 For directional selectivity, we calculated spatial information (SL,) and resultant vector length from polar maps of artificial and biological neurons (Figure I.e). Experimental data showed significantly increasing trends across all spatial metrics throughout development, confirmed by Jonckheere- Maesseere-Terpstra (JT) tests for ordered differences (p < 0.001 for all metrics). Similarly. the model showed - from walk to adult signif- icantly increasing trends in spatial tuning (JT test: SI, P < 0.01, directional metrics p < 0.001)- We validated model-experiment correspondence by comparing Jensen-Shanne distances between model and experimental distributions against null distributions generated by shuffling network units across stages 10,000 times (Figure 4D. Using established neuroscience classification criteria (Muller) & Kubie 1989 Taube et al. 1990p. we classified network units and hippocampal neurons as place cells (Figure 4h) or head direction cells (Figure 4p). Experimental data showed significant increases in both cell types across locomotion stages (JT test: place cells p < 0.001, head direction cells p 0.05). Networks trained sequentially across locomotion clusters recapitulated the progressive emergence of spatial tuning observed in hippocampal development (Figure 4. with one important exception: purely spatial place cells maturation required grid cell input in the adult model to reach experimentally observed levels. This highlights that mature positional tuning, and consequently the full increase in place cell numbers, may depend on grid cell input in adult rats.
These results prove that sensory experiences shaped by movement play a key role in spatial repre- sentation emergence and maturation. Additional supporting evidence includes decreased decoding errors for position and head direction from hidden-layer activity (Figure(5h), indicating formation of an allocentric map of the environment. To quantify the functional importance of spatial cells (Luo) et al. 2024), we performed lesion studies by silencing classified place or head direction cells and measuring validation loss. Across 100 repetitions per stage, spatial cells became increasingly criti- cal for task performance (Figure 5p), confirming their role in spatial knowledge acquisition. Finally, we assessed the robustness of spatial representation emergence by calculating inter-trial correla- tions, and training RNNs with different parameters. Population-leve Pearson correlation analyses between rate maps (and polar rate maps) computed on the first and second halves of the held-out validation trajectories consistently remained above 0.8 across development (Figure A8). Moreover, reducing or increasing by 25% the number of hidden units, training for up to 2,500 epochs at each stage, and halving or doubling the input dimensions did not alter spatial tuning maturation (Figures A3 and A4). These additional results demonstrate the stability of our findings.
3.2
THE MODEL SUGGESTS A DEVELOPMENTAL TREND IN THE DIRECTIONAL SELECTIVITY OF PLACE CELLS VALIDATED BY REAL DATA
The observed increase in directional tuning and head direction (HD) cell numbers in our model (Fig- ure -e- occurs primarily in neurons that also encode position (Figure 4g. solid line), while pure directional selectivity emerges at the "walk" stage corresponding to approximately P16, with in- stances at younger ages - and remains stable thereafter (Figure A9). This pattern of pure directional tuning aligns with experimental findings reporting adult-like HD cells by P15 in dorsal presubicu- lum and medial entorhinal cortex (Wills et al. 2010 Langston et al. 2010), with immature HD signals observable as early as P12 (Tan et al.] 2013 Bjerknes et 2015). However, the emergence of mixed selectivity in spatial neurons, and specifically the development of directional selectivity in CA1 neurons, has not been previously studied.
To investigate whether the emergence of conjunctive place-HI tuning suggested by our model is present in experimental hippocampal data, we applied corrected polar maps (Burgess et al., 2005) following the distributive hypothesis (Muller et al. 1994) to control for potential confounds such as inhomogeneous orientation sampling. We classified cells as HD cells only when both standard criteria (SI. and resultant vector length) and corrected polar map criteria were met, with Pearson correlation between original and corrected maps exceeding 0.5 identifying correlations below this threshold as sampling artefacts. This analysis revealed a significant, previously unreported developmental increase in conjunctive place-direction cells (Figure/4p, dashed line) in hippocampal
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Figure 4: Maturation of locomotion explains the development of hippocampal spatial cells. We fer to RNNs with the locomotion cluster their training data was simulated from. Experimental data (dotted lines) shows simultaneously recorded ensembles of cells, grouped by the rat's locomotion stage at the age of recording. Diamonds indicate adult model trained with grid cell input. Asterisks denote significance levels from one-sided pairwise Wilcoxon rank-sum tests (Wilcoxon, 1992) with Benjamini- Hochhbergg correction (Hollander et al.) (013) testing for increasing differences (exact p-values in [Able[A4] a Percentage of RNN units (solid line) and percentage (mean- of hip- pocampal neurons (dotted line) classified as place cells. b Min-max normalised spatial information SI, (SM))+ for rate maps of RNN units (solid) and hippocampal neurons (dotted). с Percent- age of RNN units (solid) and percentage (Mean=SEM) of hippocampal neurons (dotted) classified as head direction (HD) cells. d Min-max normalised spatial information SIa (mean SEM) for po- lar maps of RNN units (solid) and hippocampal neurons (dotted). Min-max normalised resultant vector length (RVL, -SM for polar maps of RNN units (solid) and hippocampal neurons (dotted). Comparison of the overall Jensen-Shannon (JS) distance (crosses) - calculated between model (no grid cell input) and experimental distributions for metrics shown in b. d, e with null distributions generated by shuffling network units across clusters 10,000 times (violin plots). g Per- centage of RNN units (solid) and percentage (EM) of hippocampal neurons classified as both place and HD cells (dotted).
recordings (JT test, < 0.01). Thus, our model prediction led to new experimental finding: gradual increases directionality within hippocampus occur primarily through conjunctive place-HI cells rather than pure HD cells. Furthermore, in adult rats, our results confirmed AAcharya et al. (016) s finding that approximately 25% of CA1 pyramidal neurons exhibit significant directional selectivity in both real-world and virtual environments. Similar levels of directional tuning have been reported bats (Rubin al. 2014) and mice (Jercog et al. 2019), suggesting this conserved feature across mammalian species.
3.3 SPATIAL CELL DEVELOPMENT NOT EXPLAINED RATE OF CHANGE SENSORY STIMULI, BEHAVIOURAL REPERTOIRE, OR AMOUNT OF TRAINING DATA
Levenstein et al. 2024) demonstrated that RNNs trained to predict visual inputs over extended spatio-temporal sequences develop neural manifolds reflecting environmental structure. They visu- alised these manifolds using Isomap projection and assessed them with spatial Representation Sim- ilarity Analysis (sRSA) (Levenstein et al.) 2024). which quantifies correspondence between neural and spatial distances. This suggests a transition from egocentric (reconstructive) to allocentric (pre- dictive) spatial representations. Given that locomotor development involves increasing speed, task naturally becomes more predictive as inter-frame distances increase (Figure 51d, black line). This transition is reflected in network's sRSA trend (Figure (5)) and Isomap plots (Figure 55p.
