Localist Topographic Expert Routing: A Barrel Cortex-Inspired Modular Network for Sensorimotor Processing
Authors: Tianfang Zhu
Dongli Hu
Jiandong Zhou
Kai Du
Anan LI
Venue: NeurIPS 2025 poster ()
Keywords: barrel cortex
expert system
biologically constrained model
brain-inspired intelligence
tactile sensation
Primary area: neuroscience_and_cognitive_science
TL;DR: Investigating localist expert systems analogous to MoE architectures in rodent barrel cortex.

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

Localist Topographic Expert Routing: A Barrel Cortex-Inspired Modular Network for Sensorimotor Processing
Tianfang Zhu', Dongli Jiandong Zhou', Kai Du² Anan Anan Li
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology
PPsychological and cognitive sciences, Tsinghua University School of Biomedical Engineering, Hainan University
jiandongzhou,aalll @ @hust.edu.cn
"kai_ du tsinghua.cdu.cn
Abstract
Biological sensorimotor systems process information through spatially organized, functionally specialized modules. A canonical example is the rodent barrel cortex, in which each vibrissa (whisker) projects to dedicated cortical column, forming precise somatotopic map. This anatomical organization stands in stark contrast to the architectures of most artificial neural networks, which typically monolithic or rely on expert-isolated mixture-of-expert (MoE) mechanisms. this work, we introduce brain-inspired modular architecture that treats the barrel cortex bi- ologically constrained instantiation an expert system. Each module (or "expert") corresponds cortical column composed of multiple neuron subtypes spanning vertical cortical layers. Sensory signals are routed exclusively to their correspond- ing columns, with inter-column communication restricted to local neighbors via sparse gating mechanism. Despite these anatomical constraints, our model achieves competitive, state-of-the-art performance on challenging 3D tactile object classifi- cation benchmarks. Columnar parameter sharing provides inherent regularization, enabling 97% parameter reduction with improved training stability. Notably, con- strained localist routing suppresses inter-modee activity correlations, mirroring the barrel cortex's 's lateral inhibition for sensory differentiation, while suggesting MoE's potential to reduce expert redundancy through collaborative constraints. These results suggest how cortical principles of localist expert routing and topographic organization could potentially be translated into machine learning architectures. Code available at ltthhttt:/// bttaps://github http:////i.ttaa.coooo/////////// Mwwreeeeolataaeeeeeeeeelllllllll
Introduction
One of the hallmarks of biological intelligence is its use of modular, topographically structured
systems to process sensorimotor information 221 45 20 In rodents, the barrel cortex 337
44
epitomizes this principle: each whisker maps one-to-one onto a dedicated cortical column, enabling localized, efficient processing tactile input (Fig. [A]. This precise
mapping sup- ports robust spatial discrimination and energy-efficient computation. Extensive neuroscientific studies have characterized both the vertical microcircuitry 12
36 48 [4] within each column organized across layer 2 (L2) through L6 and the horizontal connections that support lateral integration between neighboring columns [32 12 23). However, the implications of this biologically modular and anatomically constrained design for artificial intelligence remain underexplored.
Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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B
Whisker array
Sensorel
Sensor,
Sensory
Temporal ID dilated convolution
Darrel map
Barrel
Barel,
Barrela
L2i
L2:3
125
L2P
L3P
Go
L2P
L3P
L2P
L3P
L23
A
Pgr
SSP
Pyr
SSP
Pyt
SSP
TTP
STP
JTP
STP
TTP
STP
L6
Spatiorcmporal 2D convolution + MLP
Ouiput
Figure Outline of our barrel cortex- contt--contttineed localist expert model. (A) Neural pathway of "whisker-to-baree" processing in rodent. Top: whisker signals are transmitted via (1) brainstem neurons and relayed through (2) thalamic neurons to the barrel cortex. Each processing stage (brainstem, thalamus, and barrel cortex) contains somatotopically organized units corresponding to individual whiskers, with the barrel cortex exhibiting hicrarchical cortical columns (modified from ). Bottom: microscopic image of the barrel cortex, clearly showing horizontally arranged columns and vertically stratified layers. (B) Computational architecture of our multi barrel model. Each barrel column incorporates 37 synaptic connections across eight neuronal subtypes, derived from established neuroscience studies, strictly adhering to the one-to-one "whisker- thiisker-barre"" mapping by processing only its assigned tactile sensor's data. Neuronal subtype labels, from layer (L.2) L6, correspond to their neuroscientificall defined nomenclature (e.g., "SSP" denotes L4 spiny stellate pyramidal neurons ( 43 Neuronal subtype details in supplementary materials. Adjacent barrels integrate inter-barrel currents through dynamic gating network. The model employs temporally dilated 1D convolutions
17 simulate brainstem and thalamic preprocessing without inter-sensor leakage, utilizes 2D convolutions to integrate L5/6 neuronal states across barrels mimicking cortical- subcortical projections, and applies multilayer perceptron (MLP) for final predictions.
By contrast, contemporary artificial neural networks generally do not impose local specialization. Architectures such as convolutional neural networks (CNNs) 26 46 and Transformers 49 g are predominantly global in their computational flow, lacking explicit modular substructures tied to specific sensory channels. The recent success of Mfxture-of- -f-eeetts (MoE) models [I] has revived interest in modular computation in artificial intelligence. These models use gating mechanism to activate sparse subset sub-networks ("experts"") per input, improving both efficiency and scalability. Nevertheless, the routing strategy in artificial MoEs fundamentally differs from that of the brain. Artificial MoEs focus on task allocation- not enforced inter-expert collaboration and communication. In contrast, the brain utilizes topographic and localist routing, where each expert (e.g., cortical column) processes only its corresponding sensory signal and communicates primarily with spatially adjacent modules. This contrast raises a key question: can we construct a MoEs-like model based on the biologically grounded routing constraints observed in the brain?
In this work, we propose barrel cortex-inspires modular expert network for sensorimotor learning. The architecture consists of 39 interacting modules, each corresponding to a tactile sensor (whisker), faithfully replicating the somatotopic organization of the rodent barrel cortex. Each module processes its designated sensory stream using an internal structure composed of multiple biologically inspired neuron subtypes, mirroring cortical L2 through L6 (Fig. BB). All modules share common architecture

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Target subtypes
Timestep 0-1
Timestep 1-2
Timestep 2-3
Timestep 3-4
1.2/3
L4
1.5
166
L2/3
Source subtypes
Y coordinate
Neuraa activity
1.4
L6
trunk
coordinate
Neural uctivitty
VGr
ACk
X coordinate
X coordinate
X coordinate
X coordinate
Figure 2: Horizontal spread of neural activity in the multi-barrel model. (A) Schematic of vertical connectivity generalized from published neuroscience studies. Red lines indicate synaptic connections between distinct neuronal subtypes. (B) Temporal evolution of barrel states ( (spikes) in the initial model without inter-barrel currents, where only the central barrel receives sustained current input from its corresponding sensor. Barrel layout reflects the spatial arrangement of the sensor array. (C) Same as (B), but with inter-barre barrel currents enabled in the model. (D) Experimentally observed activation spread across barrels in real optogenetic recordings (adapted from (3)).
and parameter set, reflecting the repeated structure observed across cortical columns, and enabling substantial reductions in parameter count. To enable local integration while preserving spatial structure, we introduce a topographic gating mechanism: each module exchanges information only with its immediate neighbors, reproducing the localized lateral connectivity found in the barrel cortex. This results in sparse, spatially constrained expert activation, in sharp contrast to the global routing strategies used in classical MoE frameworks. Our key contributions summarized as follows:
Biologically constrained modular architecture: introduce a modular neural network whose submodules (barrels) replicate the canonical microcircuitry and sensor-mapping of the rodent barrel cortex. Each module integrates multiple neuron subtypes and strictly processes signals from its designated sensor, enabling biologically faithful modular computation.
Localist expert routing: We implement spatially grounded gating mechanism that enables communication between adjacent modules only, contrasting with current MoEs that neglect inter-expert routing. Experimental results demonstrate that localist connection suppress activity correlations between modules while reducing functional connection distances.
Empirical validation on tactile tasks: Our biologically inspired model achieves state- of-the-art performance challenging 3D tactile classification datasets [16]. The model reduces parameter usage by 97% through shared columnar weights, while improving training stability. These results demonstrate the viability of embedding strong biological priors into scalable machine learning systems.
