Neuronal pattern separation of motion-relevant input in LIP activity

Abstract

In various regions of the brain, neurons discriminate sensory stimuli by decreasing the similarity between ambiguous input patterns. Here, we examine whether this process of pattern separation may drive the rapid discrimination of visual motion stimuli in the lateral intraparietal area (LIP). Starting with a simple mean-rate population model that captures neuronal activity in LIP, we show that overlapping input patterns can be reformatted dynamically to give rise to separated patterns of neuronal activity. The population model predicts that a key ingredient of pattern separation is the presence of heterogeneity in the response of individual units. Furthermore, the model proposes that pattern separation relies on heterogeneity in the temporal dynamics of neural activity and not merely in the mean firing rates of individual neurons over time. We confirm these predictions in recordings of macaque LIP neurons and show that the accuracy of pattern separation is a strong predictor of behavioral performance. Overall, results propose that LIP relies on neuronal pattern separation to facilitate decision-relevant discrimination of sensory stimuli. A new hypothesis is proposed on the role of the lateral intraparietal (LIP) region of cortex during rapid decision making. This hypothesis suggests that LIP alters the representation of ambiguous inputs to reduce their overlap, thus improving sensory discrimination. A combination of computational modeling, theoretical analysis, and electrophysiological data shows that the pattern separation hypothesis links neural activity to behavior and offers novel predictions on the role of LIP during sensory discrimination.

Embodied working memory during ongoing input streams

“…These individual response profiles range from sustained tonic activity to gradual increase/decrease in firing rate fluctuations. The presence of heterogeneity in the response of individual neurons likely provides a meaningful computational role for higher order function [ 39 , 48 , 91 , 92 ]. Despite the presence of such diversity, our findings solely accounted for the progressive decrease in the neural response profiles [ 5 , 6 ].…”

“…The spiking neural network consisted of 500 units linked reciprocally. The initial network connectivity followed a set of rules [ 39 ]. First, inhibitory neurons occupy approximately 20% of the population of cortical neurons, whereas the great majority of remaining neurons are excitatory [ 40 ].…”