To test whether increased predictive demands are sufficient to drive allocentric representations, we trained networks crawl trajectories with extended inter-frame intervals (1-3 seconds), achieving
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grid cells
Figure 5: Behaviour- drive sensory experience not rate of change alone drives the emergence of allocentric spatial representations. Black lines show RNNs trained on trajectories modelled from developmental stages, grey lines show RNNs trained on crawl trajectories with extended sampling gaps (labelled in each plot), diamonds indicate models trained with grid cell input. a Position (top) and head direction (bottom) decoding errors (SM) using linear regression on hidden units (50-50 train-test split on validation data). b Validation loss (mean: when silencing equal numbers of classified place or HD cells (100 repetitions). e Spatial representational similarity anal- ysis SEM) quantifying correlation between agent position and hidden unit activity. d L1 distance between consecutive frames (Mean+SEM) in validation set. e Isomap visualisation of hid- den unit activity random trajectory excerpts, coloured by agent position (x y coordinates).
distances comparable to adult stage (Figure 5()). However, this manipulation failed to reproduce the developmental phenomena observed experimental data (Figure (5).. fact, the original models were significantly more likely to have generated the experimental data, shown by comparing the Bayesian information criterion (ABIC > 10; likelihood calculations are described in Appendix (F). Crucially, networks trained with extended intervals had less informative spatial responses compared to those trained on naturalistic developmental trajectories, as evidenced by higher decoding errors (Figure (5),, reduced importance in lesion studies (Figure 5p). weaker sRSA (Figure (5)), and less structured Isomap projections (Figure 5p). Even with grid cell inputs, these models were less likely have generated the experimental data (ABIC 10) and achieved lower spatial encoding than adult networks (Figure 5). confirming that behavioural changes beyond temporal sampling drive spatial representation development.
Finally, we compared our model's results to two control experiments that isolate different factors in spatial representation development. (i) We trained RNNs on reversed locomotion development (from adult to crawl), which immediately developed robust spatial representations (Figure A5 ac- curate decoding and strong sRSA (Figure[A6) Subsequent training on simpler movement patterns produced fewer changes in spatial tuning than developmental model. (ii) We trained networks exclusively on crawl movement, matching the total training exposure of the original model, which failed to produce spatial representations, with minimal changes in spatial tuning (Figure decod- ing accuracy, and sRSA (Figure(A6). Together, these experiments demonstrate that the developmen- tal progression is not merely about behavioural repertoire or accumulated training; rather, it is the specific trajectory of increasing movement complexity that drives spatial representation emergence.
DISCUSSION
Our work establishes a mechanistic link between locomotor development and the emergence of spatial representations in the hippocampus. Through computational classification of movement trajectories from experimental data (Wills et al. 2010; TTn et al., 2015) Muessig et al. 2013 Bassett al. 2018 Muessig et al. 2019), identify three distinct stages of locomotor development: crawl

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(median age PI3.5), walk (P16), and run (P20), plus adult locomotion. We introduce shallow RNN model of the hippocampus that when sequentially trained on movement patterns from these stages recapitulates the developmental timeline of spatial cells observed in recordings of hippocampal cells of maturing rats (Wills et al. 2010; Muessig et al., experiences shaped by locomotion are fundamental to the formation of cognitive map of space. More broadly, our results underscore how movement kinematics critically influence the emergence of spatial representations in self-supervised neural networks trained on egocentric sensory inputs.
This demonstrates that sensory
A particularly novel prediction arising from our model is that directional selectivity in the hip- pocampus emerges primarily through conjunctive place-direction coding rather than in pure head direction cells. We investigated this prediction in CAI recordings and confirmed a significant devel- opmental increase in cells classified as both place and head direction cells a pattern not previously highlighted in developmental studies. This observation aligns with previous work on adult animals across species, where approximately quarter of place cells exhibit significant directional modula- tion (Rubin et al. 2014 Acharya et al., 2016 JJrcog et al. 2019). The developmental trajectory of this conjunctive coding may reflect the increasing computational demands of navigation as locomo- tion becomes more complex.
Our controlled experiments dissociating rate of change in visual input from actual locomotion im- provements demonstrate that spatial representation emergence cannot be explained by increasing temporal gaps between sensory experiences alone. Even when RNNs trained on early-stage loco- motion patterns with extended sampling intervals (creating a prediction task comparable to the adult one), they fail to develop the same level of spatial representations observed in models trained on sequences of trajectories simulated from rat development. This result suggests the specific statistics of movement are critical for the emergence of an allocentric view of space.
Our results demonstrate that within a fixed network architecture assuming inductive biases anal- ogous to genetic programs the specific temporal structure of locomotor experience accounts for key phenomena of spatial cell maturation. This complements recent studies showing that birthdate- dependent preconfiguration interacts with snperience-dependenn refinement (Cavalieri et al. 2021 Huszár et al. 2022) and that rearing environments shape developing spatial representations (Uisaker- Janke et al. 2023; Farooq & Dragoi, 2024). Together, our findings establish locomotor statistics as a critical and previously unexamined developmental factor and inform the design of biologically- inspired navigation models.
However, several limitations warrant discussion. Our simplified visual and vestibular inputs do not fully replicate the rich multisensory environment of developing rats future work incorporating olfactory, somatosensory, and auditory modalities could provide more comprehensive insights (Wills) et al. 2014 Guthrie & Gall, 2003 Grant et al., 2012). Additionally, while we quantitatively compare our model to experimental recordings of CAI, spatial development involves complex interactions across brain regions. Finally, we simplified vestibular and grid cell systems as velocity inputs and initialisation signals, respectively. Achieving adult-like positional tuning required the RNN to be trained from scratch with grid cell injections. This highlights incomplete understanding of how different brain regions coordinate during spatial system maturation. The rapid timescale of grid cell maturation (Wills et al., 2012) hints that grid cells may rely more heavily on intrinsic circuit mechanisms possibly including attractor-like dynamics within medial entorhinal cortex (Burak) & Fiete| 2009; Couey et al., 2013), rather than purely experience-dependen learning. While this distinct developmental profile supports our treatment of grid cell input external signal, the precise mechanisms and potential interactions during this transition period could be addressed in further research.
Future work could explore several other promising directions. Examining how locomotor devel- opment interacts with environmental or sensory manipulations would extend our framework to test computational predictions against recent empirical works documenting the effect of rearing envi- ronment spatial representation emergence in the developing hippocampus (Ulsaker-Janke 2023 Farooq & Dragoi] 2024). Furthermore, investigating the developmental trajectory of border cells across locomotion stages represents another promising path for extending this work and possi- bly corroborating previous experimental findings of border cell emergence in development (Bjerknes) al., (2014). To conclude, we suggest future experimental manipulations of locomotor development could directly test our key prediction about conjunctive
al.)
cell emergence, potentially through targeted interventions during critical developmental periods.
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REFERENCES
Lavanya Acharya, Zahra M Aghajan, Cliff Vuong, Jason J Moore, and Mayank R Mchta. Causal influence of visual cues on hippocampal directional selectivity. Cell, 164(1):197-207, 2016.
Joseph Altman and Kiran Sudarshan. Postnaal development of locomotion in the lahoratory rat. Animal behaviour, 23:896-920, 1975.
Joshua P Bassett, Thomas Wills, and Francesca Cacucci. Self-organized attractor dynamics in the developing head direction circuit. Current Biology, 28(4):609-615, 2018.
Shirley A Bayer. Development of the hippocampal region in the rat i. neurogenesis examined with 3h-thymidine autoradiography. Journal of Comparative Neurology. 190(1):87-114, 1980.
Tale L Bjerknes, Edvard 1 Moser, and May-Britt Moser. Representation of geometric borders in the developing rat. Neuron, 82(1):11 2014.
Tale L Bjerknes, Rosamund F Langston, Ingvild U Kruge, Edvard I Moser, and May-Brit Moser. Coherenee among head direction cells before eye opening rat pups. Current Biology, 25(1):103- 100-0,0,000000000 2015.
Yoram Burak and lla R Fiete. Accurate path integration in continuous attractor network models of grid cells.
PLoS computational biology, 1(2)xe1000 2009.
Neil Burgess, Francesca Cacucci, Colin Lever, and John O'keefe. Characterizing multiple independent behav- ioral correlates of cell firing in freely moving animals. Hippocampus, 15(2):19--133 2005.