Our work positions the barrel cortex neurobiologically constrained realization of expert system, providing a conceptual and empirical bridge between cortical computation and modular artificial intelligence. By integrating a new perspective on the design of efficient, interpretable, and high-performing artificial systems. argue that principles such topographic routing, localist specialization, and structural sparsity-long favored by evolution- can inform the development of next-generation machine learning architectures with more brain-like sensorimotor intelligence.
cettrosceenceinsppiii modularity with sparse local interactions, we offer
3

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2 Related work
Barrel cortex model: Due to its highly specialized structure and unique tactile functions, the rodent barrel cortex has become a preferred model system for neuroscientists studying sensory perception. Through techniques such as immunohistochemistry microscopic imaging, and neuronal reconstruction, considerable knowledge has been accumulated on its cellular distribution patterns [36] circuit organization. Based on these
48 42] and vertical [44] [1] 31]/horizontal [37
23
13 3
anatomical data, computational neuroscientists have developed local circuit models (2, (2) [5 and
detailed dendritic models (28) to investigate network dynamics. However, these brain simulation models lack learning capabilities, and few incorporate multi-barrel column architectures. Recently, a trainable superficial layer model that maintains biological plausibility while demonstrating tactile processing functions was proposed [59], though it remains limited to single-barrel representation without incorporating wwhisker-barref topographic mapping.
Columnar machine learning models:
The uniform yet distinctly layered structure of the neocortex, widely reported in neuroscience, has also drawn attention in machine learning. Several studies [19 have mimicked the columnar organization of the neocortex by designing analogous basic units that exhibit hierarchical feature abstraction and local-to-ghee -logglaaa integration. However, their information pathways are governed by simplistic rules, resulting in limited performance. Recent improve
18 40
columnar models [55] 24 have prioritized hardware- frienady physical implementations computational efficiency. However, these models overlook the fact that cortical column functionality based on complex and diverse neural circuits 21, and still fall short of matching the performance of contemporary machine learning systems. Meanwhile, loosely modular architectures like MoE [11 84 29 in large-scale models are gaining prominence. A promising future direction lies in the simulation of the collaborative paradigm of neocortical regions to construct versatile expert systems capable of multifunctional integration.
3
Methodology
3.1
Biologically constrained columnar modules
To enable scalable yet. biologically plausible modular replication, we model barrel columns the neural pathway-level. While prior single column models [4 33 achieved one-to-one biological fidelity through massive neuron counts (requiring cluster computing), our architecture strategically incorporates 8 excitatory neuron subtypes and 37 documented projection pathways from barrel cortex
39 27 58), including projections from Layer 4 (L4) to L2/3 neurons and feedback connections from L5/6 to L4 neurons, among others (Fig. BB) To enhance scalability, we: (1) represent inhibitory effects through negative weights rather than explicit interneurons, and (2) implement each subtype with 32 cells (256 neurons/barrel), a configuration that maintains biological plausibility while enabling efficient scaling multiple barrels. Detailed information on the eight summarized neuronal subtypes is provided in the supplementary materials.
studies 37 44 12
14
31
Neuronal dynamics follow adaptive Leaky Integrate-and-Fing (aLIF) model [54 with background currents. The membrane potential V update equation is defined as follows:
v(O)_ - Fm ((t-1) (1 c Fin) ). Rm [() - (((--)) (((-1)
y(t) (( + T:00 +
l(t) (1),
(() N(0, 1)
ogg
(1)
s(t) θ (v(0-0()) )l0) O 1 > 0
0 otherwise
where R.,, and Ti represent the membrane resistance and time constant, respectively. The input cur- rents I are categorized into four distinct components: I, denotes external input currents, I, represents intra-barrel synaptic currents, Tagg corresponds to aggregated inter-barrel currents from neighboring columns, and e denotes the background noise sampled from a standard normal distribution. When the membrane potential V exceeds the firing threshold 0, the neuron emits spike S. The neuronal firing threshold undergoes an adaptive elevation through spike-triggere accumulation, governed by the following dynamics:
g(t) -imit + B 3.7((1)
(2)
(1()) = Todp (011 + (1 Fadp). ) 1((-1)

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Table 1: Comparison across methods on two tactile datasets [6].
Independent and Shared denote our model's 39-barrel configurations with independent and shared parameters, respectively. Single refers to single barrel model with equivalent neuron size (39x256).
Method
EyTouch-Objects (%)
WVTouch-Containgg (%)
TactileSGNet [6
89.44
64.17
Grid-based CNN [16
88.40
60.17 17
GCN [16]
85.14
58.83
Method 51
90.28
SnnTdle [56
91.04
67.33
AM-SSGCN 52
91.32
GGT-SNN 5
92.36
75.00
Single barrel model
88.89
70.00
Independent multi-barrel model
92.36
85.00
Shared multi- barrel model
94.44
86.67
where Oanit and Tadp denote the resting firing threshold and adaptation time constant, respectively, with 33 being a constant parameter set to 1.8. The recurrent current I,- received by a postsynaptic neuronal population from other upstream populations within the home column can be computed as:
7
Σ W S/ JEP
Wj E 32x322
where W and 5 denote the connection weight matrix and the spike vector of the j-th upstream population in the presynaptic set P. respectively. The computation processes for Lagg and L. in Eq. are elaborated the following two sections.
3.2 Localist inter-barrel routing
Beyond vertical intra-barrel pathways, the barrel cortex employs horizontal inter-barrel connections for sensory integration. Neuroanatomical evidence indicates that lateral connections follow an adjacency-priority principle [32] 12]. with inter-barrel signaling predominantly originating from spatially adjacent columns and minimal contributions from distant ones. We implemented this biologically observed connectivity through K-nearest neighbor (KNN) spatial mapping coupled with sparse gating mechanisms.
For 32-neuron subtype in model, input signals from neighboring barrels follow these governing equations. First, the most relevant neighboring subtypes indices T are dynamically selected based on gating weights:
(+)) TopK D) K. 8.7 (()) MLP.(1 E
(4)
where K denotes the number of spatially adjacent barrels (nearest neighbors). 7 represents the fraction of total (K 8) neuronal subtypes to routed, giving I selected subtypes. I!#t) and correspond external input currents and trainable parameters, respectively. TopK (-) returns a binary mask E {o, 1} Kx88 with exactly I nonzeros. The spatial distance between barrels is computed based on the sensor coordinates provided in the tactile dataset 0.2, respectively.
[16] K and y were assigned values of 4 and
Then, the final aggregated neighborhood current ((t) is computed by applying the selected subtypes indices from (()) (mask) to the spiking states sff), flattening the results vector in 1'32 and performing trainable linear transformation:
(t) o.g
Wagg Flatten((()) @ ((O)
where agg E 32x (T-32 32) denotes the trainable weight matrix mapping the flattened neighborhood
activity vector to the 32 neurons of the target barrel, and s [s]
(!) s2 ..... s Kx8l E (K xx3)
5

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A
2344
8
80
-0
10
Accurcy (%)
Stnglee herel model
Staral mulli bunel mudel
Inleepeneens mult--barel model
3.48
40
0.46
20
RA5
Ev ObJoecs-Obeects
Ev LyTosch--onttseess
Independent multi-barrel model
Shared
multi-burrel model
Figure 3: Performance comparison of three model variants. (A) Scores from 10 repeated random
training runs for each variant. (B) Loss landscapes of independent-paramene and shared-paramen parameter models. Perturbations were applied along
orthogonal directions to trained parameters: O' O-mm + 312, where and 0' denote the original and perturbed parameters. and perturbation magnitudes. Top to bottom: results on the Ev Touch-Objects and Ev TyToch--Conniin datasets.
represents the tensor composed of spiking states from all neuronal subtypes in neighboring barrels at timestep L.
Fig. 2 illustrates the temporal state evolution in multi-barrel model under optogenetic- stimulation 15), achieved through constant current activation of the central sensorimotor barrel. The results reveal that incorporating inter-barrel current coupling induces propagated activation from the stimulated barrel to neighboring regions, progressively diffusing across the entire array. This spatiotemporal propagation pattern aligns with cortical barrel dynamics reported in empirical optogenetic studies of the "whisker-barne barrel" somatosensory system
23 13
3.3 Empirical validation tactile datasets
We aim to both develop barrel cortex- cortex-inspired architecture and validate the "whisker-barrel" system as localist expert processor. Below, we describe the public datasets used and our differentiable readin- readout implementation, which bridges biological computation with machine learning optimization through effective gradient propagation.
Two tactile datasets similar whisker systems, ToTTcchh-bjecc and employed to benchmark our model against artificial neural networks. These datasets feature temporal signals recorded from independent Neu sensor arrays 47 during interactions with diverse 3DD objects, requiring the model to predict object categories based on dynamic tactile inputs. Each data sample has shape of [39. 2, 7), representing
[16]. were
channel signals recorded from 39 sensors over timesteps. Appendix A. provides complete tactile dataset specifications.
We treat each sensor rodent whisker (Fig I3), where brainstem -thlllmmm-tt signal preprocessing 41 is simulated via one-dimensional dilated convolutional layers [17]. linear layer then serves as the thalamic signal relay. The external current I. received by neuronal subtype in barrel cortex is computed as:
(0) WT, (Waz @ (Wa @ (())), We E 61x322
where X and @ denote the input data and dilated convolution operation, respectively. Wa1 and Wa represent the first- and second-layer convolution kernels. is the activation function. The convolution operates exclusively along the temporal dimension, preventing inter-sensor information leakage. The output tensor dimension becomes [39, 64, T'), where T' T- + with d, & and s being the dilation rate, kernel size and stride, respectively.

