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Towards multi-layered multi-area models of cortical networks Diesmann, Markus

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Theoretical research on the local cortical network has mainly been concerned with the study of random networks composed of one excitatory and one inhibitory population of neurons. This led to fundamental insights on the correlation structure of activity. The present contribution discusses next steps towards a more realistic representation of the cortical microcircuit and the brain-scale architecture. The talk first introduces the draft of a full-scale model of the microcircuit at cellular and synaptic resolution [1] comprising about 100,000 neurons and one billion local synapses connecting them. The emerging network activity exhibits fundamental properties of in vivo activity: asynchronous irregular activity, layer-specific spike rates, higher spike rates of inhibitory neurons as compared to excitatory neurons, and a characteristic response to transient input. As the formal executable specification is publicly available, the model can serve as a testbed for theoretical approaches and can iteratively be refined. A key element in the mean-field theory of systems of heterogeneous populations is the transfer function of the individual elements. Recent progress [2] enables insights into the anatomical origin of oscillations in the multi-layered circuitry [3]. Despite these successes, the explanatory power of local models is limited as half of the synapses of each excitatory nerve cell have non-local origins. The second part of the talk therefore argues for the need of brain-scale models to arrive at self-consistent descriptions and addresses the arising technological and theoretical questions: Are simulations of the required size feasible [4]? Are full scale simulations required as opposed to downscaled representatives [5]? How can anatomical and physiological constraints with their respective uncertainty margins be integrated to arrive at a multi-area model with a realistic activity state [6]? www.nest-initiative.org www.csn.fz-juelich.de [1] Potjans TC, Diesmann M Cerebral Cortex 24(3):785-806 (2014) www.opensourcebrain.org [2] Schuecker J, Diesmann M, Helias M Phys Rev E 92:052119 (2015) [3] Bos H, Diesmann M, Helias M arXiv:1510.00642[q-bio.NC] (2015) [4] Kunkel S, Schmidt M, Eppler JM, Plesser HE, Masumoto G, Igarashi J, Ishii S, Fukai T, Morrison A, Diesmann M, Helias M Front Neuroinform 8:78 (2014) [5] van Albada S, Helias M, Diesmann M PLoS Comput Biol 11(9):e1004490 (2015) [6] Schuecker J, Schmidt M, van Albada S, Diesmann M, Helias M arXiv:1509.03162 [q-bio.NC] (2015)

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