Event Abstract Back to Event A single cell with active conductance's can learn timing and multi-stability Recent experimental studies have demonstrated that neural responses in primary sensory cortex can significantly outlast the duration of the sensory input. In particular, a recent study by Shuler and Bear (2006) has shown learned, reward timing dependent cortical activity in primary visual cortex, activity that represents the expected duration of a reward. We have shown how such activity can be represented and learned in a network of interacting neurons (paper submitted). However, recent experimental results and computational models (Egorov et al. 2002, Fransen et al. 2002, 2006) have shown that single neurons with active conductance-s can account for persistent activity, suggesting that they might also contribute to the prolonged decaying activity observed in primary sensory cortex. Here, we first develop a simple model of a neuron with active conductance-s which can in principle account for bi-stability, multi-stability and slowly decaying activity. This model is based on voltage dependent calcium channels, and on calcium dependent cationic channels, a model inspired by the previous experiments and computational studies. In contrast to previous computational model this is a reduced single compartment model, which makes it more amenable to analysis, while still maintaining a biophysical basis. Using simulations we show that this model, with parameters chosen correctly, can exhibit bi-stability critically slowed neuronal dynamics as well as multi-stability. Using an analytical pseudo steady-state approach we reduce this model to a single non-linear dynamical equation, and we show that the reduced model is in very close agreement with the full simulated model. Using the reduced model we develop single parameter bifurcation diagrams of the model, and we show that it is robustly bi-stable. The reduced model can account both for the phase transitions and for the detailed dynamics in the falling phase of the sub-threshold regime. The reduced model also makes it possible to intuitively understand the origin of bi-stability, multi-stability as well as critically slow dynamics. Next we introduce a reinforcement based plasticity rule for the internal conductance-s which critically control the neuronal dynamics. We demonstrate that this learning rule can learn appropriate reward times, and when these reward times are learned it produces neuronal dynamics that are similar to those observed experimentally. We also show that the same learning rule, when applied at a different parameter regime can learn a target firing rate for the “up” state in a bi-stable system. Unlike our previous stochastic spiking model, this single cell model is deterministic and produces periodic firing in the up state. This stand in contrast to in-vivo experimental results in which the CV is typically ?1 (Barbieri and Brunel, 2008). However, these active conductance-s, when combined with the cortical stochastic background activity might contribute to coding and learning of representations of time and of persistent activity levels. 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). A single cell with active conductance's can learn timing and multi-stability. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.254 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 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.