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A
B
Target subtypes
C
Column
L2P
L3P
Pyr
SSP
STP TTP CTP CCP
L2P
3.33
1.67
Source subtypes
Pyr-
1.67
3.33
5.00
3.33
6.67
Perfomancss drop (%)
Models
Original Model
NoVenCorn
SSP
3.33
5.00
5.00
5.00
1.67
NotlonCom
L44
STP
0.00
3.33
3.33
0.00
L55
0.00
5.00
1.67
CCP
1.67
3.33
3.33
3.33
5.00
1.67
Accuracy (%)
Figure 4: Ablation analysis of synaptic connectivity on after isolated removal of vertical connections and horizontal inter-barrel currents, ranked by descend- ing accuracy. (B) Quantitative importance of 37 intra-barrel connections, measured by performance degradation magnitude. (C) Four highest-impact signaling pathways identified from (B).
(A) Model performance
Given the dominant role of L5/6 neurons in driving subcortical projections within barrel cortex 12
44
we read-out the state of our model's L.56 neuronal populations through: (1) spatiotemporal integration via a two-dimensional convolution layer, followed by (2) final classification prediction through a multilayer perceptron (MLP). The formula expressed as follows:
① = Softmar W mlp Flatten(Wa, @ S15/6)), S15/6 E R (39x(32-1)
where O denotes the two-dimensional convolution operation. Wa and Wmip represent the convo- lution kernel and MLP parameters, respectively. S15/6 denotes the spiking states of four neuronal ensembles (32 neurons each) in L5/6 across 39 barrels over T' timesteps. The model employs standard cross-entropy loss, with spiking neuron gradients computed via the Gaussian surrogate function: (V((())))() r²), where is set to 0.5.
Given that our multi- baalielbrrelllee model is extended from single barrel as introduced in Sec. 3.1 a natural question arises: whether these barrels share training parameters or maintain their own independent parameters. Excluding the shared readin and readout pathways, in our 39-barrel configuration, the parameter count of the column modules with shared parameters is reduced by approximately 97% compared to those with independent parameters. practice, their parameter counts are 59, 104 and 2,305,056, respectively, a reduction of two orders of magnitude. This significant decrease in parameters is crucial not only for computational efficiency, also serves as a form regularization. In the experimental section, we investigate the tactile task performance of both model variants.
4 Experimental results
4.1 Implementation details
Our multi-barrel model was trained for 200 epochs on both TrTTech--bbjecs and EvTouch- EvTouch-Containe Containers datasets [16] using an AdamW optimizer [30] with 0.1 weight decay. The initial learning rate of 0.0008 decayed by factor of 0.8 every 10 epochs. The loss function employed solely the standard cross-entropy criterion. All experiments were conducted on an 80GB NVIDÍA A100 GPU.
4.2 Performance comparison with baseline methods
First, we evaluated our multi-baree model against reported baselines on both Tbjeeehbbbjeets and "TTouchCContaine datasets, achieving state-of- saute-of-the-. fe-thh-art art (SOTA) classification accuracy (Tab. [ Our model outperforms the previous best-performing method GGT-SNN (53), achieving accuracy improvements of 2. 5%
While prior work employed graph neural networks [6 52 5k] process sensor topology, our biologically constrained
CeTTouch-Ojeeee and 11.7% on
architecture attains comparable performance through simple "whisker-to to-brreel" mapping. Notably,
7

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collapsing the columnar organization into a single barrel reduces accuracy by 5.6% CeTTocch-Obecctt and 16.7%
demonstrating the functional importance of modular architecture.
Furthermore, we compared two model variants from Sec. 3.3 parameter-shared (39 barrels) versus independenttparaaeeerr configurations. The shared-parameres variant showed superior performance (2.1% higher on Ev EyTouch-Objects, 1.7% on Containers) with greater training stability, whereas ceapeeddett-prrmmete model occasionally failed to train (Fig. AA) Loss landscape analysis reveals this dichotomy: shared parameters create smooth basins enabling robust convergence, whereas inde- pendent parameters yield rugged terrain with local fluctuations (Fig.BB) This likely occurs because independent parameters per barrel generate conflicting gradient updates during training, thereby com- plicating optimization. In contrast, shared parameters naturally enforee cross-barre barrel regularization. effectively mitigating stochastic gradient variations. These results validate the functional advantages of biologically uniform organization and modular replication in artificial expert systems.
4.3
Ablation analysis of synaptic connectivity
Then, we systematically assess synaptic importance in our multi-barrel model through ablation experiments, isolating and blocking each connection while quantifying performance degradation. Taking the
dataset as an example, inter-barrel aggregated currents show the greatest impact, reducing classification accuracy by approximately 11.7% (Fig. 4A), while some vertical connections exhibit minimal impact.
Fig. 4 demonstrates laminar-speciff effects through directional analysis of 37 intra- vertical projections. The four L.4 to 1.2/3 connections collectively contributed 13.3% performance, whereas two reciprocal 1.2/3 to L4 connections showed weaker impacts but stronger L5 modulation. We identified four dominant pathways: L4-L2/33 L4-L6, L2/3-L5, and L6-L4 (Fig. 4C). These pathways align with established neuroanatomical principles: the L4-L2/3-L55 microcircuit represents a canonical barrel cortex circuit (37 31,, and L6 cortico-thalamie pyramidal (CTP) cells receive dendritic inputs
from L4 pyramidal neurons 39 44 and project reciprocally to L4 boundaries [27] [58). This
alignment may suggest that cortical microcircuits represent an evolutionarily optimized architecture.
Besides, deactivating specific synaptic connections (c.g., STP to L2P) does not compromise model performance, suggesting the existence of redundant pathways. This functional resilience likely results from substitution by connections with similar directional properties. Consequently, more precise characterization is required delineate the distinct contributions of specific neuronal subtypes.
4.4 Analysis of localist routing patterns
Next, we examined the localist routing behavior of our multi-barrel model. In contrast to the decentralized, globally routed structure of conventional MoE systems, each expert (i.e., barrel column) in our system communicates only with its immediate neighbors. As illustrated in Fig. Fig. the KNN gating constraints introduced in Sec. 3.2 yields predominantly short-range horizontal connections in both the initial and the trained models; long-range projections spanning multiple barrels are entirely absent.
To quantify the functional impact of these horizontal connections, we analyzed the correlations of barrel-column activity on the testsets, considering both full-trial and sliding-winde -wndoww time scales Frg.BB)) Introducing inter-barrel currents decreased global mean correlations by 0.07 on EvTouch- Objects and 0.01 on respectively (Fig. F). Reduced synchrony suggests that horizontal currents promote richer, more specialized activity patterns across columns, thereby improving the discriminability of complex inputs. This effect echoes the interallinhiiition mechanism reported in the biological barrel cortex, where activated columns transiently suppress their neighbours to sharpen sensory contrast [38 35 10).
windowed (local) correlations fell by 0.03 and 0.01,
The spatial signature of these effects is equally striking. Inter-barrel currents shortened the average distance between strongly correlated column pairs by 0.06 grid units on EvTouch- Obbjctt and 0.17 on Ev Saauch-Cottaieess for the top-5 most-correlated pairs, the reductions were even larger-1.8 118 and 3.48 units, respectively (Fig. (.. As depicted in Fig. 5CDD, the top-correlation pairs overwhelmingly involved neighbouring barrels once horizontal gating was enabled. Together, these findings show that localist inter-barrel currents simultaneously strengthen short-range functional connectivity and amplify activity differentiation across columns. The results are consistent with long-standing neuro-
8

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A
Initial network
Trained network
Initial network
Trained network
EvTouch-Objects
EvTouch-CCntainers )
NaHoriConn
WithHoriConn
B
Barrels
60
Timestep
E
10
WishHeriCoon
EvTcchh-Obi Objects
Correlation
0.30
Touch-Containsee
0.25
0.15
Global mean
Window meum correlation
Strong pains distance
pairs Top-5 pains
correlation
distance
Figure 5: Statistic of barrel correlations. (A) Barrel-wise localist routing. Arrows indicate the most frequently selected source neighbor barrels, as determined by cumulative counts across samples and timesteps. (B) Neural activity of 39 barrels during single trial. A sliding window (length 10, stride = 2) was applied to compute pairwise activity correlations. The three most strongly correlated barrel pairs per window were selected high-correlation pairs. (C) Top-5 most frequent strongly correlated barrel pairs on EvTouch-Objeck dataset. From left to right are models with blocked and intact horizontal inter-barrel currents. (D) Same (C), but for TyTouch--ontaiiers dataset. (E) Statistical metrics between blocked and intact models. From left to right are whole-time mean barrel correlations, windowed mean correlations, mean distances strongly correlated barrel pairs, and mean distances of Top-5 pairs.
physiological principles 32 12 and may inform the design of future MoE systems with enforced inter-expert communication to reduce information redundancy [50] 57].
