Decision making and perceptual bistability in spike-based neuromorphic VLSI systems

Understanding how to reproduce robust and reliable decision making behavior in neuromorphic systems can be useful for developing information processing architectures in subthreshold analog circuits as well as future emerging nano-technologies, that comprise inhomogeneous and unreliable components. To this end, we explore the computational properties of a recurrent neural network, implemented in a custom mixed signal analog/digital neuromorphic chip, for realizing perceptual decision-making, bi-stable perception, and working memory. The chip comprises conductance-based integrate-and-fire neurons and configurable synapses with realistic dynamics. These circuits are configured to implement a recurrent neural network, composed of excitatory and inhibitory pools of silicon neurons coupled with local excitation and global inhibition. We show how the interplay between excitation and inhibition produces competitive winner-take-all dynamics, which is a feature of decision-making and persistent activity models, and demonstrate that the system generates reliable dynamics capable of reproducing both neuro-physiological data and psycho-physical performances in coding and collective distributed computation.