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• W2325949307 abstract "Event Abstract Back to Event Ramping activity is an inefficient estimator of time intervals In many areas of the brain, ramping activity or climbing activity, i.e., persistent neural activity that steadily increases as a function of time, has been linked to the anticipation of forthcoming events. A particular example is given by working memory tasks, in which a monkey memorizes a stimulus and then anticipates another stimulus after a fixed time interval. During the delay period of such a task, the firing rates of many neurons in the prefrontal cortex and elsewhere climb linearly with time. When the time interval between the two events is doubled, the slope of the ramping activity adjusts to half the original slope [e.g. Brody et al, 2003, Cereb Cortex 13:1196-1207] so that the system reaches exactly the same state at the end of the delay period as before. Based on these observations, ramping activity has been considered an internal estimate of elapsed time. Experiments with rescaled delay periods do indeed suggest that the respective neural systems strive to be in a particular, time-independent state at the end of the delay period. We therefore investigated the hypothesis that ramping activity is a solution to the problem of being in the right state at the right time. In particular, we asked how the observed solution - ramping activity - compares with the optimal solution to this problem in arbitrary neural networks. To study the problem with sufficient generality, we used firing rate models whose dynamics are prescribed by abstract energy landscapes. We considered systems with various types of noise (additive, multiplicative, correlated, etc.) All systems were constrained to start at one point and reach a predefined target area after a certain time interval. Once the system reaches the separatrix delimiting the target area, the delay period ends, and a learned response suitable to the task follows. The optimal system was defined to be the one that would reach the target area with the highest temporal precision in repeated runs. In one-dimensional systems with additive noise, we find that the optimal solution is indeed ramping activity. Among all possible models, the ones that yield linearly growing firing rates lead to the smallest timing error. However, when considering higher-dimensional systems, much better solutions exist. In particular, constructing a suitable long funnel in an energy landscape with high constant-velocity dynamics gives more and more accurate timing as the length of the path is increased. In fact, the standard deviation of first passage times is inversely proportional to the velocity in this case. These results hold for various types of noise and several other constraints such as energy consumption. For all of these systems, ramping activity is among the worst of all possible solutions. Optimal representation of a fixed time interval is thus not, in itself, a sufficient explanation for the existence of ramping activity. Other considerations and constraints may be more important. For instance, the ability to quickly relearn time intervals may be more influential in shaping the ramping activity solution than the accuracy of timing per se. 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). Ramping activity is an inefficient estimator of time intervals. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.053 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: 30 Jan 2009; Published Online: 30 Jan 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." @default.
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• W2325949307 date "2009-01-01" @default.
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• W2325949307 title "Ramping activity is an inefficient estimator of time intervals" @default.
• W2325949307 doi "https://doi.org/10.3389/conf.neuro.06.2009.03.053" @default.
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