Conclusion
In this work, we focus on the rodent barrel cortex as a localist expert system in the brain, developing multi-barrel model that strictly adheres to one-to-one "whisker-barrel" somatotopic mapping. Our architecture faithfully replicates the laminar and columnar organization of barrel cortex, achieving state-of-the-art performance on two tactile datasets while successfully balancing biological plausibility and behavioral performance. Experimental results demonstrate that the cortex's uniform modular

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architecture facilitates parameter sharing to enhance training stability, while localist routing reduces functional connection distances and suppresses inter-barrel activity correlations. Inter-barrel currents may sharpen perception and reduce redundancy. This work reveals the potential of brain's 's native expert systems inspire next-generation machine learning architectures.
On one hand, the redundant synaptic connections observed in Fig. 4B necessitate more refined neuronal subtype differentiation in modeling, which requires deeper anatom- ical knowledge integration. On the other hand, our multi-barrel model remains distinct from standard MoE architectures and has not yet been validated on mainstream machine learning benchmarks. De- veloping inspired MoE architectures that emulate cortical coordination mechanisms presents an exciting research direction. Another promising future direction involves embedding our biologically constrained barrel cortex model into realistic real-time interactive environments. This framework would leverage biologically constrained models to explore performance in complex sensory tasks (e.g., navigation). thereby significantly advancing both our understanding of brain operating principles and potential applications.
Limitations and future work:
Acknowledgments
This work was financially supported by the STI 2030-Major Projects (2021ZD0201002), and National Natural Science Foundation of China grants 32471149,,
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A Technical Appendices and Supplementary Material
A.1 Tactile task introduction
This section details our tactile perception task. Both employed datasets, EyTouch-Objects and EvTouch-Container [T)], were acquired through an identical protocol: the NeuTouch [47] sensor array engaged in several-sccond tactile interactions with 3D objects, followed by object category prediction based on the recorded sensor signals (Fig. (A) The NeuTouch system integrates 39 uniformly configured sensor units, each generating two-channel spiking signals, with their spatial arrangement depicted in Fig. 6B. The datasets were categorized into EvTouch T'ouch-Objectss (720 samples, 36 categories) and Ev TYTouch-Container (300 samples, 20 categories) based on object taxonomy, with both datasets split into training and test sets an 8:2 ratio for standardized evaluation.
As described in Sec. S.c. we modeled each of the 39 tactile sensors as a rodent whisker, with separate barrel modules processing each sensor's signals independently. Each raw sample in the tactile dataset has a shape of [39, 2. TJ, representing two-channel signals from 39 sensors over T timesteps. where T equals 250 for Ev EyTouch-Obeects and 325 for
ID dilated convolutions along the temporal dimension to simulate brainstem and thalamic preprocessing of whisker signals [41 [5]. including delay and integration effects [17] Dilated convolutions were exclusively applied along the time axis to prevent cross whisker signal leakage (Fig. (C). The
We first applied
processed timesteps computed T' (T-d(k-1)-- + 1, where d, k and represent the dilation rate, kernel size, and stride respectively. Then, the preprocessed whisker signals were independently propagated their corresponding barrel columns. Finally, standard 2D convolution operation integrated the barrel-wise L5/6 neuronal states across the entire array, followed by an MLP to generate the final prediction output.
NeuTouch sensors
B
C
II
Temporal
1D dilated
Y
convolution
Touch
[39, 2, T]
[39, 64, T'
Figure 6: Tactile perception task overview. (A) Object categorization via several-second tactile
scanning using NeuTTouch sensor array (datasets: SyTouch--ObjectsCCntaane
[16]. (B) NeuTouch's
39-sensor spatial configuration (adapted from 47)). (C) Each sensor modeled as a whisker, with 1D
dilated convolutions simulating brainstem thalmmmcchaaaacccccaaaooo delay/integration [17
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Reviews:
[{"review_id": "owu8Nz5KG6", "reviewer": "Reviewer_NTKp", "summary": "- This paper proposes a barrel cortex-inspired modular neural network architecture for sensorimotor processing\n- Each module corresponds to a cortical column and input sensory signals are routed exclusively to their specific columns/modules. \n- The authors achieve state-of-the-art performance on tactile object classification while reducing parameters by 97% through columnar weight sharing", "questions": "- What audience do the authors think will resonate with this work and how can it be built upon?", "limitations": "Yes", "rating": 2, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 1, "originality": 2, "strengths_and_weaknesses": "strengths:\n- Well explained biologically-inspired architecture that faithfully replicates established neuroscientific principles\n- Achieves high parameter efficiency while maintaining competitive performance\n\nweaknesses:\n- The authors claim that this work “provides a step toward next-generation expert systems that bridge neuroscience and artificial intelligence”, but it is not clear to me how this work really pushes forward either community. More performant and lower parameter count models could be built if there were no biological constraints as there are numerous non-biological ways of reducing model parameter counts. From the neuroscience perspective mouse barrel cortex organization is already well understood and the authors don’t offer any suggestion on how this new model could be used to explore testable hypotheses of neuroscientific interest.", "was_revised": true, "final_justification": "Overall, I would still like the authors to down-tone their claims regarding machine learning advances made by their work. I will maintain my score.", "rebuttal": "We sincerely thank you for your feedback on the contributions of our work. Below, we would like to respond to each of your concerns point by point.\n\n> Weaknesses: The authors claim that this work “provides a step toward next-generation expert systems that bridge neuroscience and artificial intelligence”, but it is not clear to me how this work really pushes forward either community. More performant and lower parameter count models could be built if there were no biological constraints as there are numerous non-biological ways of reducing model parameter counts. From the neuroscience perspective mouse barrel cortex organization is already well understood and the authors don’t offer any suggestion on how this new model could be used to explore testable hypotheses of neuroscientific interest.\n\n**A1:** We appreciate your concern about how our work contributes to both fields, and we provide the following points of clarification. \n\n**From an AI perspective,** our work offers more than a performance improvement or parameter reduction—it fundamentally rethinks model design by drawing inspiration from brain architecture. As one of the other reviewers insightfully noted, “*It demonstrates how cortical principles inspire machine learning, which is interesting and illuminating.*” We agree that non-biological approaches can reduce parameters, but our aim is to leverage principles from the most efficient intelligent system we know—the brain—to guide AI design. \n\nSpecifically, our study highlights the differences between AI expert systems, such as the MoE, and cortical expert systems like the barrel cortex. Unlike MoE, which emphasizes task allocation by routing tokens to the appropriate expert, the barrel cortex features innate one-to-one mappings and emphasizes collaboration and competition among modules. Our work explores these collaborative processes, applying them to real-world tactile tasks. \n\nWhile achieving SOTA performance on two challenging tactile datasets, our model’s primary goal is not to compete with unconstrained AI models purely on performance but to introduce a paradigm in designing AI systems inspired by biologically evolved structures.  Examples from bio-inspired computing already underscore the value of such an approach, including convolutional neural networks [1, 2], which were inspired by the cat’s visual cortex, and spiking neural networks [3, 4], which emulate the spiking behavior of biological neurons.  This is not the only direction for better AI design, but it is certainly a rational and promising one.\n\n**From a neuroscience perspective,** we agree that the anatomical organization of the mouse barrel cortex is well studied. However, there is a gap in translating these anatomical insights into a **functional, trainable computational model**. To our knowledge, among the six existing barrel cortex models listed in ModelDB [5, 6, 7, 8, 9, 10], none incorporate a modular multi-barrel columnar structure or support end-to-end training on behavioral tasks. Our work is the first to integrate the barrel cortex’s columnar organization and dynamics into a trainable model that balances biological plausibility with practical functionality in tactile perception tasks. \n\nThis integration allows us to propose testable computational mechanisms inspired by cortical structure and dynamics. For instance, our model enables us to: (1) Examine how reusing the same module affects learning efficiency (Fig. 3 in our paper); (2) Quantify the contribution of individual neural microcircuits to overall tactile perception (Fig. 4). (3) Investigate the effect of lateral currents on network dynamics (Fig. 5). Each of these findings can be viewed as a testable prediction or hypothesis about how the real barrel cortex processes tactile information.\n\nIn summary, our aim is not only to increase the practical value of biologically-inspired models, but also to leverage modern AI techniques to turn them into quantifiable frameworks that can bridge to neuroscience.  