Deciding with single spikes: MT discharge and rapid motion detection
-
1
University of Wisconsin , United States
-
2
University of Minnesota, Department of Neuroscience, United States
Sequential sampling models have been used to explain the behavioral performance and timing of reaction time tasks, but most electrophysiological studies employing these models have relied on visual tasks in which the nature of the stimulus precludes rapid performance by requiring extensive temporal integration. Behavioral data from our lab suggests that one particular assumption of these models, the even weighting of information over time, is unable to explain very fast reaction times (< 250 ms) in a well practiced task. In this task, monkeys detected a brief pulse of high contrast coherent motion embedded in noise. Electrophysiological recording of MT responses during this task demonstrates that individual neurons reliably signal the occurrence of these pulses on a time scale of tens of milliseconds. Moreover, there was a reliable relationship between activity on these time scales and the behavioral choices made by the animal. By employing a novel application of information theory, we demonstrated that the combined encoding and decoding reliabilties on particular neurons were largely sufficient to explain both behavioral reliability and timing in the task. Moreover, we found a strong correlation between encoding and decoding reliability, suggesting that the animals were basing their decisions solely on these reliable neurons. In this study, we test whether common employed assumptions regarding neuronal coding and integration are sufficient to explain these observations. Specifically, we model MT neurons with Poisson discharge whose rate is rapidly modulated by the onset of coherent motion. Using a decay constant of 100 ms, we linearly summed the discharge from multiple neurons to produce a decision variable in an standard accumulator model. We then adjusted the decision threshold to match observed performance and studied the nature of neuronal pooling necessary to explain performance by varying both the number, selectivity, and interneuronal correlations of sampled neurons. We find that, if overall performance and reaction time are the sole constraints, a wide range of pooling models can explain our data, including ones in which hundreds of MT neurons are sampled by the animal. However, the strong correlation between encoding and decoding reliability over time scale of milliseconds observed in our recordings, places far stronger constraints on pooling models. Specifically the poor decoding performance of neurons with moderate sensory reliability can only be explained by these neurons not contributing to the decision variable. Thus, only models in which small number of reliable neurons are sampled over brief periods of time are sufficient to explain our observations. Because the model employs standard Poisson discharge and accumulation, it does not rely on complex temporal encoding or decoding schemes. Our results demonstrate the potential for very optimized neuronal pooling in the case of well-practiced tasks, in which decisions are based on small numbers of action potentials from neurons with reliable rate modulation.
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Poster Presentation
Topic:
Poster session II
Citation:
Krause
B and
Ghose
G
(2010). Deciding with single spikes: MT discharge and rapid motion detection.
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00277
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:
05 Mar 2010;
Published Online:
05 Mar 2010.
*
Correspondence:
Geoff Ghose, University of Minnesota, Department of Neuroscience, Minneapolis, United States, geoff@cmrr.umn.edu