Cell type specific firing patterns in a V1 cortical column model depend on feedforward and feedback-driven states

Stimulation of specific cell groups under different network regimes (e.g., spontaneous activity or sensory-evoked activity) can provide insights into the neural dynamics of cortical columns. While these protocols are challenging to perform experimentally, modelling can serve as a powerful tool for such explorations. Using detailed electrophysiological and anatomical data from mouse V1, we built a novel spiking network model of a cortical column, which incorporates pyramidal cells and three distinct interneuron types (PV, SST, and VIP cells, specified per lamina), as well as the dynamic and voltage-dependent properties of AMPA, GABA, and NMDA receptors. We first demonstrate that thalamocortical feedforward (FF) and feedback (FB) stimuli arriving in the column have opposite effects, leading to net columnar excitation and inhibition respectively and revealing translaminar gain control via full-column inhibition by layer 6. We then perturb one cell group (i.e. a cell type in a specific layer) at a time and observe the effects on other cell groups under distinct network states: spontaneous, feedforward-driven, feedback-driven, and a combination of feedforward and feedback. Our findings reveal that when a given group is perturbed, the columnar response varies significantly based on its state, with strong sensory feedforward input decreasing columnar sensitivity to all perturbations and feedback input serving as modulator of intra columnar interactions. Given that activity changes within specific neuronal populations are difficult to predict a priori in experiments, our model may constitute a useful tool to predict outcomes of perturbations and assist in experimental design. Author SummaryIn this study, we explored how stimulating specific groups of neurons under different conditions (spontaneous or stimulus-evoked states) can help us understand the dynamics of visual cortical columns in mouse cortex. We developed a data-constrained model of a cortical column that includes several cell types (excitatory neurons as well as multiple inhibitory neuron types) and synaptic receptors. Our findings reveal that signals coming from higher brain areas (feedback input) and sensory input (feedforward input) have different impacts on the column: sensory inputs generally increase activity within the column, while feedback tends to decrease it. We then considered input targeting one cell type at a time to see how it affects the internal dynamics. Our results show that neural responses to selected input vary depending on whether the cortical column is in a resting state or being stimulated by feedforward input or feedback input. This model is a useful tool for predicting the outcomes of these cell interactions, which can help in planning real-world experiments and understanding how our brain processes information.