Fluctuation-driven Neurodynamics: A model of Decision-Making

The problem of decision-making has become the center of interest of many neuroscientists aiming to understand the neural basis of intelligent behavior by linking perception and action. Behavioral, neurophysiological, and theoretical studies are converging to a common theory that assumes an underlying diffusion process which integrates both the accumulation of perceptual and cognitive evidence for making the decision and motor choice in one unifying neural network. In particular, a number of recent neurophysiological experiments are providing information on the neural mechanisms underlying decision-making, by analyzing the responses of neurons that correlate with the animal's behavior. In this talk, we analyse computational models of decision-making involving populations of excitatory neurons engaged in competitive interactions mediated by inhibition. Sensory input may bias the competition in favor of one of the populations, resulting in a gradually developing decision in which neurons in the chosen population exhibit increased activity while other populations are inhibited. In this scenario spontaneous, non-selective network activity and the decision state, in which one population has been activated, both represent stable solutions of the underlying equations, i.e. they are multistable. Decision-making is then understood as the fluctuation-driven, probabilistic transition from the spontaneous to the decision state.