Background: dynamics of neural decision making

A fundamental challenge in neuroscience is to understand how decisions are computed in neural circuits. One popular approach to this problem is to record from single neurons in brain regions that lie between primary sensory and motor regions while an animal performs a perceptual decision-making task. Typical tasks require the animal to integrate noisy sensory evidence over time in order to make a binary decision about the stimulus. Such experiments have the tacit goal of characterizing the dynamics governing the transformation of sensory information into a representation of the decision. However, recorded spike trains do not reveal these dynamics directly; they represent noisy, incomplete emissions that reflect the underlying dynamics only indirectly.

This dissociation between observed spike trains and the unobserved dynamics governing neural population activity has posed a key challenge for using neural measurements to gain insight into how the brain computes decisions. Recording decision-related neural activity has certainly shed much light upon what parts of the brain are involved in forms of decision making and what sorts of roles each area plays. But without direct access to the dynamics underlying single-trial decision formation, most analyses of decision-related neural data rely on estimating spike rates by averaging over trials (and sometimes, over neurons as well). Although the central tendency is of course a reasonable starting point in data analysis, sole reliance on the mean can obscure single-trial dynamics when substantial stochastic components are present. For example, as discussed in depth in this chapter, the average of a set of step functions – when the steps occur at different times on different trials – will yield an average that ramps continuously, masking the presence of discrete dynamics. Although the majority of averaging and regression-based analyses used in the field are straightforward to conceptualize and easy to apply to data, they provide limited insight into the dynamics that may govern how individual decisions are made. State space methods, on the other hand, are particularly well-suited for analyzing the neural representation of decisions (or other cognitive variables). The latent state can account for unobserved, trial varying dynamics, and the dynamics placed on the state can be directly linked to models of decision making.