Event Abstract Back to Event Sparse connectivity in short-term memory networks Dimitri Fisher1*, Emre Aksay2 and Mark Goldman1 1 UC Davis , Center for Neuroscience, United States 2 Weill Medical College of Cornell University, United States Short-term memory is thought to be stored in patterns of neural activity that persist for several seconds following a transient stimulus. However, neural mechanisms that underlie this persistent activity are not fully understood. Previous efforts at modeling short-term memory networks have used simplifying assumptions on both the patterns of connections and the nature of responses in the network, such as assuming linear networks in which negative rates were allowed or imposing certain symmetries on the connectivity. Although these studies identified theoretical networks that can generate short-term memory, the fundamental question remains unanswered: what are the short-term memory network architectures in actual biological systems? To address this question, we have developed a general modeling framework applicable to a wide variety of short-term memory settings. Experimental data are directly incorporated into the model while the evaluation of the network connectivity is reduced to a constrained linear regression problem with no a priori assumptions on the form of the connection strengths. The framework uses single-neuron properties known from electrophysiology: neuronal tuning curves and neuronal spiking responses to somatic current injection. Responses to current injection are calibrated by tuning the parameters of a conductance-based model neuron to reproduce the current injection experimental data spike by spike. Best-fitting connection strengths are found for any choice of the (typically unknown) nonlinear synapto-dendritic activation curves, which describe the current flowing into the soma from individual dendrites as a function of presynaptic neuron firing rate. For a network of spiking neurons with stochastic noise based on experimentally measured coefficients of variation, we perform systematic searches of parameter space to determine which sets of synapto-dendritic activations and resulting network connectivities produce good matches to the memory activity observed in the experimental system. This methodology is applied to two networks: (i) a network with monotonic tuning curves - the oculomotor integrator - that calculates and stores the eye position resulting from a sequence of eye velocity commands, and (ii) a network with peaked tuning curves that stores the spatial location of a cue. In contrast to previous studies, we find that a number of network architectures provide good fits to the experimental data, and their connectivity structure is usually highly sparse. These networks range in structure from highly recurrent to strongly feedforward, and function at least as accurately as the biological system being modeled. Sparseness of connections is manifested by a power-law structure in the synaptic weight distribution. We emphasize that sparseness was not imposed upon the network, but rather emerged from the biological requirement that neurons maintain well-tuned persistent activity. Sparseness in this setting reflects that each neuron’s firing rate, as a function of the stored variable, is well-approximated by a relatively small set of recurrent inputs. Although the reasons for this sparseness are not yet entirely clear, we speculate that this result is similar to that obtained in other smooth-function-approximation problems, in which the distributions of decomposition coefficients are often sparse. We suggest that similar sparse structures may emerge in a wide range of both memory and non-memory circuits. 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: Fisher D, Aksay E and Goldman M (2010). Sparse connectivity in short-term memory networks. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00261 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: Dimitri Fisher, UC Davis, Center for Neuroscience, Davis, United States, dimitrifisher@yahoo.com 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 Dimitri Fisher Emre Aksay Mark Goldman Google Dimitri Fisher Emre Aksay Mark Goldman Google Scholar Dimitri Fisher Emre Aksay Mark Goldman PubMed Dimitri Fisher Emre Aksay Mark Goldman 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.