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.

Self-organized criticality has been proposed to be a universal mechanism for the emergence of scale-free dynamics in many complex systems, and possibly in the brain. While such scale-free patterns were identified experimentally in many different types of neural recordings, the biological principles behind their emergence remained unknown. Utilizing different network models and motivated by experimental observations, synaptic plasticity was proposed as a possible mechanism to self-organize brain dynamics toward a critical point. In this review, we discuss how various biologically plausible plasticity rules operating across multiple timescales are implemented in the models and how they alter the network’s dynamical state through modification of number and strength of the connections between the neurons. Some of these rules help to stabilize criticality, some need additional mechanisms to prevent divergence from the critical state. We propose that rules that are capable of bringing the network to criticality can be classified by how long the near-critical dynamics persists after their disabling. Finally, we discuss the role of self-organization and criticality in computation. Overall, the concept of criticality helps to shed light on brain function and self-organization, yet the overall dynamics of living neural networks seem to harnesses not only criticality for computation, but also deviations thereof.

Timescales characterize the pace of change for many dynamic processes in nature. They 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 to estimate timescales by fitting the sample autocorrelation or power spectrum with a generative model based on a mixture of Ornstein–Uhlenbeck processes using adaptive approximate Bayesian computations. 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 the primate cortex. We provide a customizable Python package that implements our framework via different generative models suitable for diverse applications.

Neural activity fluctuates endogenously on timescales varying across the neocortex. The variation in these intrinsic timescales relates to the functional specialization of cortical areas and their involvement in the temporal integration of information. Yet, it is unknown whether the timescales can adjust rapidly and selectively to the demands of a cognitive task. We measured intrinsic timescales of local spiking activity within columns of area V4 while monkeys performed spatial attention tasks. The ongoing spiking activity unfolded across at least two distinct timescales---fast and slow---and the slow timescale increased when monkeys attended to the receptive fields location. A recurrent network model shows that multiple timescales in local dynamics arise from spatial connectivity mimicking vertical and horizontal interactions in visual cortex and that slow timescales increase with the efficacy of recurrent interactions. Our results reveal that targeted neural populations integrate information over variable timescales following the demands of a cognitive task and propose an underlying network mechanism.

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.

Timescales characterize the pace of change for many dynamic processes in nature: radioactive decay, metabolization of substances, memory decay in neural systems, and epidemic spreads. Measuring timescales from experimental data can reveal underlying mechanisms and constrain theoretical models. Timescales are usually estimated by fitting the autocorrelation of sample time-series with exponential decay functions. We show that this standard procedure often fails to recover the correct timescales, exhibiting large estimation errors due to a statistical bias in autocorrelations of finite data samples. To overcome this bias, we develop a method using adaptive Approximate Bayesian Computations. Our method estimates the timescales by fitting the autocorrelation of sample data with a generative model based on a mixture of Ornstein-Uhlenbeck processes. The method accounts for finite sample size and noise in data and returns a posterior distribution of timescales quantifying the estimation uncertainty. We demonstrate how the posterior distribution can be used for model selection to compare alternative hypotheses about the dynamics of the underlying process. Our method accurately recovers the correct timescales on synthetic data from various processes with known ground truth dynamics. We illustrate its application to electrophysiological recordings from the primate cortex.

No abstract

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.