György Buzsáki and David Tingley. Space and time: the hippocampus as sequence generator. Trends in cognitive sciences, 22(10):853-869, 2018.
Davide Cavalieri, Alexandra Angelova, Anas Islah, Catherine Lopez, Marco Bocchio, Yannick Bollmann, Agnès Baude, and Rosa Cossart. Cal pyramidal cell diversity is rooted in the time of neurogenesis. Elife, 10:c69270, 2021.
Guifen Chen, Daniel Manson, Francesca Cacucci, and Thomas Joseph Wills. Absence of visual input results in the disruption of grid cell firing in the mouse. Current Biology, 26(77)2233-2342, 2016.
Jonathan J Coucy, Arec Witoclar, Sheng- Jia Zhang, Kang Zheng, Jing Yc, Benjamin Dunn, Rafal Czajkowski, May-Britt Moser, Edvard I Moser, Yasser Roudi, al. Recurrent inhibitory circuitry mechanism for grid formation, Nature nearoscience, 16(3):318 2013.
Christopher Cueva and Xue-Xin Wei. Emergence of grid-like representations by training recurrent neural networks to perform spatial localization. International Conference on Learning Representations, 2018.
Howard Eichenbaum. Hippocampus: cognitive processes and neural representations that underlie declarative memory. Neuron, 44(1):109 120, 2004.
Usman Farooq and George Dragoi. Experience of euclidean geometry sculpts the development and dynamies of rodent hippocampal sequential cell assemblies. Nature Communications, 15(1):8417, 2024.
Tom M George, Mehul Rastogi, William de Cothi, Claudia Clopath, Kimberly Stachenfeld, and Caswell Barry. Ratinabox, toolkit for modelling locomotion and neuronal activity continuous environments. el.ife, 13, 2024.
Carolyn J Gerrish and Jeffrey Alberts. Environmental temperature modulates onset of independent feeding: warmer is sooner. Developmental psychobiology, 2966)483-495, 1996.
James Goreet and Matt Thomson. Automated construction of cognitive maps with visual predictive coding. Nature Machine Intelligence, 6(7):820-83, 2024.
Robyn A Grant, Ben Mitchinson, and Tony Prescott. The development of whisker control in rats in relation to locomotion. Developmental psychobiology. 54(2):511-66,, 2012.
Kathleen M Guthrie and Christine Gall. Anatomic mapping of neuronal odor responses in the developing rat olfactory bulb. Journal of comparative neurology, 455(1):56 71, 2003.
Torkel Hafting, Marianne Fyhn, Sturla Molden, May-Britt Moser, and Edvard Moser. Microstructure of spatial map the entorhinal cortex. Nature, 436(7052):801 -006 2005.
Myles Hollander, Douglas A Wolfe, and Fric Chicken. Nonparametric statistical methods. John Wiley & Sons, 2013.
11

---

Published as a conference paper at ICLR 2026
Hughes. A schematic eye for the rat. Vision research, 1955)569--588 1979.
Roman Huszár, Yunchang Zhang. Heike Blockus, and György Buzsáki. Preconfigured dynamics in the hip- pocampus are guided by embryonic birthdate and rate of neurogenesis. Nature Neuroscience, 25(9):1201- 1212, 2022.
Kathryn J Jeffery and John Keefe. Learned interaction visual and idiothetic cues in the control place field orientation. Experimental brain research, 127:151-161, 1999.
Pablo E Jercog, Yashar Ahmadian, Caitlin Woodruff, Rajeev Deb-Sen, Laurence F Abbott, and Eric R Kandel. Heading direction with respect to reference point modulates place-cell activity. Nature communications 10(1):2333, 2019.
Adam Johnson and A David Redish. Neural ensembles in ca3 transicntly encode paths forward of the animal at a decision point. Journal of Neuroscience, 27(45):12176 12189, 2007.
Kenneth Kay, Jason E Chung, Mariclena Sosa, Jonathan S Schor, Mattias P Karlsson, Margaret C Larkin, Daniel F Liu, and Loren M Frank. Constant sub-second cycling between representations of possible futures in the hippocampus. Cell. 180(3):552-56, 2020.
Rosamund Langston, James A Ainge, Jonathan Coucy, Cathrin B Canto, Tale L Bjerknes, Menno P Witter, Edvard I Moser, and May-Britt Moser. Development of the spatial representation system in the rat. Science, 328(555) 1580- 2010.
Daniel Levenstein, Aleksei Ffremov, Roy Henha Eyono, Adrien Peyrache, and Blake Richards. Sequential pre- dictive learning is unifying theory for hippocampal representation and replay, bioRxiv 0024.04.28.5915288, 2024.
Colin Lever, Stephen Burton, Ali Jeewajee, John O'Keefe, Neil Burgess. Boundary vector cells the subiculum of the hippocampal formation. Journal of Neuroscience, 29(11)9771-9777 2009.
William B Levy. A computational approach to hippocampal function. In Psychology of learning and motivation, volume 23, pp. 243-305. Elsevier, 1989.
MB Lowrie and G Vrbova. Dependence of postnatal motoneurones on their targets: review and hypothesis. Trends in neurosciences, 15(3):80-84, 1992.
Xiaoliang Luo. Robert M Mok, and Bradley C Love. The inevitability and superfluousness of cell types in spatial cognition. eLife, 2024.
Laurenz Muessig, Jonas Hauser, Thomas Joseph Wills, and Francesca Cacucci. A developmental switch in place cell accuracy coincides with grid maturation. Newron, 8665).1167-117 2015.
Laurenz Muessig, Michal Lasck, Isabella Varsavsky, Francesca Cacueci, and Thomas Joseph Wills. Coor- dinated emergence of hippocampal replay and theta sequences during post-natal development. Current Biology, 2944588348440, 2019.
Robert U Muller and John L. Kubie. The firing of hippocampal place cells predicts the future position of freely moving rats. Journal of Neuroscience, 9(12):4101-411 10, 1989.
Robert U Muller, Elizabeth Bostock, Jeffrey S Taube, and John L Kubie. On the directional firing properties of hippocampal place cells. Journal of Neuroscience, 14(12):725--7511, 1994.
John O' Keefe and Dulcie H Conway. Hippocampal place units in the freely moving rat: why they fire where they fire. Experimental brain research, 31:573-590, 1978.
John O' O'eeefe and Jonathan Dostrovsky. The hippocampus as a spatial map: preliminary evidence from unit activity in the mrvely-movnng rat. Brain research, 1971.
John O' O'keefe and Lynn Nadel. The hippocampus as a cognitive map. Oxford University Press, 1978.
José Antonio Pérez-Escobar Olga Kornienko, Patrick Latuske, Laura Kohler, and Kevin Allen. Visual land- marks sharpen grid cell metric and confer context specificity to neurons of the medial entorhinal cortex. Elife, 5:c 1637, 2016.
Stefano Recanatesi, Matthew Farrell, Guillaume Lajoie, Sophie Deneve, Mattia Rigotti, and Eric Shea- Brown. Predictive learning as a network mechanism for extracting Low-dinensional latent space representations. Nature communications, 12(1))1417, 2021.
12

---

Published as a conference paper at ICLR 2026
Alon Rubin, Michael M Yartsev, and Nachum Ulanovsky. Encoding of head direction by hippocampal place cells in bats. Journal of Neuroscience, 34(3):107710800, 2014.
Francesco Savelli, D Yoganarasimha, and James J Knierim. Influence of boundary removal the spatial representations of the medial entorhinal cortex. Hippocampus, 18(12):1270-1285 2008.
