Behavioral Context Determines Network State and Variability Dynamics in Monkey Motor Cortex (original) (raw)
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On the complexity of resting state spiking activity in monkey motor cortex
2020
Resting state has been established as a classical paradigm of brain activity studies, mostly based on large scale measurements such as fMRI or M/EEG. This term typically refers to a behavioral state characterized by the absence of any task or stimuli. The corresponding neuronal activity is often called idle or ongoing. Numerous modeling studies on spiking neural networks claim to mimic such idle states, but compare their results to task- or stimulus-driven experiments, which might lead to misleading conclusions. To provide a proper basis for comparing physiological and simulated network dynamics, we characterize simultaneously recorded single neurons’ spiking activity in monkey motor cortex and show the differences from spontaneous and task-induced movement conditions. The resting state shows a higher dimensionality, reduced firing rates and less balance between population level excitation and inhibition than behavior-related states. Additionally, our results stress the importance o...
Measurement of variability dynamics in cortical spike trains
Journal of Neuroscience Methods, 2008
We propose a method for the time-resolved joint analysis of two related aspects of single neuron variability, the spiking irregularity measured by the squared coefficient of variation (CV 2 ) of the ISIs and the trial-by-trial variability of the spike count measured by the Fano factor (FF). We provide a calibration of both estimators using the theory of renewal processes, and verify it for spike trains recorded in vitro. Both estimators exhibit a considerable bias for short observations that count less than about 5-10 spikes on average. The practical difficulty of measuring the CV 2 in rate modulated data can be overcome by a simple procedure of spike train demodulation which was tested in numerical simulations and in real spike trains. We propose to test neuronal spike trains for deviations from the null-hypothesis FF = CV 2 . We show that cortical pyramidal neurons, recorded under controlled stationary input conditions in vitro, comply with this assumption. Performing a time-resolved joint analysis of CV 2 and FF of a single unit recording from the motor cortex of a behaving monkey we demonstrate how the dynamic change of their quantitative relation can be interpreted with respect to neuron intrinsic and extrinsic factors that influence cortical variability in vivo. Finally, we discuss the effect of several additional factors such as serial interval correlation and refractory period on the empiric relation of FF and CV 2 .
Journal of Computational Neuroscience, 2010
Spike time irregularity can be measured by the coefficient of variation. However, it overestimates the irregularity in the case of pronounced firing rate changes. Several alternative measures that are local in time and therefore relatively rate-independent were proposed. Here we compared four such measures: CV 2 , LV, IR and SI. First, we asked which measure is the most efficient for time-resolved analyses of experimental data. Analytical results show that CV 2 has the less variable estimates. Second, we derived useful properties of CV 2 for gamma processes. Third, we applied CV 2 on recordings from the motor cortex of a monkey performing a delayed motor task to characterize the irregularity, that can be modulated or not, and decoupled or not from firing rate. Neurons with a CV 2-rate decoupling have a rather constant CV 2 and discharge mainly irregularly. Neurons with a CV 2-rate coupling can modulate their CV 2 and explore a larger range of CV 2 values.
Cerebral Cortex Communications, 2020
The properties of motor cortical local field potential (LFP) beta oscillations have been extensively studied. Their relationship to the local neuronal spiking activity was also addressed. Yet, whether there is an intrinsic relationship between the amplitude of beta oscillations and the firing rate of individual neurons remains controversial. Some studies suggest a mapping of spike rate onto beta amplitude, while others find no systematic relationship. To help resolve this controversy, we quantified in macaque motor cortex the correlation between beta amplitude and neuronal spike count during visuomotor behavior. First, in an analysis termed “task-related correlation”, single-trial data obtained across all trial epochs were included. These correlations were significant in up to 32% of cases and often strong. However, a trial-shuffling control analysis recombining beta amplitudes and spike counts from different trials revealed these task-related correlations to reflect systematic, yet...
ARTICLE Large-scale spatiotemporal spike patterning consistent with wave propagation in motor cortex
Aggregate signals in cortex are known to be spatiotemporally organized as propagating waves across the cortical surface, but it remains unclear whether the same is true for spiking activity in individual neurons. Furthermore, the functional interactions between cortical neurons are well documented but their spatial arrangement on the cortical surface has been largely ignored. Here we use a functional network analysis to demonstrate that a subset of motor cortical neurons in non-human primates spatially coordinate their spiking activity in a manner that closely matches wave propagation measured in the beta oscillatory band of the local field potential. We also demonstrate that sequential spiking of pairs of neuron contains task-relevant information that peaks when the neurons are spatially oriented along the wave axis. We hypothesize that the spatial anisotropy of spike patterning may reflect the underlying organization of motor cortex and may be a general property shared by other cortical areas.