By doing so, we take a step toward next-generation expert systems that truly bridge neuroscience and AI: benefiting AI through new design principles and benefiting neuroscience through models that generate experimentally testable insights.\n\n***\n\n> Questions: What audience do the authors think will resonate with this work and how can it be built upon?\n\n**A2:** In terms of the audience who may resonate with this work, we believe there are several key groups. \n\n1. **Computational Neuroscientists:** Our work offers an approach of how biological realism can be combined with functional performance. Rather than focusing on anatomy alone, we demonstrate a way to incorporate cortical structures into a working model that performs real tasks.  This balance of biological plausibility and functional utility could inspire similar integrative modeling efforts for other brain regions.  As neuroscience data and knowledge continue to grow, there is a need for unified models that can both explain and predict brain function.  We hope our approach encourages the community to develop more such models that are biologically grounded yet behaviorally relevant.\n\n2. **Neuromorphic Computing Researchers:** Built on a spiking neural network backbone, our model is well-suited for deployment on neuromorphic hardware [11], which could drastically improve energy efficiency for tactile processing. Moreover, by directly instantiating principles from sensory cortex, our approach offers a ready-made architecture for advanced tactile perception. This is especially timely given the rising interest in tactile intelligent systems and robotics [12, 13]. Our model could serve as a starting point for implementing brain-like tactile processing on neuromorphic chips, and further research can explore optimizations (e.g., sparse computing, on-chip learning) that capitalize on its brain-inspired structure.\n\n3. **MoE Researchers:** As mentioned in the paper and response A1, current MoE research primarily focuses on task allocation—how to appropriately divide tokens and assign them to corresponding experts at the right level.  Our work highlights that the brain's natural expert systems (barrel cortex) exhibit distinct mechanisms of collaboration and competition.  These differences could inspire novel MoE architectures, such as exploring inter-expert collaboration to reduce information redundancy or leveraging inter-regional communication in the brain to handle multimodal tasks.  Some recent MoE studies [14] have started focusing on expert collaboration, and understanding how the brain divides and coordinates tasks could provide valuable insights for these efforts.\n\nWe envision that researchers in these areas can build upon our model in various ways: by extending it to other sensory modalities, by increasing its biological detail (if their goal is a more accurate brain simulation), or by abstracting it further for general AI tasks.  We are excited to see how the community will take these ideas forward and we are happy to engage in a dialogue to support such efforts.\n\n### **References**\n\n[1] Yann Lecun et al., “Gradient-Based Learning Applied to Document Recognition”, 1998.  \n[2] D. H. Hubel and T. N. Wiesel, “Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex”, 1962.  \n[3] Wolfgang Maass, “Networks of spiking neurons: The third generation of neural network models”, 1997.  \n[4] Kaushik Roy et al., “Towards spike-based machine intelligence with neuromorphic computing”, 2019.  \n[5] Lavzin et al., “Nonlinear dendritic processing determines angular tuning of barrel cortex neurons in vivo, 2012.  \n[6] Kremer et al., “Late Emergence of the Vibrissa Direction Selectivity Map in the Rat Barrel Cortex”, 2011.  \n[7] Diego et al.,” Mechanisms underlying a thalamocortical transformation during active tactile sensation”, 2017.  \n[8] Argaman et al.,” Does Layer 4 in the Barrel Cortex Function as a Balanced Circuit when Responding to Whisker Movements? ”, 2017.  \n[9] Domanski et al., “Cellular and synaptic phenotypes lead to disrupted information processing in Fmr1-KO mouse layer 4 barrel cortex ”, 2019.  \n[10] Huang et al., “Cortical Representation of Touch in Silico”, 2022.  \n[11] Eunhye Baek et al., “Neuromorphic dendritic network computation with silent synapses for visual motion perception”, 2024.  \n[12] Meng Qi et al., “Self-powered artificial vibrissal system with anemotaxis behavior”, 2025.  \n[13] Zihang Zhao et al., “Embedding high-resolution touch across robotic hands enables adaptive human-like grasping”, 2025.  \n[14] Zhang et al.,  \"Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design\", 2025."}, {"review_id": "Hj1oDDw1bX", "reviewer": "Reviewer_FAhN", "summary": "This paper describes a localist MoE architecture based on sensory neuroscience, specifically rodent whisker-barrel cortex. Each expert receives input from a unique sensory channel and experts communicate with their neighbors in the topology induced by the input space. Each expert is a spiking NN modeled after laminar cortex. The model shows SOTA performance on two haptic object classification tasks.", "questions": "I realize the ML-brain analogy is intended to go only so far, but is it plausible for cortical columns to share learnable synaptic weights? Assuming no, is there a different solution available to the brain?\n\nFor the goal of developing scalable ML systems based on this architecture, it would help to know whether the spiking dynamics and within-column recurrence are essential. Have you investigated your framework in FF analog networks?\n\nIt would also help to know the relative importance of the localist MoE architecture versus the biologically based organization within each expert (e.g., differentiated cell types and cortical layers with predefined circuitry).\n\nMy understanding of lateral connections in the brain is that they are primary inhibitory for computing local contrast. Fig 2c seems to show the opposite. What patterns do the learned inter-barrel weights show in the main experiments?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\nNovel localist MoE architecture\n\nImpressive experimental results\n\nWeaknesses:\n\nIt's unclear how scalable the approach is. The current implementation using spiking neurons and recurrence. The ablation study is a bit concerning in this regard as it suggests recurrence is essential to the results.", "was_revised": true, "final_justification": "Novel localist MoE architecture with implications for neuroscience and SOTA performance on 3D tactile tasks. Future work is needed to scale up but the conceptual contribution is clearly above threshold.", "rebuttal": "We appreciate your insightful and valuable question. Below, we will address your concerns in detail, one by one.\n\n> Q1: I realize the ML-brain analogy is intended to go only so far, but is it plausible for cortical columns to share learnable synaptic weights? Assuming no, is there a different solution available to the brain?\n\n**A1:** This is an interesting and important question.  **We believe that the brain's parameters consist of two components:** a fixed foundational structure and fine-tuned synaptic plasticity.  The former represents genetically preserved features shaped by evolution, such as brain region organization, connectivity, and electrophysiological parameters (discussed in *\"Probabilistic skeletons endow brain-like neural networks with innate computing capabilities\", 2021*).  These structural features are shared among individuals, require no learning, and provide a baseline level of intelligence present at birth.  On the other hand, synaptic plasticity, such as Hebbian learning rules, enables individuals to acquire and refine their intelligence through experience, serving as fine-tuning on top of the foundational structure.\n\nIn the barrel cortex, individual barrels naturally exhibit similar neural distributions and connectomes, which form the core functional structure.  While differences in whisker usage frequency may lead to variations in synaptic weights, these are supplementary adjustments rather than the primary basis for function.  Therefore, we believe the structure of barrel columns largely adheres to the principle of modular reuse.\n\nWhat makes this particularly interesting is its alignment with the current development of large models in artificial intelligence.  Researchers often use a limited set of pre-trained model parameters as a foundation and then fine-tune them to develop expert models in different domains.  Pre-trained parameters resemble genetic information, while post-training processes can even reveal a \"lineage\" of large models based on the statistics of shared parameters.  From this perspective, studying the convergent evolution of natural and artificial intelligence represents an exciting future direction.\n\n***\n\n> Q2: For the goal of developing scalable ML systems based on this architecture, it would help to know whether the spiking dynamics and within-column recurrence are essential. Have you investigated your framework in FF analog networks?\n\n**A2:** We agree that ablation experiments for biological constraints are important, and we will provide additional experimental results in detail in our response A3 for the next question. Here, we would like to focus specifically on the Forward-Forward network. As you pointed out, there is currently no definitive evidence for end-to-end backpropagation occurring in the brain. However, it remains a practical and stable learning algorithm, which is why it is used in our model. In fact, one of the main reasons we avoid explicitly defining inhibitory neurons is that excessive recurrent connections could result in vanishing gradients in distant layers.\n\nBrain-inspired local learning rules, such as Hebbian learning and Spike-Timing-Dependent Plasticity (STDP), offer significant advantages in terms of efficiency and cost. However, in their current form, they are less practical than backpropagation for achieving comparable performance. Therefore, we continue to use gradient descent and backpropagation to balance the biological plausibility of our model with its behavioral functionality. We believe integrating brain-inspired learning rules is an important research direction, and it relies on the development of more effective local learning algorithms in the future.