Bernhard Schölkopf, Alexander Smola, and Klaus-Rober Müller. Kernel principal component analysis. In International conference artificial neural networks, pp. 383-588. Springer, 1997.
Gideon Schwarz. Estimating the dimension of a model. The annals of statistics, pp- 461-464, 1978.
Rod C Scott, Gregory Richard. Gregory Holmes, and Pierre- Lenck-Santii. Maturational dynamics of hippocampal place cells immature rats. Hippocampus, 21 (4):3447-355 2011.
William Skaggs, Bruce L McNaughton, Matthew A Wilson, and Carol A Barnes. Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences. Hippocampus, 6(2):149- 172. 1996.
Trygve Solstad, Charlotte N Boccara, Emilio Kropff, May- Britt Moser, and Edvard Moser. Representation of geometric borders the entorhinal cortex. Science, 322(5909) 1868, 2008,
Kimberly L Stachenfeld, Matthew M Botvinick, and Samuel Gershman. The hippocampus predictive map. Nature neuroscience, (0(11))16433166 2017.
Hui Min Tan, Joshua Pope Bassett, John O' O'Keefe, Francesca Cacueci, and Thomas Joseph Wills. The de- velopment of the head direction system before eye opening in the Current Biology, 25(4):479-483, 2015.
Hui Min Tan, Thomas Joseph Wills, and Francesca Cacucci. The development of spatial and memory circuits in rat. Wiley Interdisciplinary Reviews: Cognitive Science, 8(3)xe122, 2017.
Jeffrey S Taube, Robert U Muller, and James B Ranck. Head-direction cells recorded from the postsubiculum in freely moving rats. i. description and quantitative analysis. Journal of Neuroxcience, 10(2):4004355 1990.
Edward C Tolman. Cognitive maps in rats and men. Psychological review, 55(4))189, 1948.
Balazs B Ujfalussy and Gergö Orbán. Sampling motion trajectories during hippocampal theta sequences. Elife, 11:e74088, 2022.
Ingvild Ulsaker-Jane, Torgeir Waaga, Tanja Waaga, Edvard Moser, and May- Moser. Grid cells in rats deprived of geometric experience during development. Proceedings of the National Academy of Sciences, 120(41):e23) 10820120, 2023.
Benigno Uria, Borja Ibarz, Andrea Banino, Vinicius Zambaldi, Dharshan Kumaran, Demis Hassabis, Caswell Barry, and Charles Blundell. The spatial memory pipeline: a model of egocentric to allocentric understanding in mammalian brains. BioRxiv 2020. 11.17.371111 2022.
Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 911),, 2008.
Torgeir Waaga, Haggai Agmon, Valentin Normand, Anne Nagelhus, Richard Gardner, May-Britt Moser, Edvard Moser, and Yoram Burak. Grid-cell modules remain coordinated when neural activity is dissociated from external sensory cues. Newron, 110(1))114331888 2022.
James CR Whittington, Timothy Muller, Shirley Mark, Guifen Chen, Caswell Barry, Neil Burgess, and Timothy EJ Behrens. The tolman-eichenbauu machine: unifying space and relational memory through generalization in the hippocampal formation. Cell, 1299 2020.
Frank Wilcoxon. Individual comparisons by ranking methods. Breakthroughs statistics: Methodology and distribution, pp. 196-202. Springer, 1992.
Thomas Wills, Caswell Barry, and Francesca Cacucci. The abrupt development of adult-like grid cell firing in the medial entorhinal cortex. Frontiers in neural circuits, 6:21, 2012.
Thomas Wills, Laurenz Muessig, and Francesca Cacucci. The development of spatial behaviour and the hippocampal neural representation of space. Philosophical Transactions of the Royal Society B: Biological Sciences, 369( (335):20130400) 2014.
Tom J Wills, Francesca Cacucci, Neil Burgess, and John O' Keefe. Development of hippocampal cognitive map in preweanling rats. science, 528(5985):1533-1556, 2010.
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APPENDIX
A LLM USAGE STATEMENT
Large language models (LLMs) were employed exclusively to improve the quality of written En- glish and for stylistic refinement, with attentive supervision by the authors. No scientific content, experimental design, data analysis, or any intellectual contribution were generated through LLMs, nor did such assistance influence any reported results.
B EXPERIMENTAL DATASETS
In our work, we aggregated experimental data from multiple previous studies (Wills et al. 2010 Tan et al.
et al. 2013 Bassett et al.) 2018 Muessig et al. 2019), where rats explored familiar square open field environment. All data was included in the locomotor development clustering analysis of Section while a subset - with recordings from hippocampal area CA1 (Wills et al. 2010: Muessig al. 2015 2019) was used for comparison with results from modelling of Section 3.T Experiments were performed on PI to P25 (and adults) Lister Hooded rats (Figure A1]) in familiar square-walled (62.5 sides, 50 cm high) light-gray wooden box, placed black, square platform while rats searched for drops of soya-based infant formula milk randomly scattered on the floor of the environment. Behavioural tests began different ages for different rats, hence experience of the arena was dissociated from age, For specific information about experimental settings, we redirect the reader to the relevant publications.
C LOCOMOTOR DEVELOPMENT CLUSTERING ANALYSIS
C.I METRICS EXTRACTION
In the clustering analysis, we included only experimental trials lasting 300 seconds or more. Trials exceeding 900 seconds were truncated to this duration. For rats ages P11 to P25, maximum of 60 trials were selected at random. We estimated metrics for each rat at each age. Probability density functions of speed were computed with histograms of 50 bins in the range 0 to 0.15 m/s. Similarly, probability density functions of rotational speed were generated with 50 bins spanning -360 to 360 %/s. Transition probabilities that encodes the likelihood of moving between spatial bins within the environment. The arena was divided into 10x grid (Br 100 bins). TM E Br xBr initialised to matrix of zeros and - for each position the rat visited p: (Iz, Mt), belonging to bin b - it was updated as:
calculated by constructing a transition matrix TM
(M........) (M....._))) (0.5) "²", k=1- K
where K was set to 7 seconds and indicates the threshold over which accumulating the (discounted) transitions, and t > K. The value in TM corresponding to the last bin visited (k 1) before reaching b, was incremented by 1, while the entry of T'M corresponding to the bin before that (k 2) was incremented by around 0.71, and on. At the end of trial, TM normalized by the occupancy matrix which counted the number of times each bin was visited and smoothed using a Gaussian filter (a=1 bin).
C.2 CLUSTERING POSTNATAL LOCOMOTOR DEVELOPMENT
Clustering was used to group developmental stages with similar locomotor patterns based on the metries introduced in Section C.I specifically, probability density functions of speed and rotational speed, as well as transition matrices. We quantified locomotor differences using Jensen-Shannel (JS) distances between the distributions and between the transition matrices. The JS distance be- tween two vectors and q was defined as:
KL. (p/lm))) KL (qllm)
(5) where m is the pointwise mean of p and q, and KL is the Kullback-Leibh divergence. When comparing transition matrices, JS distances were calculated over the first dimension and averaged over the second.
JS(p. q)
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b
Probability density
20-
15-
Number of
10-
20
19
20
23
adult
0.00
0:05
0.10
0.20
0.25
0.30
0.35
Age
Speed (m/s)
d
Probablity clensity
0.64
Desination bin
20-
40-
00.4
0.24
0.0
//2
a/2
100-
40
80
Rotational speed (rad/s)
Starting bin
Figure Al: Simulation of new trajectories emulating experimental data. Histogram of ages of rats used in the clustering analysis. Adult rats are aged 3-6 months. b Speed probability density functions of trajectories simulated for the grid search. Solid lines indicate curves from selected parameters. e Rotational speed probability density functions of trajectories simulated for the grid search. Solid lines indicate curves from selected parameters. d Transition matrix estimated from adult trajectories. Warmer colours indicate higher probability to move from the starting bin to the destination bin.
