Mixed Signal Learning by Spike Correlation Propagation in Feedback Inhibitory Circuits

The brain can learn and detect mixed input signals masked by various types of noise, and spike-timing-dependent plasticity (STDP) is the candidate synaptic level mechanism. Because sensory inputs typically have spike correlation, and local circuits have dense feedback connections, input spikes cause the propagation of spike correlation in lateral circuits; however, it is largely unknown how this secondary correlation generated by lateral circuits influences learning processes through STDP, or whether it is beneficial to achieve efficient spike-based learning from uncertain stimuli. To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve. We derive analytical conditions at which neurons detect minor signals with STDP, and show that depending on the origin of the noise, different correlation timescales are useful for learning. In particular, we show that non-precise spike correlation is beneficial for learning in the presence of cross-talk noise. We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. In addition, we demonstrate that the model performs blind source separation in a manner similar to the sequential sampling approximation of the Bayesian independent component analysis algorithm. Our results provide a basic understanding of STDP learning in feedback circuits by integrating analyses from both dynamical systems and information theory.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.