\n\n***\n\n> Q3: It would also help to know the relative importance of the localist MoE architecture versus the biologically based organization within each expert (e.g., differentiated cell types and cortical layers with predefined circuitry).\n\n**A3:** We conducted three supplementary ablation experiments to evaluate the relative importance of biological constraints. First, **we replaced the horizontal localist routing in the model with random connections** while keeping the number of neighbors fixed at 4. Each configuration was trained three times, and the best results are shown in the table below:\n\n| Datasets | &#124; Random connection &#124; | Localist routing |\n|:---|:---:|:---:|\n|EvTouch-Objects|90.28|**94.44**|\n|EvTouch-Containers|83.33|**86.67**|\n\nAs shown, replacing localist routing with random connections led to performance drops of approximately 4.2% and 3.3%, respectively, highlighting the advantages of localist routing for these tactile perception tasks.  This is likely because object textures often exhibit spatially structured patterns, and localist routing preserves this neighborhood information, facilitating the integration of spatially correlated signals for better classification.  In contrast, random connections disrupt this spatial organization, introducing noise and reducing the model’s ability to capture meaningful relationships between neighboring signals.  By leveraging the inherent spatial coherence of tactile data, localist routing aligns more closely with the structured nature of sensory inputs, making it more effective for these tasks.\n\nNext,  **we replaced each barrel in the multi-barrel model with an MLP** of varying depths while keeping other configurations unchanged.  The MLPs shared parameters, with each hidden layer containing 256 neurons.  Each configuration was trained three times, and the best results are shown below:\n\n| Datasets | &#124; 2-layer MLP &#124; | 3-layer MLP &#124; | 4-layer MLP &#124; | Biological barrel |\n|:---|:---:|:---:|:---:|:---:|\n|EvTouch-Objects|89.58|88.89|88.89|**94.44**|\n|EvTouch-Containers|83.33|83.33|81.67|**86.67**|\n\nIt is evident that replacing biologically constrained barrels with MLPs, regardless of the number of layers, results in a significant performance drop.  This is likely because biologically inspired recurrent spiking neural networks exhibit richer temporal dynamics, allowing them to capture complex time dependencies.  Additionally, their inherent ability to store historical states makes them better suited for sequential tasks.  However, understanding how the biological structure within a barrel (e.g., laminar structure and neuronal distributions) aligns with tactile tasks requires deeper insights from neuroscience.\n\nFurthermore, **we tested whether localist routing remains effective within the MLP units** (With Localist Routing vs. No Localist Routing). The average results of three repeated experiments are presented in the table below. Overall, incorporating localist routing achieved better performance, particularly on the more challenging EvTouch-Containers dataset, indicating the generalizability of our model.\n\n| Datasets | 2-layer MLP (No LR) | 2-layer MLP (With LR) | 3-layer MLP (No LR) | 3-layer MLP (With LR) | 4-layer MLP (No LR) | 4-layer MLP (With LR) |\n|-----------------|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|\n| EvTouch-Objects | **90.74** | 88.87 | 88.193 | **88.89** | 88.43 | **88.66** |\n| EvTouch-Containers | 76.67 | **82.78** | 72.22 | **81.67** | 72.78 | **80.56** |\n\n***\n\n> Q4: My understanding of lateral connections in the brain is that they are primary inhibitory for computing local contrast. Fig 2c seems to show the opposite. What patterns do the learned inter-barrel weights show in the main experiments?\n\n**A4:** Thank you for pointing out this potentially confusing aspect. While lateral inhibition is widely recognized, it is important to note that the **horizontal connections in the barrel cortex include both inhibitory and excitatory types** (clearly illustrated in *Neuronal Circuits in Barrel Cortex for Whisker Sensory Perception, 2021*).\n\nIn reality, rodent whiskers facilitate two forms of tactile perception: active (referred to as \"whisking\") and passive (referred to as \"touch\") [1, 2, 3]. During whisking, muscles actively move the whiskers to sweep over surrounding objects, triggering brief lateral inhibition to enhance precise whisker localization. Neural responses in this case are smaller and more localized. In contrast, during passive touch, external stimuli to a single barrel elicit larger and more widespread neural responses, which is illustrated in Fig. 2C-D in our paper.\n\nFor machine learning classifiers, **excitatory inter-barrel currents can similarly enhance neural activity differences** (Fig. 5E). By selectively modulating inter-barrel currents, they amplify relative activity contrasts between barrels, thereby encouraging competition. When neural activity across barrels is highly uniform, it suggests the encoding of redundant information. Thus, even without explicitly defining lateral inhibition, the inter-barrel currents in our model, combined with machine learning techniques, can selectively amplify perceptual differences to improve representation diversity.\n\n### **References:**\n\n[1] Carl C.H. Petersen and Sylvain Crochet, Synaptic Computation and Sensory Processing in Neocortical Layer 2/3, 2013.  \n[2] Carl C. H. Petersen, Sensorimotor processing in the rodent barrel cortex, 2019.  \n[3] Anton Sumser et al., Active and passive touch are differentially represented in the mouse somatosensory thalamus, 2025."}, {"review_id": "rzDxKApK5B", "reviewer": "Reviewer_qWQh", "summary": "This paper proposed the Modular Network using Localist Topographic Expert Routing, which is inspired by Rodent Barrel Cortex . The method achieved state-of-the-art performance on challenging 3D tactile 13 object classification benchmarks, which shows the effectiveness of this method. The biologically inspired design is novel and interesting, and the experimental results have also demonstrated the effectiveness of the proposed method.", "questions": "Question：\n\nAre the different modules within a functional column designed differently, or are they designed identically with each module merely corresponding to the concept of a biological structure?", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The biologically inspired design is novel. \n\n2. Routing within and between modules can effectively improve computational efficiency, and the experimental results also demonstrate advantages in performance.\n\n3. It demonstrates how cortical principles inspire machine learning, which is interesting and illuminating.\n\nWeakness:\n1. There seems to be no further introduction on how the modules within the functional columns are designed.\n\n2. The article mentions MoE multiple times, but there is no comparison with simple MoE. This could have better demonstrated the effectiveness of the biologically inspired routing.", "was_revised": true, "final_justification": "Based on the article and the authors' responses, I consider that this work is more of a biologically inspired approach at the conceptual level. Due to the complexity of biological mechanisms and the current challenges of compatibility with existing artificial networks, I think this is a very good attempt even if it only draws on biology conceptually. Therefore, I will maintain my original rating.", "rebuttal": "Thank you for taking the time to review our work and raising valuable concerns. We will address each point individually.\n\n> W1: There seems to be no further introduction on how the modules within the functional columns are designed.\n\n**A1:** Thank you for this valuable suggestion. The description of the internal structure of barrel columns in our paper is indeed insufficient. **In response A3 to Q1**, we have provided more detailed information on how the neural circuits are constructed, which will be added to the supplementary materials.\n\n***\n\n> W2: The article mentions MoE multiple times, but there is no comparison with simple MoE. This could have better demonstrated the effectiveness of the biologically inspired routing.\n\n**A2:** To make the experimental results more comprehensive, **we supplemented two simple baseline MoE models for comparison:** Top-2 MoE and Dense MoE, with different numbers of experts. Each expert is a two-layer MLP with a hidden layer of 256 neurons, and a gating network allocates signals from 39 tactile sensors to the respective experts. Computations along the temporal dimension are performed independently. Each configuration was tested three times, and the best results are as follows:\n\n| Datasets |&#124;  | Top-2 MoE | |&#124; | Dense MoE | |&#124; Ours |\n|----------|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:----:|\n|          | 16 experts | 32 experts | 48 experts | 16 experts | 32 experts | 48 experts |      |\n| EvTouch-Objects | 88.89 | 90.28 | 90.97 | 90.28 | 89.58 | 88.89 | **94.44** |\n| EvTouch-Containers | 70 | 73.33 | 73.33 | 66.67 | 75 | 70 | **86.67** |\n\nIt can be observed that these two MoE baselines perform significantly worse than our multi-barrel model on both tactile datasets.  This may be due to the misalignment of the MoEs with the tactile data (our model follows a cortical one-to-one mapping) or the limited capacity of the MLP units.\n\nHowever, we guess that you might be more interested in how the localist routing mechanism in our model would perform when applied to mainstream large-scale MoE architectures on LLM benchmarks—a question that also greatly interests us. This poses several challenges: How should \"neighboring\" experts be defined? How can communication between experts be modeled? Addressing these questions may require some abstracting principles, such as \"expert grouping\", which would almost be another whole different work and is a promising direction for our future research.\n\n***\n\n> Q1: Are the different modules within a functional column designed differently, or are they designed identically with each module merely corresponding to the concept of a biological structure?