The Poisson clicks task: long time constant of neural integration of discrete packets of evidence

Abstract

Event Abstract Back to Event The Poisson clicks task: long time constant of neural integration of discrete packets of evidence Bingni Brunton1, 2* and Carlos D. Brody3 1 Princeton Neuroscience Institute, United States 2 Princeton University, Department of Molecular Biology, United States 3 HHMI & Princeton University, United States With the goal of studying neural mechanisms of integration, we have developed a discrimination task that explicitly focuses on promoting integration of evidence over time. During each trial, subjects hear a series of randomly timed clicks from two well-separated speakers. The subjects must report which speaker played the greater total number of clicks. This requires keeping a running counter of clicks (i.e., integrating clicks) over the stimulus period. Task difficulty is controlled by the magnitude of the difference in click rate for the two speakers. Each click is a discrete quantum of evidence, occurring at a single, well-defined and experimenter-controlled timepoint. In this design, then, each trial’s inputs to a putative neural integrator of evidence are precisely known. We call the task the "Poisson clicks" task. We used the well-known drift-diffusion framework to model the neural integrator as a noisy, possibly biased, finite time constant (tau) integrator. A positive tau indicates a tendency to forget evidence that arrived more than tau seconds ago. A negative tau indicates a tendency to make a decision based only on the initial tau seconds of the stimulus. The ideal observer and integrator would have infinite tau. Current proposals of neural architectures for integration have variously suggested positive, negative, and infinite tau. Data from monkeys have suggested a magnitude of tau of several hundred milliseconds. We trained rats to perform our discrimination task. Knowledge of the specific trial-by-trial inputs to the integrator allowed obtaining accurate estimates of its parameters. Complete likelihood landscapes for the model parameters were computed, using both numerical integration of the drift-diffusion model’s Fokker-Planck equations and closed-form solutions. We found that (1) best-fitting tau are overwhelmingly positive, consistent with neural architectures approximated by leaky integrator models and inconsistent with unstable integrator architectures. (2) Well-trained subjects can achieve remarkably long time constants tau of up to 1000 ms, suggesting that the task was indeed successful at promoting integration. Analysis of stimulus-behavior correlations further confirmed the long integration time constant. (3) During learning, the signal-to-noise ratio of the integrator quickly stabilizes to its final value, but tau grows only slowly, achieving its final value after ~4 months of training. This indicates that in this task, associational learning is completed quickly, and the brunt of perceptual learning consists of fine-tuning of the integrator, once again consistent with the task being focused on integration. These findings establish the Poisson clicks task as particularly appropriate for studying neural integration. Our findings further show that rats are capable of integrating evidence over long time constants, and thus establish rats as a viable model system for studying neural integration. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Oral Presentation Topic: Oral presentations Citation: Brunton B and Brody CD (2010). The Poisson clicks task: long time constant of neural integration of discrete packets of evidence. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00041 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 18 Feb 2010; Published Online: 18 Feb 2010. * Correspondence: Bingni Brunton, Princeton Neuroscience Institute, Princeton, NJ, United States, bwen@princeton.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Bingni Brunton Carlos D Brody Google Bingni Brunton Carlos D Brody Google Scholar Bingni Brunton Carlos D Brody PubMed Bingni Brunton Carlos D Brody Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.