Emergence and control of spatiotemporal sequences of neuronal activity in biological neuronal networks
Arvind Kumar, KTH Royal Institute of Technology
Tuesday September 15, 12-1pm
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Abstract: Ordered sequences of actions are the key to any meaningful behavior. This also implies that the task-related neuronal spiking activity in the relevant brain regions must also be ordered in temporal activity sequences. This idea was conceptualized by Sherrington in his enchanted loom analogy and neutralized by Donald Hebb as phase sequences.
In the last decade temporal sequences of neuronal activity have been observed in many sensory, motor and cognitive tasks. Mechanisms underlying the emergence of such sequential activity are poorly understood. Existing models are biologically untenable because they either require manual embedding of a feedforward network within a random network or supervised learning to train the connectivity of a network to generate sequences. Could there be some other more generic and biologically plausible mechanisms that intrinsically render the network to generate sequential activity?
We found that when (1) neurons project a small fraction of their outputs to a preferential direction and (2) the preferred directions of neighboring neurons are similar, the network can generate temporal sequences without learning. This generative rule results in 鈥榮patial correlated anisotropic connectivity鈥�. Such connectivity can arise when neighbouring neurons have similar morphologies or but also when neurons form long-range patchy connections.
Finally, I will argue that spatially patchy patterns of neuromodulator release is a simple and natural way to generate 鈥榗orrelated spatial anisotropic connectivity. This way neuromodulators can not only allow for the formation of temporal sequences but also provide a biological plausible way to dynamical control the arrangement of sequences. Thus, spatial structure of the connectivity or neuromodulators can form the basis of temporal structure of neuronal activity or the phase sequences of Hebb.
