Misha Tsodyks

Misha Tsodyks


Models of Brain Functions


My research is in the field of computer modeling in neuroscience. Computer models of brain functions are rapidly becoming important as the amount of experimental data available grows steadily, and computer power and theoretical ideas are being developed.

The goals of computational models are very diverse, ranging from the understanding of consciousness to the properties of individual neurons. My main interest lies in the properties of large-scale neuronal networks, responsible for such brain functions as memory, processing of visual information and spatial navigation. Understanding these complex functions requires a multidisciplinary approach, comprising techniques and ideas of biology, physics and computer science. Several theoretical ideas proposed recently regarding the neuronal mechanisms of associative memory were borrowed from statistical physics. According to these ideas, the cooperative dynamics of large neuronal populations are the correlate of information processing. In my work I apply this line of research to developing biologically plausible models of information processing and memory.

My most rewarding work by now in this direction was performed in collaboration with Prof. D. Amit, while I was in the Hebrew University. We have constructed a neural network model of visual memory, which captured the results of the delayed memory experiments on monkeys, performed in the lab of Miyashita in Tokyo. The model explained how the neuronal representations of the pictures in the monkey's visual cortex became correlated dependingon the order in which they were learned.

It is conceivable that the progress in computational neuroscience will accelerate due to the development of new experimental technique, such as optical imaging of the brain, and multiunit electrical recordings, which will enable the activity of large populations of neurons to be followed simultaneously. The data obtained with these modern techniques allows a degree of comparison with modeling results that so far was not possible. One of my ongoing projects in the lab of B. McNaughton in Arizona, involves the modeling of place coding in the rodent hippocampus, and relating it to recordings from more than 100 neurons simultaneously. As a result of this collaboration, the influence of cooperative neuronal dynamics on the formation of place specificity of the hippocampal neurons was found.

I started my scientific career as a Ph.D. student in the Landau Institute for Theoretical Physics in Moscow. After spending two years in the Institute of Neurophysiology in Moscow, I moved to the Hebrew University, where I stayed for 3 years in the Physics Department. For the next one and a half years I was in the Computational Neurobiology Lab in the Salk Institute in San Diego, and I recently joined the Weizmann Institute.

Griniasty, M., M.Tsodyks, D.Amit (1993): Conversion of temporal correlations between stimuli to spatial correlations between attractors, Neural Computations, 5:1-17.

Tsodyks, M., I.Mit'kov, H.Sompolinsky (1993): Pattern of synchrony in inhomogeneous networks of oscillators with pulse interactions. Phys. Rev. Lett., 71:1280-1283.

Amit, D., N. Brunel, M. Tsodyks (1994): Correlations of cortical Hebbian reverberations: experiment versus theory, J. Neuroscience, 14:6435-6445.

Tsodyks, M., T. Sejnowski (1995): Rapid switching in balanced cortical network models, network, 6:111-124.

Tsodyks, M., T. Sejnowski (1995): Associative memory and hippocampal place cells, Int. Journal of Neural Systems, in press.


Tel: (972)-8-9342157

Fax: (972)-8-9344140

e-mail: bnmisha@wicc.weizmann.ac.il

Misha Tsodyks home page



Experimental and modeling results of delayed matching to sample memory task. The monkey had to learn a set of computer-generated pictures presented in a fixed order. Neuronal representations formed in the monkey's visual cortex were found to be correlated according to the order in which the pictures were presented. The correlation coefficient is plotted vs separation in the training sequence. Symbols are average over cell sample. , Experiment; , Model. Experimental data are from Fig. 3c of Miyashita (1988): Neuronal correlate of visual associative long-termmemory in the primate temporal cortex, Nature 331:68.

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