\n\n**A3:** Each column in our 39-barrel model shares the same structure. The column module was designed based on existing neuroscience studies and replicated 39 times to form the 39-barrel model. This replication enables parameter sharing between columns (Fig. 3). To clarify the structure of the barrel columns, the table below provides detailed information about the 8 neuron types within each barrel and **will be included in the supplementary materials**：\n\n|Abbreviation|&emsp;&emsp;&emsp;&emsp;&emsp;Scientific Name|&emsp;Type|&emsp;&ensp;Location|&emsp;References|\n|:---|:---:|:---:|:---:|:---:|\n| L2P | L2 pyramidal neurons | Excitatory | Upper Layer 2/3 | [1,2,3,4] |\n| L3P | L3 pyramidal neurons | Excitatory | Lower Layer 2/3 | [4,5,6] |\n| Pyr | Star pyramidal and pyramidal neurons | Excitatory | Layer 4 | [7,8,9,10,11] |\n| SSP | Spiny stellate pyramidal neurons | Excitatory | Layer 4 | [6,7,8,12] |\n| STP | Slender-tufted pyramidal neurons | Excitatory | Layer 5a | [13,5,14] |\n| TTP | Thick-tufted pyramidal neurons | Excitatory | Layer 5b | [15,16,17,18,19] |\n| CTP | Cortico-thalamic pyramidal neurons | Excitatory | Layer 6a | [20, 21, 22,23] |\n| CCP | Cortico-cortical pyramidal neurons | Excitatory | Layer 6a | [21, 23, 24,25] |\n\nThe article \"*Neuronal Circuits in Barrel Cortex for Whisker Sensory Perception, 2021*\" offers an overall view on the barrel column structure. We constructed a connectivity matrix (Fig. 2A) based on the projection preferences of these neuron types reported in neuroscience studies, with connection weights automatically updated during training.\n\n### **References:**\n \n[1] Van Brederode JF et al., Morphological and electrophysiological properties of atypically oriented layer 2 pyramidal cells of the juvenile rat neocortex, 2000.  \n[2] Bureau I et al., Interdigitated paralemniscal and lemniscal path-ways in the mouse barrel cortex, 2006.  \n[3] Staiger JF et al., A gradual depth-dependent change in connectivity features of supragranular pyramidal cells in rat barrel cortex, 2015.  \n[4] Sermet BS et al., Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex, 2019.  \n[5] Lefort S et al., The excitatory neuronal networkof the C2 barrel column in mouse primary somatosensory cortex, 2009.  \n[6] Petersen CC et al., Functionally independent columns of rat somatosensory barrel cortex revealed with voltage-sensitive dye imaging, 2001.  \n[7] Egger V et al., Subcolumnar dendritic and axonal organization of spiny stellate and star pyramid neurons within a barrel in rat somatosensory cortex, 2008.  \n[8] Staiger JF et al., Functional diversity of layer IV spiny neurons in rat somatosensory cortex: quantitative morphology of electrophysiologically characterized and biocytin labeled cells, 2004.  \n[9] Schubert D et al., Cell type-specific circuits of cortical layer IV spiny neurons, 2003.  \n[10] Brecht M et al., Dynamic representation of whisker deflection by synaptic potentials in spiny stellate and pyramidal cells in the barrels and septa of layer 4 rat somatosensory cortex, 2002.  \n[11] Moore CI et al., Spatio-temporal subthreshold receptive fields in the vibrissa representation of rat primary somatosensory cortex, 1998.  \n[12] Feldmeyer D et al., Reliable synaptic connections between pairs of excitatory layer 4 neurons within a single ‘barrel’ of developing rat somatosensory cortex, 1999.  \n[13] Frick A et al., Monosynaptic connections between pairs of L5A pyramidal neurons in columns of juvenile rat somatosensory cortex, 2008.  \n[14] Oberlaender M et al., Three-dimensional axon morphologies of individual layer 5 neurons indicate celltype-specific intracortical pathways for whisker motion and touch, 2011.  \n[15] Chagnac-Amitai Y et al., Burst generating and regular spiking layer5 pyramidal neurons of rat neocortex have different morphological features, 1990.  \n[16] Connors BW et al., Electrophysiological properties of neocortical neurons in vitro, 1982.  \n[17] Le Be J-V et al., Morphological, electrophysiological, and synaptic properties of corticocallosal pyramidal cells in the neonatal rat neocortex, 2007.  \n[18] Markram H, A network of tufted layer 5 pyramidal neurons, 1997.  \n[19] Molnár Z et al., Towards the classification of subpopulations of layer V pyramidal projection neurons, 2006.  \n[20] Kumar P et al., Inter- and intralaminar subcircuits of excitatory and inhibitory neurons in layer 6a of the rat barrel cortex, 2008.  \n[21] Pichon F et al., Intracortical connectivity of layer VI pyramidal neurons in the somatosensory cortex of normal and barrelless mice, 2012.  \n[22] Qi G et al., Dendritic target region-specific formation of synapses between excitatory layer 4 neurons and layer 6 pyramidal cells, 2016.  \n[23] Zhang ZW et al., Intracortical axonal projections of lamina VI cells of the primary somatosensory cortex in the rat: a single-cell labeling study, 1997.  \n[24] Narayanan RT et al., Beyond columnar organization: cell type- and target layer-specific principles of horizontal axon projection patterns in rat vibrissal cortex, 2015.  \n[25] Mercer A et al., Excitatory connections made by presynaptic cortico-cortical pyramidal cells in layer 6 of the neo-cortex, 2005."}, {"review_id": "d7Aae4gBKV", "reviewer": "Reviewer_s3by", "summary": "This paper proposes a barrel cortex-inspired modular network with localist topographic expert routing for sensorimotor processing. The architecture strictly adheres to biological constraints (one-to-one \"whisker-barrel\" mapping, vertical/horizontal microcircuits) and achieves SOTA performance on 3D tactile tasks with 97% parameter reduction. While biologically innovative and technically sound, broader validation and theoretical grounding are needed for stronger impact.", "questions": "1. Can the architecture maintain its efficiency/performance advantages on non-tactile tasks?\nexample: \nTest on two new task types:\n(a) Sensorimotor: Audio event classification (e.g., DCASE datasets) using spectrogram \"topography\"\n(b) Non-sensorimotor: Image classification (e.g., CIFAR-10) with spatial patch → expert mapping\n\n2. Do biological constraints provide tangible benefits over standard MoEs?\nexample:\nAdd 3 baselines with matched params:\n(a) Switch Transformer (global routing)\n(b) Top-k MoE with fixed neighbors (non-adaptive)\n(c) Dense model (all experts active)\n\n3. Which constraints are essential vs. redundant?\nexample: \nRun ablations:\n(a) Remove topographic routing → random neighbor connections\n(b) Remove vertical microcircuits → replace with MLP\n(c) Remove parameter sharing → independent weights\nMeasure performance drop on EvTouch-Containers\n\n4. Does performance degrade with scaled sensor arrays?\nexample: \nSimulate larger arrays (78/156 sensors) via:\n(a) Sensor duplication + noise\n(b) Zero-padding unused inputs", "limitations": "The authors should more systematically explore the conditions under which their method works effectively and where it may fail. This enhances understanding and application of the model by other researchers and guides future research. For instance, experimenting with different input data types can analyze the model's robustness and adaptability. Also, studying the model's performance under extreme conditions or with abnormal inputs can reveal its potential limitations.\n\nRegarding the computational overhead from biological constraints, the authors should conduct an in - depth analysis of the increased computational overhead from the biological constraints in the model design. This includes evaluating their impact on computational resource requirements and model efficiency during training and inference. To boost the model's practicality, methods to optimize computational processes and reduce resource consumption while maintaining biological plausibility should be explored.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. Novel Bio-Inspired Architecture:\nFaithfully translates rodent barrel cortex principles (somatotopic organization, laminar microcircuits) into a modular MoE-like system (§3.1–3.2).\n\n2. Localist routing via KNN gating effectively suppresses inter-module correlations, mirroring cortical lateral inhibition (§4.3, Fig. 5).\n\n3. 97% parameter reduction via columnar weight sharing (§3.3) while improving accuracy over baselines (+2.1–11.7% on EvTouch datasets, Table 1).\n\n4. Loss landscape analysis confirms enhanced training stability (§4.2, Fig. 3B).\n\n5. Spatiotemporal activation patterns align with optogenetic studies (§3.2, Fig. 2C-D).\n\n6. Ablation studies validate neuroanatomical principles (L4→L2/3 dominance, Fig. 4C).\n\nWeaknesses \n1. Limited Generalizability (Critical):\nEvaluated only on tactile tasks (EvTouch). Unclear if the architecture generalizes to other modalities (e.g., vision/audio) or non-sensorimotor domains.\n\n2. Insufficient Comparison with MoE Baselines:\nLacks direct comparison to standard MoEs (e.g., Switch Transformer) to quantify benefits of biological constraints (routing efficiency, expert diversity).\n\n3. Methodological Clarity Issues:\nUndefined tensor dimensions (e.g., Eqs in §3.2) and incomplete figure annotations (Fig. 2 temporal evolution).\n\n4. Inadequate explanation for why biological constraints improve performance beyond empirical results.", "was_revised": true, "final_justification": "The study demonstrates impressive innovation; however, it would benefit from additional exploration of the optimal trade-offs between simplicity and biological fidelity. In summary, while the work is acceptable and merits publication, it exhibits certain limitations that prevent it from achieving spotlight-level recognition.", "rebuttal": "Thank you for your careful review and valuable comments. We will improve the relevant descriptions in the revised manuscript (e.g., the equations in Sec. 3.2 and the caption for Fig. 2). Below, we would like to respond to your questions point by point.\n\n> Q1: Can the architecture maintain its efficiency/performance advantages on non-tactile tasks? example: (a) Audio event classification... (b) Image classification...