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

Intrinsic timescales characterize dynamics of endogenous fluctuations in neural activity. Variation of intrinsic timescales across the neocortex reflects functional specialization of cortical areas, but less is known about how intrinsic timescales change during cognitive tasks. We measured intrinsic timescales of local spiking activity within columns of area V4 in male monkeys performing spatial attention tasks. The ongoing spiking activity unfolded across at least two distinct timescales, fast and slow. The slow timescale increased when monkeys attended to the receptive fields location and correlated with reaction times. By evaluating predictions of several network models, we found that spatiotemporal correlations in V4 activity were best explained by the model in which multiple timescales arise from recurrent interactions shaped by spatially arranged connectivity, and attentional modulation of timescales results from an increase in the efficacy of recurrent interactions. Our results suggest that multiple timescales may arise from the spatial connectivity in the visual cortex and flexibly change with the cognitive state due to dynamic effective interactions between neurons.

A flexible Bayesian framework for unbiased estimation of timescales.

Timescales characterize the pace of change for many dynamic processes in nature. Timescales are usually estimated by fitting the exponential decay of data autocorrelation in the time or frequency domain. Here we show that this standard procedure often fails to recover the correct timescales due to a statistical bias arising from the finite sample size. We develop an alternative approach which estimates timescales by fitting the sample autocorrelation or power spectrum with a generative model based on a mixture of Ornstein-Uhlenbeck (OU) processes using adaptive approximate Bayesian computations (aABC). Our method accounts for finite sample size and noise in data and returns a posterior distribution of timescales that quantifies the estimation uncertainty and can be used for model selection. We demonstrate the accuracy of our method on synthetic data and illustrate its application to recordings from primate cortex. We provide a customizable Python package implementing our framework with different generative models suitable for diverse applications.

Interspike interval analysis and spikelets in presubicular head-direction cells.

Head-direction (HD) neurons are thought to provide the mammalian brain with an internal sense of direction. These cells, which selectively increase their firing when the animal's head points in a specific direction, use the spike rate to encode HD with a high signal-to-noise ratio. In the present work, we analyzed spike train features of presubicular HD cells recorded juxtacellularly in passively rotated rats. We found that HD neurons could be classified into two groups on the basis of their propensity to fire spikes at short interspike intervals. "Bursty" neurons displayed distinct spike waveforms and were weakly but significantly more modulated by HD compared with "nonbursty" cells. In a subset of HD neurons, we observed the occurrence of spikelets, small-amplitude "spike-like" events, whose HD tuning was highly correlated to that of the co-recorded juxtacellular spikes. Bursty and nonbursty HD cells, as well as spikelets, were also observed in freely moving animals during natural behavior. We speculate that spike bursts and spikelets might contribute to presubicular HD coding by enhancing its accuracy and transmission reliability to downstream targets. NEW & NOTEWORTHY We provide evidence that presubicular head-direction (HD) cells can be classified into two classes (bursty and nonbursty) on the basis of their propensity to fire spikes at short interspike intervals. Bursty cells displayed distinct electrophysiological properties and stronger directional tuning compared with nonbursty neurons. We also provide evidence for the occurrence of spikelets in a subset of HD cells. These electrophysiological features (spike bursts and spikelets) might contribute to the precision and robustness of the presubicular HD code.

Manipulating Hippocampal Place Cell Activity by Single-Cell Stimulation in Freely Moving Mice.

Learning critically depends on the ability to rapidly form and store non-overlapping representations of the external world. In line with their postulated role in episodic memory, hippocampal place cells can undergo a rapid reorganization of their firing fields upon contextual manipulations. To explore the mechanisms underlying such global remapping, we juxtacellularly stimulated 42 hippocampal neurons in freely moving mice during spatial exploration. We found that evoking spike trains in silent neurons was sufficient for creating place fields, while in place cells, juxtacellular stimulation induced a rapid remapping of their place fields to the stimulus location. The occurrence of complex spikes was most predictive of place field plasticity. Our data thus indicate that plasticity-inducing stimuli are able to rapidly bias place cell activity, simultaneously suppressing existing place fields. We propose that such competitive place field dynamics could support the orthogonalization of the hippocampal map during global remapping.

Scale-freeness of dominant and piecemeal perceptions during binocular rivalry.

When two eyes are simultaneously stimulated by two inconsistent images, the observer's perception switches between the two images every few seconds such that only one image is perceived at a time. This phenomenon is named binocular rivalry (BR). However, sometimes the perceived image is a piecemeal mixed of two stimuli known as piecemeal perceptions. In this study, a BR task was designed in which orthogonal gratings are presented to the two eyes. The subjects were trained to report 3 states: dominant perceptions (two state matching to perceived grating orientation) and the piecemeal perceptions (third state). We explored the scale-freeness of the BR percept durations considering the two dominant monocular states as well as the piecemeal transition state using detrended fluctuation analysis. Our results reproduced the previous finding of memory in the perceptual switches between the monocular perception states. Moreover, we showed that such memory also exists in the transitory periods of dichoptic piecemeal perceptions. These results support our hypothesis that the pool of unstable piecemeal perceptions could be regarded as separate multiple low-depth basin in the perceptual state landscape. Likewise, the transitions from these piecemeal state basins and stable monocular state basins are faced with resistance. Therefore there is inertia and memory (i.e. positive serial correlation) for the piecemeal dichoptic perception states as well as the monocular states.