Tompos, T. | bioRxiv Science

bioRxiv2025

From Skin to Cortex: End-to-End Spiking Neural Network Simulation of Tactile Information Flow

Autonomous systems and neuroprosthetic devices demand real-time tactile processing under strict energy and latency constraints. Designing these systems using neuromorphic principles, where communication is event-based and node activity is sparse, could improve their speed and energy efficiency. Here we present an end-to-end spiking neural network model of the ascending tactile pathway, from mechanoreceptors in the skin in humans (or whisker follicles in rodents) to cortical neurons, that operates in an event-driven neuromorphic fashion. The model comprises distinct anatomical stages, (1) three types of mechanoreceptor afferents, (2) trigeminal ganglion, (3) brainstem, (4) thalamic nucleus, and (5) three cortical layers, connected in a feed-forward hierarchy. We demonstrate the models responses to both rodent whisker deflection and human fingertip skin displacement, using information-theoretic analysis, pairwise correlation, and stimulus decoding at each layer. Our results show that tactile information is efficiently encoded and transformed at each stage: stimulus features are represented with high fidelity and reduced redundancy as signals ascend. Notably, simple linear or Bayesian decoders can reliably classify stimulus features from single-neuron activity in the thalamus and cortex for low-noise inputs, highlighting the emergence of robust neural representations. This open-source model is the first to include the mechanosensory periphery in a full tactile pathway simulation, enabling researchers to study how perturbations at any stage affect tactile encoding. Moreover, the network is well-suited for deployment on low-power, real-time neuromorphic hardware, facilitating the development of multi-layer signal processing and tactile navigation algorithms.

neuroscience↗

bioRxiv2025

Stability and Adaptability in Balance: A Dual Mechanism for Metaplasticity in Cortical Networks

1.Circuit dynamics arise from the interaction between the networks connectivity structure and intrinsic neuronal nonlinearities, yet the roles of key structural parameters -- synaptic weight (W) and connection probability (P) -- are usually examined in simplified network models. Using a biologically grounded, multilayer spiking model of thalamocortical microcircuitry, incorporating conductance-based neurons and rodent somatosensory cortex connectivity, we systematically scaled W and P and identified four organising principles of population dynamics. First, stronger synapses monotonically amplified spiking across all populations. Second, increasing connection density produced a weight-dependent bidirectional outcome: adding weak synapses preserved baseline activity, whereas adding strong ones suppressed firing. Third, concurrent increases in W and P yielded sublinear effects, where population activity increased less than expected from the sum of their individual impacts. Fourth, two functional neuronal classes emerged -- scaling-invariant neurons that reliably transmitted thalamic input across connectivity regimes, and variant neurons that spiked selectively under specific connectivity scales. These classes differed in their excitation-inhibition balance, shaped by the strength of recurrent inhibition. Together, our findings show that synaptic weight and connection probability work in concert to define cortical operating regimes and generate the functional diversity in neuronal responses that supports flexible computation.

neuroscience↗