\n\n**A1:** In the early stages of exploring the single barrel column structure, we conducted preliminary tests on visual datasets (MNIST and Fashion-MNIST) and an auditory dataset (SHD). However, key challenges arose when extending the model to a multi-barrel design: the central concept of the multi-barrel model relies on the one-to-one \"whisker-barrel\" mapping, which is absent in visual and auditory datasets. While the alternatives you proposed, such as using *\"spatial patches\"* or *\"spectrograms\"*, **are technically feasible, they raise the following conceptual concerns:** \n1. Do visual and auditory signals possess a natural modular topological structure, similar to tactile signals?  \n2. Are visual and auditory signals primarily reliant on local dependencies? Long-range dependencies are also common.  \n\nThese questions suggest that the proposed localist routing mechanism may not immediately align with visual and auditory datasets. In fact, different cortical regions have evolved distinct structures to handle tasks across different modalities. For example, the barrel cortex is specialized for processing tactile signals, whereas the auditory cortex has no reported discrete modular structures resembling barrels.\n\nBut, we are not dismissing the value of extending the model to other domain tasks. On the contrary, such challenges inspire the abstraction of broader principles. For instance, while single samples in certain domains may lack an inherently modular structure, could their distribution in semantic space allow for natural clustering and measurable distances? Similarly, the localist routing mechanism in the barrel cortex intuitively bears conceptual similarity to the sliding window operation employed in convolutional neural networks. Overall, we agree the importance of investigating the model's generalizability to other modalities, but this lies somewhat beyond the scope of this paper's specific focus.\n\n***\n\n> Q2: Do biological constraints provide tangible benefits over standard MoEs? Add 3 baselines (a) Switch Transformer...(b) Top-k MoE... (c) Dense model...\n\n**A2:** Following this valuable suggestion, **we added two MoE baseline models for comparison**. Considering that Switch Transformer is primarily designed for NLP tasks, we adapted standard simple MoE architectures — Top-2 MoE and Dense MoE — for tactile tasks. The gating network assigns signals from individual sensors to corresponding experts, with each expert designed as a 2-layer MLP containing 256 neurons in the hidden layer. Computation is performed independently across the time dimension. Multiple configurations with varying numbers of experts were tested, and each configuration was repeated three times. The best results are as follows:\n\n| Datasets |&#124;  | Top-2 MoE | |&#124; | Dense MoE | |&#124; Ours |\n|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n|          | 16 experts | 32 experts | 48 experts | 16 experts | 32 experts | 48 experts |      |\n| EvTouch-Objects | 88.89 | 90.28 | 90.97 | 90.28 | 89.58 | 88.89 | **94.44** |\n| EvTouch-Containers | 70 | 73.33 | 73.33 | 66.67 | 75 | 70 | **86.67** |\n\nAs shown, both MoE baseline models perform markedly worse than our multi-barrel model across the tactile datasets. This disparity may arise from a lack of alignment between the simple MoEs and the characteristics of tactile data, as our model leverages a cortical-inspired one-to-one mapping. Additionally, the performance gap could be attributed to the limited representational capacity of the MLP units within the MoE experts.\n\nHowever, integrating localist routing into standard MoEs is challenging, as mapping the spatial topology of sensors to experts in a way that establishes meaningful neighborhood relationships is difficult. Achieving this would require adopting the one-to-one mapping from our multi-barrel model, but doing so would make it equivalent to the experiments in Q3(b), where the barrels in our model are replaced with MLPs. **We will detail these results in our response to Q3 (A3).**\n\nWe believe you may be more interested in the performance of localist routing in large-scale MoEs on LLM benchmarks—a direction we are also eager to explore. However, this requires addressing critical questions: How should expert distances be defined in standard MoEs, perhaps in semantic space? And how should expert neighborhoods be structured, potentially through expert grouping?\n\nOur paper aims to highlight the key differences between AI expert systems (MoEs) and natural intelligence systems (e.g., the barrel cortex). Current MoE studies focus on task allocation, while the barrel cortex exhibits unique mechanisms of cooperation and competition. As future work, we aim to test brain-inspired MoE architectures on LLM benchmarks, though this would be another whole different work.\n\n***\n\n> Q3: Which constraints are essential vs. redundant? example: Run ablations: (a)...(b)... (c)...\n\n**A3:** We agree that ablation experiments on biological constraints are important. Below are the detailed results for the three experimental configurations you suggested.\n\n**(a) Remove topographic routing → random neighbor connections**  \nFollowing the suggestion, we kept the number of neighbors fixed at 4, replaced horizontal localist routing with random connections, and trained each model three times, reporting the best results in the table below. According to the results, this replacement caused performance drops of 4.2% and 3.3%, respectively. This is likely because object textures often exhibit spatial patterns, and localist routing preserves neighborhood information, enabling better integration of spatially correlated signals.  In contrast, random connections disrupt spatial organization, add noise, and weaken the model’s ability to capture meaningful relationships.  By leveraging the spatial coherence of tactile data, localist routing aligns better with the structured nature of sensory inputs, making it more effective.\n\n| Datasets | &#124; Random connection &#124; | Localist routing |\n|:---|:---:|:---:|\n|EvTouch-Objects|90.28|**94.44**|\n|EvTouch-Containers|83.33|**86.67**|\n\n**(b) Remove vertical microcircuits → replace with MLP**  \nAs suggested, we replaced each barrel column with MLPs of varying depths, each hidden layer containing 256 neurons. The model was trained three times, and the best results are recorded as follows:\n\n| Datasets | &#124; 2-layer MLP &#124; | 3-layer MLP &#124; | 4-layer MLP &#124; | Biological barrel |\n|:---|:---:|:---:|:---:|:---:|\n|EvTouch-Objects|89.58|88.89|88.89|**94.44**|\n|EvTouch-Containers|83.33|83.33|81.67|**86.67**|\n\nIt is clear that replacing biologically constrained barrels with MLPs, regardless of the number of layers, leads to a substantial performance decline. This is likely due to the fact that biologically inspired recurrent spiking neural networks possess more complex temporal dynamics, enabling them to capture intricate time dependencies. And, their natural capacity to retain historical states makes them more effective for sequential tasks. \n\nFurthermore, **we evaluated the effectiveness of incorporating localist routing (With LR) vs. without localist routing (No LR) within MLP units**. The average results of three repeated experiments are shown in the table below. Overall, integrating this mechanism demonstrated better performance, particularly on the more challenging EvTouch-Containers dataset, indicating the generalizability of the localist routing mechanism.\n\n| Datasets | 2-layer MLP (No LR) | 2-layer MLP (With LR) | 3-layer MLP (No LR) | 3-layer MLP (With LR) | 4-layer MLP (No LR) | 4-layer MLP (With LR) |\n|---|:---:|:---:|:---:|:---:|:---:|:---:|\n| EvTouch-Objects | **90.74** | 88.87 | 88.193 | **88.89** | 88.43 | **88.66** |\n| EvTouch-Containers | 76.67 | **82.78** | 72.22 | **81.67** | 72.78 | **80.56** |\n\n**(c) Remove parameter sharing → independent weights Measure performance drop on EvTouch-Containers**  \nFig. 3A-B in the paper show the results of shared parameters and independent parameters on both EvTouch-Objects and EvTouch-Containers datasets. Independent parameters led to performance drops of 9.5% and 14.5%, respectively. **We will revise Fig. 3 to make the content more intuitive and clear.**\n\n***\n\n> Q4: Does performance degrade with scaled sensor arrays? example: Simulate larger arrays (78/156 sensors) via: (a) Sensor duplication + noise (b) Zero-padding unused inputs\n\n**A4:** We agree that scaling analysis is an insightful extension with potential applications in tactile scenarios like artificial skin. While feasible in principle, this experiment has limitations: the two tasks in the paper rely on publicly available datasets (*\"TactileSGNet: A Spiking Graph Neural Network for Event-Based Tactile Object Recognition,\" 2020*), and no real data from a scaled sensor array is available. The two alternatives you proposed, *\"Sensor duplication + noise\"* and *\"Zero-padding unused inputs,\"* can simulate signals from additional sensors, but another challenge arises—estimating the spatial coordinates of these sensors. Localist routing relies on constructing the sensor's spatial topology, which introduces additional assumptions.\n\nReturning to your original question (*\"Does performance degrade with scaled sensor arrays?\"*), **we believe the answer is affirmative**, even without conducting additional experiments. Adding noise without increasing useful information is expected to reduce performance. We appreciate this thoughtful suggestion and plan to explore larger-scale tactile datasets in future work."}]

Author rebuttal:

Meta-review: Inspired by the rodent barrel cortex, this work introduces a modular neural network where sensory inputs are routed to specialized, locally-connected expert modules that mimic cortical columns.  This model achieves strong performance on tactile classification tasks with few parameters, and improved training stability.

The reviewers agree that this is interesting work at the intersection of neuroscience and machine learning. Reviewer NTKp raised some concerns regarding overclaiming the importance of the current findings with respect to state-of-the-art artificial neural networks. Given the current limited evaluation of the model (which is appropriate for such a first paper), I would ask the authors to manage this line carefully when writing the final version of their paper.
Decision: Accept (poster)