Year: 1999
Type: conference-paper
Abstract: Most theoretical and empirical studies of population codes make the assumption that underlying neuronal activities is a unique and unambiguous value of an encoded quantity. However, population activities can contain additional information about such things as multiple values of or uncertainty about the quantity. We have previously suggested a method to recover extra information by treating the activities of the population of cells as coding for a complete distribution over the coded quantity rather than just a single value. We now show how this approach bears on psychophysical and neurophysiological studies of population codes for motion direction in tasks involving transparent motion stimuli. We show that, unlike standard approaches, it is able to recover multiple motions from population responses, and also that its output is consistent with both correct and erroneous human performance on psychophysical tasks. A population code can be defined as a set of units whose activities collectively encode some underlying variable (or variables). The standard view is that population codes are useful for accurately encoding the underlying variable when the individual units are noisy. Current statistical approaches to interpreting population activity reflect this view, in that they determine the optimal single value that explains the observed activity pattern given a particular model of the noise (and possibly a loss function). In our work, we have pursued an alternative hypothesis, that the population encodes additional information about the underlying variable, including multiple values and uncertainty. The Distributional Population Coding (DPC) framework finds the best probability distribution across values that fits the population activity
Source: MPG.PuRe (Max Planck Society)
Authors: Richard S. Zemel, Peter Dayan
Institutions: University of Arizona, Gatsby Computational Neuroscience Unit
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2137463787