bioRxiv · 10.1101/2023.02.28.530384
A biologically plausible decision-making model based on interacting cortical columns
Abstract
We present a novel decision-making model with two populations. Each population is composed of Regularly Spiking (excitatory) and Fast Spiking (inhibitory) cells in cortical layer 2/3. Each population votes for one of the two visual alternatives shown on a monitor in human and macaque experiments. The model is biophysically plausible since it is based on long-range cortico-cortical connections between the layer 2/3 populations. These connections are excitatory. They contact both Regularly Spiking and Fast Spiking cells. This long-range excitation is conflicted by an inhibition based on local connections within the populations. This configuration introduces a competition between the layer 2/3 populations, sufficient for making a decision to choose between two alternatives shown on the monitor. We integrate the model with a reward-driven learning mechanism. This allows the model to learn the optimal strategy maximizing the cumulative reward in the long term. We test the model on two decision-making tasks applied on human and macaque. This model elaborates certain biophysical details which were not considered by simpler phenomenological models proposed for similar decision-making tasks. Finally, the model can be embedded in a brain simulator such as The Virtual Brain to study decision-making in terms of large-scale brain dynamics.
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Baspinar, E., Cecchini, G., DePass, M., Andujar, M., Pani, P., Ferraina, S., Moreno-Bote, R., Cos, I., Destexhe, A.. 2023-03-01. A biologically plausible decision-making model based on interacting cortical columns. https://doi.org/10.1101/2023.02.28.530384
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