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OM&P
Section COMPUTATIONAL NEUROSCIENCE
it has the following advantages: (i) classification accuracy monotonously increases in the limit, as the ensemble approaches the stable equilibrium state; (ii) the mathematical model of the competitive dynamics admits an analytical expression for species sizes in the equilibrium state, which theoretically allows to achieve the exactly optimal Bayesian classification rule as a result of learning in the limit of infinite training sequence.
Acknowledgements
This work was supported by the Russian Science Foundation (grant 16-12-00077). References
1. A. Didovyk et al., ACS synthetic biology, 2014, 4(1), 72-82.
2. O. Kanakov et al., PLOS ONE, 2015, 10(5), e0125144.
Computations with Intracellular Circuits
M.V. Ivanchenko1*, O.I. Kanakov1 and A.A. Zaikin1,2
1 Lobachevsky University, Nizhny Novgorod, Russia;
2 University College London, London, United Kingdom. * Presenting e-mail: ivanchenko.mv@gmail.com
Information processing, or computation, can be performed by natural and man-made «devices». Man-made computers are made from silicon chips, whereas natural «computers», such as the brain, use cells and molecules. At the same time there is a growing understanding that complex information processing in living systems goes beyond neurons, for example, in adaptive immune system, or in synthetically engineered bacterial cells. Even further, computation occurs on a subcellular level, that is regulatory and signaling pathways in individual cells. In fact, what we perceive as living processes originates from the remarkable ability of integrated biological «elementary» circuits to perform sophisticated computations. For neuronal systems it follows that their information processing abilities may substantially involve similar mechanisms. In our talk we will introduce the key concepts and discuss recent progress that has been made in biomolecular computing. As a proop of principle we present our recent results on a scheme of a synthetically engineered distributed genetic circuit capable of solving classification tasks for quite generic input vectors of chemical signals.
Acknowledgements
The authors acknowledge support of the Russian Science Foundation (grant No. 16-12-00077).
Synaptic Origins of Working Memory Capacity
M. Tsodyks*
Weizmann Institute of Science, Israel. * Presenting e-mail: misha@weizmann.ac.il
Working memory plays a fundamental role in many cognitive tasks. It is thus puzzling that its capacity is extremely limited, averaging just 4 items for most of the people. The origins of this limit are not clear. I will consider this issue in the framework of synaptic theory of working memory. I will derive an analytical estimate for capacity in terms of basic parameters of short-term synaptic plasticity and neuronal spike-generation dynamics. The obtained expression indicates that capacity can be tuned to the desired level by modulating the average excitation in the network. If time permits, I will show how this process could account for spontaneous chunking of word lists in free recall experiments.
54 Opera Med Physiol 2016 Vol. 2 (S1)