More formally, given a sample E R where R is the set of rats all ages, probability density function of speed p!r) of rotational speed p(r), and transition matrix (r) were calculated. Then, these metrics were compared to all other rats r' E R fr & r'} using the JS distance in order construct a distance matrix
((Ps) JS (PS"), p()")
((Pw) (1.rr)
JSS (p(r), P!!))
(6)
Σ is (M))) TM (.b))
Br
(1,))
be
Distances were normalised and aggregated into a single distance matrix D E IR x| as
(
D
Σ
max D(X)
XE(Ps.P.,TM
radial basis function kernel matrix K was derived from D as
Kirry exp (-D(rrr)
where fr.r') E R. Kernel principal component analysis (Schölkopf et al. 1997) was applied to K and the first 15 principal components were extracted. We estimated clusters from principal components by fitting Gaussian mixture (GM) model. To determine the optimal number of clusters, Bayesian information criterion (BIC) was minimized while varying the number of components (from 1 to 7) used for the GM model as
min BIC min
(-2 I, |R] + d(n) In((
#E1.77 1.71
11
(9)
d(n) n 1)
2
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Table A1: RatInABox agent parameters grid search
Parameter
Description
Range
dv [m/s]
Scale parameter and mode of speed Rayleigh distribution.
logspace (-5.1, -3.3, 11, base=2)
Tv [s]
Timescale over which speed decorrelates.
linspace (0.7, 2, 3)
Ow [rad/s]
Std of rotational velocity Normal distribution.
linspace (m/6, 6. 6.5m/7, 7)
thigmo
Bias towards following walls.
[0.4, 0.5, 0.6]
Table A2: RatInABox selected parameters
Locomotion stage
Oy [m/s]
[s]
ow [rad/s]
Thigmo
crawl walk
0.03794
2
m/4
0.6
0.09087
1.35
2x/3
0.6
rum adult
0.07081
1.35
2T/3
0.6
0.08021
1.35
2.5m/3
0.4
where In, is the per-sample average log-likelihood when setting components, and d is the number of free parameters. Three locomotion clusters were found minimize the BIC, which we designate crawl, walk, and run. For this analysis, the Python software libraries Scikit-learn, Scipy, and Pandas were used.
C.3 RAT TRAJECTORIES SIMULATION
Rat trajectories were simulated using RatInABox (George et al. 2024). an open-source library which provides position, head direction, velocity, and rotational speed information (among other data) at temporal resolution set by the user. The software implements two independent Ornstein- Uhlenbeck processes for speed and rotational speed. The rat moved in a squared arena with sides of length 62.5 em. Data was collected at 50 Hz for the clustering analysis of Section (matching experimental setup), and at Hz for modelling results of Section
Ornstein-Uhlenbeeck
To simulate locomotor development, grid search over key RatInABox parameters was performed (Table Al) and the combinations optimally matching locomotion stages selected. An addi- tional parameter set was picked to match adult locomotion of rats aged 3-6 months. In more detail, estimated probability density functions of speed, rotational speed,
for each set of parameters, and a transition matrix (Figure ATD--)). For each locomotion stage, the set parameters yielding simulated trajectories closest (in terms of JS distance) to experimental data selected (Table A2
C.4 VIRTUAL REALITY
To recreate the visual environment, Blender was used to design virtual arena that mimicked the experimental conditions. The virtual box featured brick-like textures on the walls, a rhomboid- patterned floor, and distinct visual landmarks attached to three of the walls. A camera was positioned 3.5 cm above the ground to collect visual frames according to the agent's 's location and head direction. The panoramic camera
configured to emulate rat's vision with horizontal field of view of 240", a vertical field of view of 120", and a resolution 32 by 16 pixels, capturing black-and -nitte images. Visual frames were rendered using the Blender Python API.
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Table A3: RNNs training parameters
Parameter
crawl
walk
run
adult
adult with grid cells
Hidden units N₂
500
500
500
500
500
Initialisation
random
crawl
walk
run
random
Optimizer
RMSprop
RMSprop
RMSprop
R.Sprrp
RMSprop
Epochs
1500
1500
1500
1500
1500
Learning rate
5e-5
5e-5
5e-5
5e-5
1e-4
BPTT steps
9
9
9
9
Number of grid cells
25
Grid cells scale
0.2, 0.4, 0.6
Grid cells orientation
0.1
crawl
walk
adult
adult grld cells
0.124
0.1104
0.150
Loss
0.15-
Valditicn
0.105-
0.105-
0.11
0.125
0.10+
0.100-
0.100-
0.100
0
1500
1500
0
1600
1500
1600
Epoch
Epoch
Epoch
Epoch
Epoch
Figure A2: Training and validation loss trends for models: a crawl, b walk, e adult, and adult with grid cell input.
D RECURRENT NEURAL NETWORK
single-layer recurrent neural network (RNN) was trained predict upcoming visual stimuli from concurrent visual and vestibular inputs. Modelling was performed using Python and Py PyTorch, machine with single CPU and single GPU (Nvidia RTX 4000 Ada Gen with 20 GB).
Training Table A3 presents the training parameters used for each network. Biologically pllogicallly-lluuibble parameters that allow the network to reach convergence (validation loss plateau, Figure A2) were chosen without any parameter optimization. For the "rate of change" models introduced in Section 3.3 parameters were the same as for our original models, except for the inter-frame gap which was increased range from
seconds.
RNN with grid cell input The hidden layer of the RNN with grid cell input trained on the adult trajectories was initialised to a linear projection of population of 25 grid cells G, 25 the start of trajectory and consequently every 90 timesteps. Given the wave orientation 0 0.1, randomly sampled grid-scale X; E {0.2, 0.4, 0.6) and phase offset di ~ 210, 2m), the firing rate of cell at time t was defined as
$1. ))
a max 0,
Σ
2n
Pr Cuta
(10)
A
(3.5/3327/33
where p: = (Irr is the position of the agent at time t and is the unit vector pointing toward direction 0.
Position and head direction decoding Position and head direction of the agent decoded from the activity of hidden units. An ordinary least squared linear regressor was trained on half of the validation data and decoding errors were reported for the other half of the validation data. Given
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predicted position P. and head direction 8,, decoding errors calculated
Ap: pp: Pr/2
(11)
A0; 00, 0,1
Lesion of hidden units Lesions were performed to investigate the functionality of hidden units with high spatial tuning with respect to solving the predictive task. Given multiple trained models to compare, we call the set of units classified as place or head direction (HD) cells Sm classification method described in Section random from Sm. keeping |S minm |Sml- Lesioning was performed by setting to zero selected units' input-to-hidden weights Wa and modden-to-hidder weights Wa. Finally, validation loss was calculated. When Sml > I the process was repeated 100 times.
for each model m. Then, subset S of units were lesioned
Spatial representational similarity analysis (sRSA) and Isomap sRSA was used to quantify correspondence between neural and spatial distances. Given hidden layer's activity A, E R and position P. at time / validation sequences length T (720 in our dataset), distances were calculated
A Av
D
[Ai2 |Ael2
(12)
D, = Ip= Prlz
at all pairs of timesteps f and t', hence {DA. Dp} E RT. Finally, sRSA was defined as the Spearman rank correlation coefficient between the two distances p(DA, Dp). Isomap was used to obtain two-dimensional visualisation of neural manifolds. 7.500 timesteps were randomly sampled from
the validation set, the cosine distance was used, and the number neighbours was set to 100. Each point in the Isomap visualisations was coloured by the 11-norm of its corresponding position |Pr/1-
E SPATIAL TUNING ANALYSIS
Once trained, the RNN hidden units were analysed to gauge their spatial tuning. For each locomotion stage, 9 sequences of which were also part of the validation set) of 720 frames were used to measure spatial representations. We call (((k) and p(k) the rate map and polar map of unit k, respectively. A rate map shows how active a unit is different locations within the environment, and their calculation is introduced in Section A polar map shows how active a unit is function of the agent's is head direction. The head direction space is divided into Bg 60 bins and p(k) R Be was calculated as
(k)) E. E.A, A.A 1o.. Σ, Lo.
