Intrinsic timescales in the visual cortex change with 1 selective attention and reﬂect spatial connectivity

Intrinsic timescales characterize dynamics of endogenous ﬂuctuations in neural activity. Variation 18 of intrinsic timescales across the neocortex reﬂects functional specialization of cortical areas, but 19 less is known about how intrinsic timescales change during cognitive tasks. We measured intrinsic 20 timescales of local spiking activity within columns of area V4 while monkeys performed spatial 21 attention tasks. The ongoing spiking activity unfolded across at least two distinct timescales, fast 22 and slow. The slow timescale increased when monkeys attended to the receptive ﬁelds location and 23 correlated with reaction times. By evaluating predictions of several network models, we found 24 that spatiotemporal correlations in V4 activity were best explained by the model in which mul- 25 tiple timescales arise from recurrent interactions shaped by spatially arranged connectivity, and 26 attentional modulation of timescales results from an increase in the efﬁcacy of recurrent interac- tions. Our results suggest that multiple timescales arise from the spatial connectivity in the visual cortex and ﬂexibly change with the cognitive state due to dynamic effective interactions between neurons. the connection strength affect the timescales of network dynamics. example, slower timescales in networks with clustered connections compared to random networks 34 , or heterogeneity in the strength of inter-node connections gives rise to diverse localized timescales in a one dimensional network . Thus, network models can relate dynamics to connectivity and generate testable predictions to identify mechanisms underlying the generation of intrinsic timescales in the brain. We examined how the intrinsic timescales of spiking activity in visual cortex were affected by the trial- to-trial alterations in the cognitive state due to visual spatial attention. We analyzed spiking activity recorded from local neural populations within cortical columns in primate area V4 during two different spatial attention tasks and a ﬁxation task. In all tasks, the autocorrelation of intrinsic activity ﬂuctuations 81 showed at least two distinct timescales, one fast and one slow. The slow timescale was longer on trials when monkeys attended to the receptive ﬁelds of the recorded neurons and correlated with the 83 monkeys’ reaction times. We used recurrent network models to test several alternative mechanisms for generating the multiplicity of timescales and their ﬂexible modulation. We established analytically that spatially arranged connectivity generates multiple timescales in local population activity and found support for this theoretical prediction in our V4 recordings. In contrast, heterogeneous biophysical properties of individual neurons alone cannot account for both temporal and spatial structure of V4 correlations. Thus, the V4 timescales arise from spatiotemporal population dynamics shaped by the local spatial connectivity structure. The model indicates that modulation of timescales during attention can be explained by a slight increase in the efﬁcacy of recurrent interactions. Our results suggest that multiple intrinsic timescales in local population activity arise from the spatial network structure of the neocortex and the slow timescales can ﬂexibly adapt to trial-to-trial changes in the cognitive state due to dynamic effective interactions between neurons. the the slow timescale beneﬁts spatial To understand the mechanisms underlying the multiplicity and ﬂexible modulation of timescales, we developed network models linking intrinsic timescales to biophysical properties of individual neurons or the spatial connectivity structure of the visual cortex. Only the spatial network model correctly predicted the distance-dependence of spatiotemporal correlations in V4, indicating that multiple timescales in V4 dynamics arise from the spatial connectivity of primate visual cortex. The model suggests that slow timescales increase with the effective strength of recurrent interactions. Multiple intrinsic timescales in neural activity. Previous studies characterized the autocorrelation of ongoing neural activity with a single intrinsic timescale 11,13,15,16 . The intrinsic timescale was usually measured for neural populations either by averaging autocorrelations of single neurons in one area 11 or using coarse-grained measurements such as ECoG 13 or fMRI 15,16 . Thus, ongoing dynamics in each area were described with a single intrinsic timescale that varied across areas. We extended this view by showing that, within one area, local population activity exhibits multiple intrinsic timescales. These 413 timescales reﬂect ongoing dynamics on single trials and are not driven by task events. Our results suggest that the multiplicity of timescales is an intrinsic property of neural activity arising from inherent cellular and network properties of the cortex.