Measuring distributed properties of neural representations beyond the decoding of local variables: Implications for cognition

Introduction Neural representations are distributed. This means that more information can be gleaned from neural ensembles than from single cells. Modern recording technology allows the simultaneous recording of large neural ensembles (of more than 100 cells simultaneously) from awake behaving animals. Historically, the principal means of analyzing representations encoded within large ensembles has been to measure the immediate accuracy of the encoding of behavioral variables (“reconstruction”). In this chapter, we will argue that measuring immediate reconstruction only touches the surface of what can be gleaned from these ensembles. We will discuss the implications of distributed representation, in particular, the usefulness of measuring self-consistency of the representation within neural ensembles. Because representations are distributed, neurons in a population can agree or disagree on the value being represented. Measuring the extent to which a firing pattern matches expectations can provide an accurate assessment of the self-consistency of a representation. Dynamic changes in the self-consistency of a representation are potentially indicative of cognitive processes. We will also discuss the implications of representation of non-local (non-immediate) values for cognitive processes. Because cognition occurs at fast timescales, changes must be detectable at fast (millisecond, tens of milliseconds) timescales. Representation As an animal interacts with the world, it encounters various problems for which it must find a solution. The description of the world and the problems encountered within it play a fundamental role in how an animal behaves and finds a solution.