Abstract
One of the most fundamental organizational principles of the brain is the separation of excitatory (E) and inhibitory (I) neurons. In addition to their opposing effects on post-synaptic neurons, E and I cells tend to differ in their selectivity and connectivity. Although many such differences have been characterized experimentally, it is not clear why they exist in the first place. We studied this question in an artificial neural network equipped with multiple E and I cell types. We found that a deep convolutional recurrent network trained to perform an object classification task was able to capture salient distinctions between E and I neurons. We explored the necessary conditions for the network to develop distinct selectivity and connectivity across cell types. We found that neurons that project to higher-order areas will have greater stimulus selectivity, regardless of whether they are excitatory or not. Sparser connectivity is required for higher selectivity, but only when the recurrent connections are excitatory. These findings demonstrate that the differences observed across E and I neurons are not independent, and can be explained using a smaller number of factors.