E B
(13)
Where (A)) is the hidden layer's activity (or value) of unit & and 0 is the agent's 's head direction time l. Numerator and denominator were smoothed using Gaussian filter with standard deviation of bins.
To measure the positional selectivity of unit, we calculated the spatial information (Skaggs et al. 1996) of the rate map corresponding to unit as
Positional selectivity
(k))
R((k)
Oi
$I
loga
||0||
R(k)
R((k)
i.j
N Σn R O
(14)
110111
O2 Σ 1. EBin(a.))
1,j E (1..... (1,..,,)
where E B. BB, B, is the occupancy matrix, R(R) is the average activity of the unit normalized by the agent's occupancy, and Pt (Ire y1) is the position of the agent time t.
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Published as a conference paper at ICLR 2026
Directional selectivity To quantify directional selectivity, the resultant vector length (RVL) of the polar map corresponding to unit was calculated as
1r |
RVL
Ei p(k)
(h) = Σ exp
2T
p(h)
(15)
2Bo
i {l...., Bo}
where r(() is the resultant vector, and j is the imaginary unit. Spatial information of p(k) was
calculated
P k) P(k)
p(k)
$I
Σ
log2
......,b))
(16)
DI
p(k)
where D E BB is the directional occupancy vector, and ((k) is the average activity of the unit normalized by the agent's 's directional occupancy.
Spatial metries min-max normalisation We applied min-max normalisation to spatial metrics SI,. RVL, and St, for all model-experiment comparisons. This procedure followed the standard approach: subtracting the minimum and dividing by the range (difference between maximum and minimum values). Minimum and maximum values were determined across all developmental stages. For experimental data normalisation, minimum values could also be derived from the "crawl" stage of simulated data, since no hippocampal recordings were available from animals at this early loco- motor stage.
E.1 MODEL-EXPPRIIMENTAL CORRESPONDENCE VALIDATION
We validated model-experiment correspondence by comparing tween model and experimental distributions. For metric m E {SI,,, SI,, RVL). we estimated prob- ability distributions of experimental data p and modelled data q at each locomotion stage by calculating a histogram with 50 bins, excluding outliers (defined as being outside 1.5 times the interquartile range). We caleulated the JS between these distributions as:
(JS) distances be-
JS (m)
JS(p! (m)) (m))
(17)
CE (waik,,un,adull)
We then compared these distances the distributions of distances obtained by shuffling metrics from modelled units 10,000 times.
Table A4: p-values for one-sided pairwise Wilcoxon rank-sum tests (Wilcoxon 1992) with Honjamini-Hochheee correction (Hollander et al. 2013) testing for increasing differences of Fig- u
Measure
Condition
v-vuuue walk run
p-value walk adult
p-value walk + adult with grid cells
SIr
Model
0.0055
x 10-10
1.1 x 10-5
SIr
Experiment
4.8 10 16
2 x 10
SId
Model
0.0013
9.3 10-10
1.3 10-7
SId
Experiment
9.6 x 10-7
2 x 10-16
RVL
Model
0.0016
2.5 10-9
2.5 x 10-7
RVL
Experiment
8.6 10-10
x 10-16
Perc. place cells
Experiment
7.5 x 10-5
0.0016
Perc. HD cells
Experiment
0.037
0,450
Perc. place HD cells
Experiment
0.017
0.337
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Published as a conference paper at ICLR 2026
F COMPARISON WITH ALTERNATIVE MODEL
F.1 SPATIAL CELL DEVELOPMENT IN RATE OF CHANGE MODEL
The rate of change model was trained crawl trajectories with extended inter-frame intervals (1-3 seconds) to control for predictive demand in the emergence of spatial representations. Figure A5 shows the rate of change model did not reach same levels of spatial tuning when compared to the original model staying well below experimental values.
F.2 LIKELIHOOD ANALYSIS
For metric m E (SI, SIa. RVL), we estimated probability distributions of experimental data (m) explained in Section E. with 100 bins, applying softmax deal with zero-probability values. Then, we calculated the log- oogliikeihhod of generating experimental data with given model as:
(m) model
Σ
Σ
In (p(m)
(m) 1x)
(18)
oE [walk, aduit) I. R]
where 1g a vector of zeros, with one in the position corresponding to the bin in which the value of metric m (for unit k) belongs to, and K is the number of hidden units. To compare models, we computed the BIC as:
mlCre 2 LL (m)
model + danotel In(K)
(19)
where d is the degrees of freedom, which we set to for the rate of change model (which corresponds to the parameter specifying the gap), and to 4 for the original model (which corresponds to the number of parameters for trajectory simulation).
20

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Published as a conference paper at ICLR 2026
Tuning
modelled
Development
positional
adult with
Perc. place cells
S0
0.2
Less hidden units
directional
grid cells
More hidden units
both
experimental
80-
Train longer
Smaller input
0.3
Bigger input
c1000
nin
Pere. place n HD cells
20
0.30
Perc. HD cells
0.15
20-
0.10
RVL
0.25
104
0.15-
craw]
walk
walk
wall
adull
crawl
walk
adult
Figure A3: The maturation of spatial tuning does not deviate from the original results (indi- cated as "development") when decreasing/nncreasing hidden layer size, training longer, and feeding smaller/bigger images. Model data is shown in solid lines with transparency based on the changed parameter. Experimental data shown with dotted lines. Diamonds indicate adult model trained with grid cell input. Percentage of RNN units (solid lines) and percentage (Mean=SEM) of hip- pocampal neurons (dotted line) classified as place cells. b Min-max normalized spatial information SI, SEM) for rate maps of RNN units (solid) and hippocampal neurons (dotted). Percent- age of RNN units (solid) and percentage (Mean=SEM) of hippocampal neurons (dotted) classified head direction (HD) cells. d Min-max normalised spatial information SIş (Mean+-EEM) for po- lar maps of RNN units (solid) and hippocampal neurons (dotted). Min-max normalised resultant vector length (RVL, - for polar maps of RNN units (solid) and hippocampal neurons (dotted). Percentage of RNN units (solid) and percentage (Mean=SEM) of hippocampal neurons classified both place and HD cells (dotted).
Development Less hidden units
More hidden units
Train longer
Smaller input
Bigger input
des err (cm)
dec err (deg)
0.6-
sRSA
0.4-
30.0+
0.2
walk
Figure A4: Decoding emros for position and head direction from hidden-layer activity decrease and correlation between agent's position and hidden state activity increases in the original model (development) when performing a parameter sweep. Transparency indicates a change in parameters. Diamonds indicate adult model trained with grid cell input. a Position decoding erros (Ee) and b head direction decoding errors (mean=SEM) calculated by linear regression on hidden units (50-50 train-tes split on validation data). e Spatial representational similarity analysis SEM) quantifying correlation between agent position and hidden unit activity.
