Neural dynamics and circuit mechanisms of decision-making

Abstract

In this review, I briefly summarize current neurobiological studies of decision-making that bear on two general themes. The first focuses on the nature of neural representation and dynamics in a decision circuit. Experimental and computational results suggest that ramping-to-threshold in the temporal domain and trajectory of population activity in the state space represent a duality of perspectives on a decision process. Moreover, a decision circuit can display several different dynamical regimes, such as the ramping mode and the jumping mode with distinct defining properties. The second is concerned with the relationship between biologically-based mechanistic models and normative-type models. A fruitful interplay between experiments and these models at different levels of abstraction have enabled investigators to pose increasingly refined questions and gain new insights into the neural basis of decision-making. In particular, recent work on multi-alternative decisions suggests that deviations from rational models of choice behavior can be explained by established neural mechanisms.

Descending Control of Neural Bias and Selectivity in a Spatial Attention Network: Rules and Mechanisms

“…In each of these areas, subtypes of neurons respond with differentially high response rates when the stimulus inside their spatial receptive field is selected as the target for decision. Computational modeling work has characterized selection in terms of a race-to-bound process or one-dimensional decay process to account for the dynamics of neural responses during selection tasks (Ganguli et al, 2008; Gold and Shadlen, 2007; Smith and Ratcliff, 2004; Wang, 2012). Other models have invoked recurrent interactions along with feedback inhibition (Machens et al, 2005), including the potential contribution of multiple brain areas (Wang, 2008).…”

The Challenge of Understanding the Brain: Where We Stand in 2015

“…Because the distance moved is proportional to external input, this network is capable of integration (Zhang, 1996). For use of such bumps during decisions, see (Wang, 2012). E. Cumulative integrator.…”

History-dependent variability in population dynamics during evidence accumulation in cortex

“…In cortical microcircuits, ongoing activity patterns are combined with new inputs to perform many complex neural computations, including evidence accumulation during decision-making 1,2 . To understand how ongoing activity is combined with external inputs, considerable focus has been placed on the posterior parietal cortex (PPC) 3,4 , which is thought to be necessary for visual decision-making tasks in rodents 5-8 .…”