Abstract
Spatio-temporal sequences of neuronal activity are observed in many brain regions in a variety of tasks and are thought to form the basis of meaningful behavior. However, mechanisms by which a neuronal network can generate spatio-temporal activity sequences have remained obscure. 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. Here, we propose a biologically plausible, generative rule to create spatio-temporal activity sequences in a network of spiking neurons with distance-dependent connectivity. We show that the emergence of spatio-temporal activity sequences requires: (1) individual neurons preferentially project a small fraction of their axons in a specific direction, and (2) the preferential projection direction of neighboring neurons is similar. Thus, an anisotropic but correlated connectivity of neuron groups suffices to generate spatio-temporal activity sequences in an otherwise random neuronal network model.
Highlights
Ordered sequences of actions are the key to any meaningful behavior
We show that neuronal networks exhibit temporal sequences of activity when (1) neurons do not connect in all directions with equal probability, and (2) neighboring neurons have similar connection preference
Connection asymmetry is consistent with the experimental findings that axonal and dendritic arbors are spatially asymmetric
Summary
Ordered sequences of actions are the key to any meaningful behavior. This implies that the task-related neuronal spiking activity in the task-related brain regions must be ordered in temporal activity sequences [1, 2]. Activity sequences have been observed in tasks that do not involve any specific sequential stimuli, e.g. in decision making [7, 8], in learning [10], in memory recall [6], and in generating bird songs [3]. This suggests that neuronal networks in the brain are able to generate neuronal activity sequences using intrinsic mechanisms
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