How the layer-dependent ratio of excitatory to inhibitory cells shapes cortical coding in balanced networks

Abstract

The cerebral cortex exhibits a sophisticated neural architecture across its six layers. Recently, it was found that these layers exhibit different ratios of excitatory to inhibitory (EI) neurons, ranging from 4 to 9. This ratio is a key factor for achieving the often reported balance of excitation and inhibition, a hallmark of cortical computation. However, neither previous theoretical nor simulation studies have addressed how these differences in EI ratio will affect layer-specific dynamics and computational properties. We investigate this question using a sparsely connected network model of excitatory and inhibitory neurons. To keep the network in a physiological range of firing rates, we varied the inhibitory firing threshold or the synaptic strength between excitatory and inhibitory neurons. We find that decreasing the EI ratio allows the network to explore a higher-dimensional space and enhance its capacity to represent complex input. By comparing the empirical EI ratios of layer 2/3 and layer 4 in the rodent barrel cortex, we predict that layer 2/3 has a higher dimensionality and coding capacity than layer 4. Furthermore, our analysis of primary visual cortex data from the Allen Brain Institute corroborates these modelling results, also demonstrating increased dimensionality and coding capabilities of layer 2/3.Author summaryExperimental studies indicate that the ratio of excitatory to inhibitory neurons varies across different cortical layers. In this study, we investigate how these varying excitatory-to-inhibitory (EI) ratios affect the layer-specific dynamics and computational capacity of cortical networks. We modeled a randomly connected network of spiking neurons, incorporating different EI ratios based on experimental observations. Our findings reveal that as the influence of inhibition increases, corresponding to lower EI ratios, the network explores a higher dimensionality in its activity, thereby enhancing its capacity to encode high-dimensional inputs. These results align with our analysis of experimental data recorded from layers 2/3 and layer 4 of the rodent primary visual cortex. Specifically, our findings support the hypothesis that layer 2/3, which has a lower EI ratio compared to layer 4, possesses a greater computational capacity.