Event Abstract Back to Event Synaptic decision making: flipping switch-like synapses with cubic autocatalysis. In the brain, the process of learning is a noisy decision making process. At the level of a single synapse, when two connected neurons are active, each generating a different noisy, but perhaps correlated, sequence of action potentials, the synapse connecting them faces a decision ? how or whether to modify the strength of that connection. Recent work suggests that this change in synapse strength might come in the form of one of two binary decisions (O'Connor et al., PNAS102:9679-9684, 2005). For a synapse in a low-state, the decision is whether to increase in strength (long-term potentiation; LTP), or remain the same. For a synapse in a high-state, the decision is whether to decrease in strength (long-term depression; LTD) or remain the same. When plasticity is measured in a population of synapses, graded learning rules can be measured which reflect the statistics of the single synapse decisions. One form of learning rule which has generated a great deal of interest over the last decade is spike-timing dependent plasticity (STDP), in which the amount and direction of plasticity is a function of the timing difference between presynaptic and postsynaptic action potentials. In hippocampal synapses however, the story is more complicated: both the magnitude and direction of plasticity are influenced by other activity parameters such as spiking frequency, the number of pairings delivered (Wittenberg and Wang, J Neurosci 26:6610-6617, 2006). The biophysical mechanism by which neural activity changes the strength and state of a synapse is well-studied. Both increases in synapse strength and decreases in synapse strength are initiated by increases in postsynaptic [Ca2+] at the synapse above baseline [Ca2+] levels. Large calcium transients drive synapses to potentiate, whereas moderate and prolonged calcium transient drive synapses to depress. The goal of this work is to synthesize and summarize these experimental findings with a simple biophysical model for a switch-like synapse. We construct a simple model based on the bistability arising from cubic autocatalysis, which is reminiscent of the Activator/Inhibitor models for spatial pattern formation in animal coats. With this approach we are able to naturally capture the features of the learning rule determined at the CA3-CA1 synapse described above. 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). Synaptic decision making: flipping switch-like synapses with cubic autocatalysis.. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.273 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: 04 Feb 2009; Published Online: 04 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 Supplemental Data 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.
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