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Published as a conference paper at ICLR 2026
Tuning
modelled
Perc. place cells
80-
positional
adult with
Development
directional
grid cells
ST.
Rate of change
both
experimental
204
Crawl with more data
0.14
Reverse training
craed
Pere. place n HD cells
20
0.35
0.16
Perc. cells
10-
00010
RVT.
0.225
10-
0.05
0.15
craw]
walk
sdhlt
Walk
zun
adult
0m011
Walk
adull
0m01
walk
adult
Figure A5: Spatial tuning development of alternative hypotheses controls deviates from original model results (development) and does mimic experimental data (dotted lines). Model data is shown in solid lines with transparency indicating the control runs. Diamonds indicate adult model trained with grid cell input. a Percentage of RNN units (solid lines) and percentage (Mean::SEM) of hippocampal neurons (dotted line) classified as place cells. b Min-max normalized spatial in- formation SI, (EMa for rate maps RNN units (solid) and hippocampal neurons (dotted). Percentage of RNN units (solid) and percentage (mean of hippocampal neurons (dotted) classified as head direction (HD) cells. d Min-max normalised spatial information SIş (Mcan=SEM) for polar maps of RNN units (solid) and hippocampal neurons (dotted). Min-max normalised re- sultant vector length (RVL, + for polar maps of RNN units (solid) and hippocampal neurons (dotted). Percentage of RNN units (solid) and percentage (Mean+ of hippocampal neurons classified as both place and HD cells (dotted).
Development Rate of change
Crawl with more data
Reverse training
Pos dee exr (cm)
err (deg)
364
sRSA
0.4
44
30
0.2
crawl
walk
Figure A6: Decoding errors for position and head direction from hidden-layer activity and correla- tion between agent's position and hidden state activity remain stable in control runs. Original model results are indicated as "development". Transparency indicates control runs. Diamonds indicate adult model trained with grid cell input. a Position decoding errors (MMa and head direc- tion decoding errors SEM) calculated by linear regression on hidden units (50-50 train-test split on validation data). Spatial representational similarity analysis (mean SEMM) quantifying correlation between agent position and hidden unit activity.
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Published as a conference paper at ICLR 2026
modelled
1.04
1.04
1.04
experimental
Stg nomm.
RVL nomm.
Tuning
0.54
0.5
0.5
positional
S1,
directional
0.0
0.04
0.04 13310
crawl
walk
aduit sdult cellss
adult adult grd cells
walk
aduli aduit cells
Figure A7: Population-lever spatial metrics of modelled units and experimentally recorded ensem- bles of cells (dimmed colours), grouped by the rat's locomotion stage at the age of recording. Min-max normalized spatial information SI, distribution for rate maps of RNN units (solid) and hippocampal neurons (dimmed). Min-max normalised spatial information SI, distribution for polar maps of RNN units (solid) and hippocampal neurons (dimmed). Min-max normalised resul- tant vector length (RVL) distribution for polar maps of RNN units (solid) and hippocampal neurons (dimmed).
adult with grid cells
Polar rale map inter-trial correlation
Rate inter-trial correlation
Tuning
positional
directional
walk
adult
walk
adult
Figure A8: Inter-trial stability: Pearson correlation between rate maps and b polar rate maps, computed on the first and second halves of the held-out validation trajectories across developmental stages. Diamonds indicate adult model trained with grid cell input.
adult with grid cells
Perc. pure HD cells
of pure HD cells
0.254
RVL of pure HD celly
0.40-
SIg norn.
3-
0.20+
0.35+
0.15-
adhli
walk
Figure A9: Pure directional selectivity emerges at the "walk" stage and remains stable thereafter. Diamonds indicate adult model trained with grid cell input. Non-significance was determined by one-sided pairwise Wilcoxon rank-sum tests (Wilcoxon, 1992) with Benjamini-Hochherg correction (Hollander et al. 2013) testing for increasing differences. Percentage of RNN units classified as pure head direction (HD) cells i.c., meeting only the criteria for directional selectivity. b Min- max normalised spatial information SI (Sean for polar maps of RNN units classified as pure HD cells. Min-mat max normalised resultant vector length (RVL, mean SEM) for polar maps of RNN units classified as pure HD cells.
23


Reviews:
[{"review_id": "GOKM3LjJH7", "reviewer": "Reviewer_PPg2", "summary": "This paper investigates how developmental changes in locomotion shape the emergence of spatial representations in the hippocampus. The authors analyzed experimental data on rat locomotor behavior, identified three developmental stages (crawl, walk, and run) via clustering. Then, the paper trained RNNs to predict visual input from visual and vestibular inputs using simulated trajectories that matched those observed in experimental data. They showed that the model exhibited the emergence of spatial tuned units in orders resembling the biological development timelines. The paper also provided a novel prediction where directional selectivity emerges through conjunctive place-direction coding and confirmed this in experimental data.", "questions": "1. In the model, grid cell input is introduced as an external input provided only in the adult stage. Is it known that grid cells develop independently from the spatially tuned cells studied here? Could their development process be somewhat interactive? \n2. How does the model and its learned representation generalize to novel maps or out-of-distribution visual conditions? Could the model predict developmental abnormalities if exposed to impoverished or atypical sensory inputs?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 1, "strengths": "1. Originality: provided a novel mechanistic model connecting locomotion experience to hippocampal spatial neuron development, and provided novel predictions that are confirmed with experimental data. \n2. Quality: well-controlled study with good ablation and control experiments (e.g., reversed developmental order). \n3. Clarity: very clearly written. The figures are also informative and clear. \n4. Significance: makes a substantive contribution to understanding how sensory-motor inputs shape spatial neuron formations in the hippocampus. It also opens an interesting direction for embodied AI, raising the question of how incorporating embodied inputs and developmental principles could improve the way AI systems learn useful internal representations for spatial tasks.", "weaknesses": "1. Biological motivation for the model architecture: The paper could provide a clearer biological rationale for the modeling choice. Why should we expect a single-layer RNN trained to reconstruct high-dimensional visual input to correspond to the hippocampal circuitry? Is there recurrent interactions between the spatially tuned neurons modeled in the paper? The author should better justify this correspondence. \n2. Task: It would be useful to discuss why the paper chooses to predict the full high-dimensional visual input. Right now, it reads a bit arbitrary as an objective for studying spatially tuned neurons. Is the task choice critical for the results in the paper? Could an alternative task that is lower-dimensional or spatially-relevant (e.g., next location prediction?) yield similar results? Clarifying this could strengthen the argument that the finding reflects some general principle rather than task-specific results. \n3. The context of prior computational work could be presented more clearly. The authors could provide a systematic comparison explaining what phenomena previous models have captured, what gaps remain, and how this work extends or differentiates itself.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tZostwm2PI", "reviewer": "Reviewer_Wj3x", "summary": "This paper investigates the relationship between locomotor development and the emergence of spatial representations in the hippocampus. The authors employ a computational approach, analyzing rat locomotion data to define developmental stages (crawl, walk, run, adult) and training a recurrent neural network (RNN) model to predict visual input from previous movement states. The core hypothesis is that changes in locomotor development drive the sequential emergence of spatial tuning properties in the hippocampus.", "questions": "Could the authors provide insight into why their current framework might be insufficient for this emergence, especially when compared to other models, such as the Tolman-Eichenbaum Machine (TEM), which have successfully produced grid-like representations? Specifically, what fundamental architectural or mechanistic differences in TEM, if incorporated into the authors' model, might enable the emergence of grid cells?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "The most notable strength of this work lies in its data-driven finding regarding the emergence of directional tuning. The analysis suggests that hippocampal directional tuning during development tends to arise from cells that initially encode location. This specific observation, derived from their data analysis, offers a potentially valuable insight into the developmental trajectory of hippocampal function.", "weaknesses": "My assessment of this paper reveals fundamental weaknesses that severely undermine its conclusions and overall scientific contribution.