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Attentional modulation of neural activity timescales and their relation to spatial network structure

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Zeraati, R., Shi, Y., Steinmetz, N., Moore, T., Engel, T., & Levina, A. (2021). Attentional modulation of neural activity timescales and their relation to spatial network structure. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2021).

Cite as: https://hdl.handle.net/21.11116/0000-000A-F734-5
Ongoing neural activity unfolds across different timescales reflecting networks’ specialization for task-relevant
computations. However, it is unknown whether these timescales can be flexibly modulated during trial-to-trial
alternations of cognitive states (e.g., attention state) and what mechanisms can cause such modulations. We
analyzed autocorrelations of population spiking activity recorded from individual cortical columns of the primate
area V4 during a spatial attention task and a fixation task. We estimated timescales from autocorrelations using
a novel method based on Approximate Bayesian Computations and applied a Bayesian model comparison to
determine the number of timescales in neural activity. We found that at least two distinct timescales are present
in both spontaneous and stimulus-driven activity. The slower timescale was significantly longer on trials when
monkeys attended to the receptive fields location of the recorded neurons than on control trials when monkeys
attended to a different location.
We hypothesized that the observed timescales emerge from the recurrent network dynamics shaped by the spatial
connectivity structure. We developed a network model consisting of binary units representing cortical minicolumns
with local spatial connectivity among them. We found that the activity of model minicolumns exhibits two distinct
timescales: A fast timescale induced by vertical recurrent excitation within a minicolumn and a slow timescale
induced by horizontal interactions among minicolumns. The timescales depend on the network topology, and
the slow timescale disappears in networks with random connectivity. We derived an analytical relationship be-
tween the timescales and connectivity parameters, enabling us to identify model parameters best matching the
timescales in the data. The model indicates that modulation of timescales during attention arises from a slight
increase in the efficacy of horizontal recurrent interactions. Our results suggest that multiple timescales in local
neural dynamics emerge from the spatial network structure and can flexibly adapt to task demands.