Abstract
The spike activity of cells in some cortical areas has been found to be correlated with reaction times and behavioral responses during two-choice decision tasks. These experimental findings have motivated the study of biologically plausible winner-take-all network models, in which strong recurrent excitation and feedback inhibition allow the network to form a categorical choice upon stimulation. Choice formation corresponds in these models to the transition from the spontaneous state of the network to a state where neurons selective for one of the choices fire at a high rate and inhibit the activity of the other neurons. This transition has been traditionally induced by an increase in the external input that destabilizes the spontaneous state of the network and forces its relaxation to a decision state. Here we explore a different mechanism by which the system can undergo such transitions while keeping the spontaneous state stable, based on an escape induced by finite-size noise from the spontaneous state. This decision mechanism naturally arises for low stimulus strengths and leads to exponentially distributed decision times when the amount of noise in the system is small. Furthermore, we show using numerical simulations that mean decision times follow in this regime an exponential dependence on the amplitude of noise. The escape mechanism provides thus a dynamical basis for the wide range and variability of decision times observed experimentally.
Highlights
Over the last decade several experimental groups have identified neurons in association areas that participate in the decision making process
Electrophysiological recordings in the lateral intraparietal (LIP) area of macaque monkeys during random dot motion discrimination tasks have revealed that the activity of LIP neurons is correlated to the subject’s choice and reaction time [1,2] and is causally related to the decision formation [3]
For small noise a decrease in the mean input to the network leads to an increase of the positive skewness of decision-time distributions. These results suggest that noise-driven decision models provide an alternative dynamical mechanism for the variability and wide range of decision times observed, which span from a few hundreds milliseconds to more than one second [15,2]
Summary
Over the last decade several experimental groups have identified neurons in association areas that participate in the decision making process. Sensory moment-bymoment evidences, like those provided by MT cells that are selective to either of the two target directions in a random dot task [10], are modeled with specific external inputs to the competing populations. The activation of these inputs forces the network to change its state from a spontaneous activity state, in which both subpopulations show low firing activity, to an activated state, in which one of the subpopulations fires at a significantly higher rate than the other. The presence of noise in the system makes network decisions random
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