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Modelling Predictive Coding in the Primary Visual Cortex (V1): Layer 2/3 Circuits for Prediction Error Computation through Compartmentalized Spiking Neurons

Created on 04 Nov 2025

Authors

Nemati, E., Davey, C. E., Meffin, H., Burkitt, A. N.

Abstract

Cortical Layer 2/3 has been consistently implicated as the locus of prediction-error signalling in hierarchical models of cortical sensory processing. However, the circuit mechanisms that generate biologically plausible prediction-error (PE) signals remain elusive. A spiking network model is presented here in which two-compartment excitatory pyramidal neurons interact with three inhibitory subtypes: parvalbumin-expressing (PV), somatostatin-expressing (SOM), and vasoactive-intestinal-peptide-expressing (VIP) interneurons,to compute sign-specific prediction errors (positive and negative PEs). Feedforward input targets the soma, whereas top-down feedback reaches the distal apical dendrite, enabling a local somato-dendritic comparison. A PE emerges whenever the balance between excitation and inhibition is selectively disrupted within one compartment, recruiting either positive-error (PE+) or negative-error (PE-) subpopulations of pyramidal neurons. Unlike prior learning-dependent, rate-based accounts, this fixed-weight spiking circuit shows that bidirectional PE signals (PE+ and PE) can arise online from compartment-specific balance without any synaptic weight updates. The model reproduces key experimental observations, including sparse mismatch responses, compartment-specific inhibition, and VIP-mediated disinhibition. Across four canonical sensory-prediction configurations, the circuit maintains a tight balance during matched input and generates bidirectional PE signals only under mismatch. By routing sensory drive from Layer 4 into Layer 2/3 and allowing the resulting PE activity to project toward deeper feedback generators, the model situates Layer 2/3 as a dedicated, feature-specific mismatch detector within a hierarchical inference network. These results provide a mechanistic bridge from dendritic computation to laminar predictive coding, demonstrating how realistic spiking dynamics can implement fast, sign-specific PE signaling without learning.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 04 Nov 2025.

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