\n\n1. Overly Strong and Unjustified Core Hypothesis: The central assumption that developmental changes in brain spatial representations are solely determined by locomotor patterns is a drastic oversimplification. This hypothesis completely neglects the crucial role of genetically programmed neural system development, which is an undeniable and fundamental aspect of brain maturation. To validate such a strong, almost certainly unreasonable assumption, the authors would need exceptionally compelling evidence, which is conspicuously absent.\n\n2. Insufficient Evidence to Support the Core Hypothesis: The model, at best, can only capture some features of CA1 representational development. Crucially, the authors themselves acknowledge that their model fails to spontaneously generate grid cells, and struggles to adequately characterize the developmental features of head-direction (HD) cells in the medial entorhinal cortex (MEC) and other relevant brain regions. This demonstrates that their core hypothesis lacks the explanatory power to account for the full complexity of the navigation system's development. If locomotor patterns were the primary driver, the model should reproduce these key elements more robustly.\n\n3. Biological Implausibility of Model Choice for CA1: The authors' argument that their RNN model is specifically modeling CA1 is highly problematic and fundamentally flawed.\n(1) **Lack of Biological Constraints**: The RNN model is trained without any specific biological constraints pertinent to CA1. Without articulating why brain regions other than CA1 cannot be similarly modeled as RNNs, this claim is entirely unsubstantiated.\n(2) **Contradiction with CA1 Connectivity**: CA1 is widely recognized for having very sparse recurrent connections; indeed, it is primarily considered a feedforward processing stage from CA3 and entorhinal cortex. Therefore, using a recurrent neural network (RNN), whose defining characteristic is its rich internal recurrence, to model a region known for its lack of such connections is biologically inconsistent and fundamentally unsound. This choice of model severely undermines any mechanistic insights the paper claims to offer regarding CA1 function.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EQBGOqDTyD", "reviewer": "Reviewer_HkDn", "summary": "This paper examines how motor development of rodents may shape the spatial representations found in the brain. By analyzing real rodent behavior during development, the authors show that their trajectories can be broadly classified into 3 stages. The authors then train RNNs on predicting the next sensory and motor state, given the current sensory and motor state. They use sensory inputs that are inspired by real experiments, as well as real rodent trajectories. Analyzing the learned representations from the RNN, the authors find that place and head direction cells emerge with training, mirroring what is found in experiments. The authors also show that the model predicts a greater increase in direction selective place cells, which they find evidence for when re-analyzing existing neurophysiology data.", "questions": "1. Why do the authors choose the loss function they do (one time-step ahead prediction)? \n\n2. How robust are the place and HD cells the model produces? \n\n3. What are the population level properties of the RNN units (i.e., distribution of SI, distribution of RVL)? \n\n4. How does border score emerge with training?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. This paper is - to my knowledge - the first computational work to show how motor development impacts spatial representations. This is an important point, and one that will (and should) change how the field thinks about place and head direction cells. \n\n2. The paper is well written and easy to follow. \n\n3. The experiments are well done, clearly explained, and aligned with experimental evidence. \n\n4. The use of the RNN model to make a prediction about spatial development that is then found in re-analyzed neurophysiological data is great and further strengthens the case for using RNNs to study this.", "weaknesses": "I identified no major weaknesses of this submission. However, there are a few things that I think should be addressed to make the paper stronger:\n\n1. The authors train the RNN to do one time-step ahead prediction of both motor and sensory state. For path integration, this kind of one step prediction makes sense to me. E.g. when it's dark and I walk from my bed to my bathroom, I'm constantly updating where I think I am. But it was unclear to me: a) why this kind of one time-step prediction would be done in the hippocampus with sensory information b) why this prediction should be one time-step ahead (as opposed to other time-scales, or multiple time-scales). Adding more discussion on this to provide better motivation would be helpful I think. \n\n2. The authors correctly say that their approach for identifying place and head direction cells - based on a spatial information metric - is standard. However, there is a growing understanding in the systems neuroscience field that a fixed threshold may not be the best way to classify. Many labs now use measures of cross-validation, robustness, consistency, etc., as well as compare to shuffled controls, to identify place cells. I understand the authors are primarily comparing to older experiments where this fixed thresholding approach was used, but it would be helpful I think to include some quantification of robustness of the place and head direction cells. If you split the trial in half and compute the SI for each half, is it similar? \n\n3. Related to the point above, it would be helpful to have population summary level plots of the distribution of spatial information and RVL, in addition to having individual units plotted (Fig. 3). This would help the readers understand the extent to which the RNN model develops these kinds of properties. \n\n4. Border cells seem to emerge in both the rodent recordings and the RNN model. Does the border score (and percent border cells) also increase in a similar way between RNN and neurophsyiological data? If so, this could be a further strengthening of the results (and could potentially be a supplemental figure). If no, then this suggests something different between the RNN and rodent development that would be interesting to comment on. \n\n5. Finally, Cueva and Wei (2019) looked at how an RNN trained to perform path integration develops spatial representations. They find that HD units emerge first, then border units, then grid units. This is in rough agreement with actual experiments. While Cueva and Wei (2019) did not consider how properties of trajectories the RNN follows impacts the development (as the authors of this submission do), I do think it is reasonable for the authors to acknowledge this prior work and its attempt to study development of spatial representation through RNNs.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NKI3Pmgw5z", "reviewer": "Reviewer_Mf5d", "summary": "This paper presents a compelling computational account of how locomotor\ndevelopment shapes hippocampal spatial representations. The authors identify\ndistinct developmental locomotion stages through clustering analysis, then show\nthat RNNs trained sequentially on these movement patterns recapitulate the\nbiological timeline of spatial cell emergence. Notably, the model predicts\ndevelopmental increases in conjunctive place-direction cells, which the authors\nconfirm in experimental data.", "questions": "1) Have you considered testing the model's predictions about conjunctive coding emergence through targeted experimental manipulations of locomotor development?\n2) Could the framework extend to other sensory modalities or species with different developmental timelines?", "limitations": "", "rating": 10, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "* Rigorous methodology with excellent reproducibility\n* Novel mechanistic insight linking embodied experience to neural development\n* Strong experimental validation across multiple datasets\n* Comprehensive controls ruling out alternative explanations\n* Makes and validates testable predictions", "weaknesses": "## Minor Issues\n* Missing ICLR requirement: The paper lacks the required statement about LLM usage in manuscript preparation. Please add this declaration.\n* Grid cell specification: The grid cell input for the adult model (Eq. 2, Table 3) could be better justified. Please clarify: (a) what the scale values (0.2, 0.4, 0.6) represent in physical units, (b) how these relate to biological grid scales (given that they are usually increased from one scale to the next by a factor of something close to √{2}, and (c) rationale for the specific orientation value of 0.1 (although I guess this is due to the grid cell orientation relative to walls).\n* Statistical reporting: Consider reporting exact p-values rather than just significance markers, particularly for borderline cases (e.g., Figure 4c,g \"ns\").\n\n## Minor clarifications:\n* Explain the min-max normalization approach more explicitly when first introduced\n* In Figure 1f, \"rat's eye view\" should clarify this is simulated panoramic camera perspective (or if this is not a panoramic camera model, which camera model was used)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Oral)