Journal of Neuroscience Methods, 2010
In analyzing neurophysiologic data, individual experimental trials are usually assumed to be statistically independent. However, many studies employing functional imaging and electrophysiology have shown that brain activity during behavioral tasks includes temporally correlated trial-to-trial fluctuations. This could lead to spurious results in statistical significance tests used to compare data from different interleaved behavioral conditions presented throughout an experiment. We characterize trialto-trial fluctuations in local field potentials recorded from the frontal cortex of a macaque monkey performing an oculomotor delayed response task. Our analysis identifies slow fluctuations (<0.1 Hz) of spectral power in 22/27 recording sessions. These trial-to-trial fluctuations are non-Gaussian, and call into question the statistical utility of standard trial shuffling. We compare our results with evidence for slow fluctuations in human functional imaging studies and other electrophysiologic studies in nonhuman primates.
Measurement of Time-Dependent Changes in the Irregularity of Neural Spiking
Journal of Neurophysiology, 2006
Irregularity of firing in spike trains has been associated with coding processes and information transfer or alternatively treated as noise. Previous studies of irregularity have mainly used the coefficient of variation (CV) of the interspike interval distribution. Proper estimation of CV requires a constant underlying firing rate, a condition that most experimental situations do not fulfill either within or across trials. Here we introduce a novel irregularity metric based on the ratio of adjacent intervals in the spike train. The new metric is not affected by firing rate and is very localized in time, so that it can be used to examine the time course of irregularity relative to an alignment marker. We characterized properties of the new metric with simulated spike trains of known characteristics, and then applied it to data recorded from 108 single neurons in the motor cortex of two monkeys during performance of a precision grip task. Fifty six cells were antidromically identified as pyramidal tract neurons (PTNs). Sixty one cells (30 PTNs) exhibited significant temporal modulation of their irregularity during task performance with the contralateral hand. The irregularity modulations generally differed in sign and latency from the modulations of firing rate. High irregularity tended to occur during the task phases requiring the most detailed control of movement, whereas neural firing became more regular during the steady hold phase. Such irregularity modulation could have important consequences for the response of downstream neurons, and may provide insight into the nature of the cortical code.
Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions
2021
Stimulus presentation is believed to quench neural response variability as measured by fanofactor (FF). However, the relative contribution of within trial spike irregularity (nY) and trial to trial rate variability (nRV) to FF reduction has remained elusive. Here, we introduce a principled approach for accurate estimation of variability components for a doubly stochastic point process which unlike previous methods allows for a time varying nY (aka f). Notably, analysis across multiple subcortical and cortical areas showed across the board reduction in rate variability. However, unlike what was previously thought, spiking irregularity was not constant in time and was even enhanced in some regions abating the quench in the post-stimulus FF. Simulations confirmed plausibility of a time varying nY arising from within and between pool correlations of excitatory and inhibitory neural inputs. By accurate parsing of neural variability, our approach constrains candidate mechanisms that give rise to observed rate variability and spiking irregularity within brain regions.
2020
Both neural activity and behavior of highly trained animals are strikingly variable across repetition of behavioral trials. The neural variability consistently decreases during behavioral tasks, in both sensory and motor cortices. The behavioral variability, on the other hand, changes depending on the difficulty of the task and animal performance. Here we study a mechanism for such variability in spiking neural network models with cluster topologies that enable multistability and attractor dynamics, features subserving functional roles such as decision-making, (working) memory and learning. Multistable attractors have been studied in spiking neural networks through clusters of strongly interconnected excitatory neurons. However, we show that this network topology results in the loss of excitation/inhibition balance and does not confer robustness against modulation of network activity. Moreover, it leads to widely separated firing rate states of single neurons, inconsistent with experimental observations. To overcome these problems we propose that a combination of excitatory and inhibitory clustering restores local excitation/inhibition balance. This network architecture is inspired by recent anatomical and physiological studies which point to increased local inhibitory connectivity and possible inhibitory clustering through connection strengths. We find that inhibitory clustering supports realistic spiking activity in terms of a biologically realistic firing rate, spiking irregularity, and trial-to-trial spike count variability. Furthermore, with the appropriate stimulation of network clusters, this network topology enabled us to qualitatively and quantitatively reproduce in vivo firing rate, variability dynamics and behavioral reaction times for different task conditions as observed in recordings from the motor cortex of behaving monkeys. .
Phase synchronization between LFP and spiking activity in motor cortex during movement preparation
Neurocomputing, 2007
A common approach to measure and assess cortical dynamics focuses on the analysis of mass signals, such as the local field potential (LFP), as an indicator for the underlying network activity. To improve our understanding of how such field potentials and cortical spiking dynamics are related, we analyzed the phase and amplitude relationships between extracellular recordings from motor cortex of monkey in a delayed pointing task. We applied methods from phase synchronization analysis to extract the instantaneous phase of the LFP time series and to characterize the degree of phase coupling between the spike train and oscillation cycles in a frequencyindependent manner. In particular, we investigated the dependence of observed phase preferences on the different periods of a behavioral trial. Furthermore, we present evidence to support the hypothesis that increased LFP oscillation amplitudes are related to a stronger degree of synchronization between the LFP and spike signals. However, neurons tend to keep a fixed phase relationship to the LFP independent of the amplitude or the choice of the electrode used to record the LFP.