Neural noise shapes perceptual landscapes for different forms of plasticity in perceptual learning

Abstract

Event Abstract Back to Event Neural noise shapes perceptual landscapes for different forms of plasticity in perceptual learning Some forms of perceptual learning are thought to involve changes in how sensory information is represented in the brain. Others involve changes in how the sensory representation is read out to form decisions. Little is known about the conditions that give rise to these different forms of plasticity or their implications for perceptual abilities. We implemented a simple model that separates the sensory representation from the population readout to gain insights into how perceptual decisions can be influenced by changes in each process alone or interactions between the two. Specifically, we examined how changes in the tuning curves of individual neurons and selective pooling of task-specific sensory information affect perceptual performance in detection, coarse discrimination, fine discrimination and estimation tasks. We first derived analytical solutions for the optimal readout for each task of a population of neurons with uniform Gaussian tuning curves, Poisson variability and interneuronal correlations. For the detection task, the optimal readout selectively pools activity from neurons whose preferred directions are closest to the detection signal. For coarse discrimination, the optimal readout generates neuron and anti-neuron pools with positive and negative peaks centered at the discriminated stimuli. For fine discrimination, the optimal readout operates as local differentiator whose peaks are away from the discriminated stimuli. For stimulus estimation, the optimal readout is scaled linearly as a function of the parameter to estimate. Further, we solved analytically the optimal sizes of neural pools for detection, coarse and fine discriminations for different sensory representations. The optimal pool size increases linearly with increased tuning widths, decreases as a power-law function with increased neural noise and can depend substantially on the level of interneuronal correlations. Once the readout is optimized for a particular sensory representation, our model suggests that further changes to the representation can enhance or degrade performance in a task-specific manner. Estimation is impaired by any change in the representation. Detection and coarse discrimination improve with broadening of tuning curves, whereas fine discrimination improves with narrowing of tuning curves. We created perceptual-learning landscapes for each task, summarizing the effects of changes in representation and readout on performance. These landscapes indicate which kinds of change are most effective for improving performance under different conditions. For each task, the landscape depended substantially on the level of neural background activity, which therefore helped to determine preferences for particular forms of plasticity for perceptual learning. These insights are discussed in the context of experimental observations of changes in sensory representations for auditory and somatosensory tasks and changes in readout for visual tasks during perceptual learning. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Neural noise shapes perceptual landscapes for different forms of plasticity in perceptual learning. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.237 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: 03 Feb 2009; Published Online: 03 Feb 2009. 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 Google Google Scholar PubMed 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.