Searching for Principles of Brain Computation

Abstract

Highlights O_LIHints for computational principles from experimental data\nC_LIO_LIComputational role of diverse network components\nC_LIO_LIEmergence and computational role of assemblies\nC_LIO_LIProbabilistic inference through stochastic network dynamics\nC_LIO_LIOngoing network rewiring and compensation through synaptic sampling\nC_LI\n\nAbstractExperimental methods in neuroscience, such as calcium-imaging and recordings with multielectrode arrays, are advancing at a rapid pace. They produce insight into the simultaneous activity of large numbers of neurons, and into plasticity processes in the brains of awake and behaving animals. These new data constrain models for neural computation and network plasticity that underlie perception, cognition, behavior, and learning. I will discuss in this short article four such constraints: Inherent recurrent network activity and heterogeneous dynamic properties of neurons and synapses, stereotypical spatio-temporal activity patterns in networks of neurons, high trial-to-trial variability of network responses, and functional stability in spite of permanently ongoing changes in the network. I am proposing that these constraints provide hints to underlying principles of brain computation and learning.