R. Legenstein, Z. Jonke, S. Habenschuss, and W. Maass
Previous theoretical studies on the interaction of excitatory and inhibitory
neurons proposed to model this cortical microcircuit motif as a so-called
Winner-Take-All (WTA) circuit. A recent modeling study however found that the
WTA model is not adequate for data-based softer forms of divisive inhibition
as found in a microcircuit motif in cortical layer 2/3. We investigate here
through theoretical analysis the role of such softer divisive inhibition for
the emergence of computational operations and neural codes under spike-timing
dependent plasticity (STDP). We show that in contrast to WTA models - where
the network activity has been interpreted as probabilistic inference in a
generative mixture distribution - this network dynamics approximates
inference in a noisy-OR-like generative model that explains the network input
based on multiple hidden causes. Furthermore, we show that STDP optimizes the
parameters of this model by approximating online the expectation maximization
(EM) algorithm. This theoretical analysis corroborates a preceding modelling
study which suggested that the learning dynamics of this layer 2/3
microcircuit motif extracts a specific modular representation of the input
and thus performs blind source separation on the input statistics.