Modelling Predictive Coding in the Primary Visual Cortex (V1): Layer 2/3 Circuits for Prediction Error Computation through Compartmentalized Spiking Neurons
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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.
Author summary
In this study, we present a biologically grounded spiking model of layer 2/3 in primary visual cortex within the predictive coding framework. Our goal is to explain how superficial cortical circuits compute fast, sign-specific prediction errors when sensory input does not match top-down expectations. The model uses two-compartment pyramidal neurons whose somata receive feedforward drive from layer 4 while apical dendrites receive feedback, together with three key inhibitory interneuron classes, parvalbumin-expressing (PV), somatostatin-expressing (SOM), and vasoactive-intestinal-peptide-expressing (VIP), that provide compartment-specific inhibition and disinhibition. When input and prediction match, excitation and inhibition remain tightly balanced and activity is sparse; when they differ, this balance is transiently broken in the appropriate compartment, and distinct populations signal either a positive error (unexpected presence) or a negative error (unexpected absence). The circuit reproduces several in vivo observations in layer 2/3, including sparse mismatch responses, compartment-specific inhibition, and VIP-mediated disinhibition, and it does so without requiring synaptic weight changes. By routing feature-selective signals from layer 4 into layer 2/3 and relaying the resulting errors toward deeper layers, the model positions layer 2/3 as a local, feature-specific mismatch detector in a hierarchical system. This work provides a concrete, testable mechanism linking dendritic computation, inhibitory diversity, and predictive coding in the cortex.
