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Timescales of spontaneous cortical dynamics reflect the underlying spatial network structure

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Zeraati,  R
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Levina,  A
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Zeraati, R., Steinmetz, N., Moore, T., Engel, T., & Levina, A. (2019). Timescales of spontaneous cortical dynamics reflect the underlying spatial network structure. Poster presented at Bernstein Conference 2019, Berlin, Germany. doi:10.12751/nncn.bc2019.0266.


Cite as: http://hdl.handle.net/21.11116/0000-0004-A191-1
Abstract
Spontaneous cortical activity unfolds across different spatial scales. On a local scale of individual columns, activity spontaneously transitions between episodes of vigorous (On) and faint (Off) spiking synchronously across cortical layers [1]. On a wider spatial scale, activity propagates as cascades of elevated firing across many columns, characterized by a branching ratio defined as the average number of units activated by each active unit [2]. Timescales of these intrinsic fluctuations were suggested to reflect the network's specialization for task-relevant computations, but how they arise from the spatial structure of the network is unknown. To find out to what extent these timescales reflect the dynamics on different spatial scales and the underlying network structure, we developed a branching network model capable of capturing both local On-Off dynamics and global activity propagation. Our model consists of bistable units representing cortical columns with spatially structured connections to other columns (Fig 1A). We found that the timescales of local dynamics reflect the spatial network structure. In the model, activity of single columns exhibits two distinct timescales: one induced by the recurrent excitation within the column and another induced by interactions between the columns (Fig 1B). The first timescale dominates dynamics in networks with more dispersed connectivity, whereas the second timescale is prominent in networks with more local connectivity (Fig 1C). The second timescale is also evident in cross-correlations (CC) between columns because of their shared recurrent inputs and becomes longer with increasing distance between columns (Fig 1D). To test model predictions, we analyzed multi-electrode recordings of spiking activity from single columns in the area V4 and observed two timescales in both local On-Off fluctuations and CCs of neural activity on different channels within the same column (Fig 1E, F). We examined the dependency of these timescales on horizontal cortical distance, by leveraging the slight horizontal shifts in columnar recordings and using the distances between centers of receptive fields (RF) across different channels as a surrogate for horizontal displacement. As predicted by the model, the second timescale in CCs became longer with increasing RF-center distance. Our results suggest that timescales of local fluctuations in single cortical columns provide information about the underlying spatial network structure.