Mixed Signal Learning by Spike Correlation Propagation in Feedback Inhibitory Circuits

Naoki Hiratani, Tomoki Fukai

Abstract: Author Summary In natural environments, although sensory inputs are often highly mixed with one another and obscured by noise, animals can detect and learn discrete signals from this mixture. For example, humans easily detect the mention of their names from across a noisy room, a phenomenon known as the cocktail party effect. Spike-timing-dependent plasticity (STDP) is a learning mechanism ubiquitously observed in the brain across various species and is considered to be the neural basis of such learning; however, it is still unclear how STDP enables efficient learning from uncertain stimuli and whether spike-based learning offers benefits beyond those provided by standard machine learning methods for signal decomposition. To begin to answer these questions, we conducted analytical and simulation studies examining the propagation of spike correlation in feedback neural circuits. We show that non-precise spike correlation is useful for handling noise during the learning process. Our results also suggest that neural circuits make use of stochastic membrane dynamics to approximate computationally complex Bayesian learning algorithms, progressing our understanding of the principles of stochastic computation by the brain.