Detecting multineuronal temporal patterns in parallel spike trains

We present a non-parametric and computationally efficient method that detects spatiotemporal firing patterns and pattern sequences in parallel spike trains and tests whether the observed numbers of repeating patterns and sequences on a given timescale are significantly different from those expected by chance. The method is generally applicable and uncovers coordinated activity with arbitrary precision by comparing it to appropriate surrogate data. The analysis of coherent patterns of spatially and temporally distributed spiking activity on various timescales enables the immediate tracking of diverse qualities of coordinated firing related to neuronal state changes and information processing. We apply the method to simulated data and multineuronal recordings from rat visual cortex and show that it reliably discriminates between data sets with random pattern occurrences and with additional exactly repeating spatiotemporal patterns and pattern sequences. Multineuronal cortical spiking activity appears to be precisely coordinated and exhibits a sequential organization beyond the cell assembly concept

Detection of dependence patterns with delay

The Unitary Events (UE) method is a popular and efficient method used this last decade to detect dependence patterns of joint spike activity among simultaneously recorded neurons. The first introduced method is based on binned coincidence count \citep{Grun1996} and can be applied on two or more simultaneously recorded neurons. Among the improvements of the methods, a transposition to the continuous framework has recently been proposed in \citep{muino2014frequent} and fully investigated in \citep{MTGAUE} for two neurons. The goal of the present paper is to extend this study to more than two neurons. The main result is the determination of the limit distribution of the coincidence count. This leads to the construction of an independence test between $L\geq 2$ neurons. Finally we propose a multiple test procedure via a Benjamini and Hochberg approach \citep{Benjamini1995}. All the theoretical results are illustrated by a simulation study, and compared to the UE method proposed in \citep{Grun2002}. Furthermore our method is applied on real data

Efficient Identification of Assembly Neurons within Massively Parallel Spike Trains

The chance of detecting assembly activity is expected to increase if the spiking activities of large numbers of neurons are recorded simultaneously. Although such massively parallel recordings are now becoming available, methods able to analyze such data for spike correlation are still rare, as a combinatorial explosion often makes it infeasible to extend methods developed for smaller data sets. By evaluating pattern complexity distributions the existence of correlated groups can be detected, but their member neurons cannot be identified. In this contribution, we present approaches to actually identify the individual neurons involved in assemblies. Our results may complement other methods and also provide a way to reduce data sets to the “relevant” neurons, thus allowing us to carry out a refined analysis of the detailed correlation structure due to reduced computation time

Correlated Activity and Corticothalamic Cell Function in the Early Mouse Visual System

Vision has long been the model for understanding cortical function. Great progress has been made in understanding the transformations that occur within some primary visual cortex (V1) layers, like the emergence of orientation selectivity in layer 4. Less is known about other V1 circuit elements, like the shaping of V1 input via corticothalamic projections, or the population structure of the cortico-cortical output in layer 2/3. Here, we use the mouse early visual system to investigate the structure and function of circuit elements in V1. We use two approaches: comparative physiology and optogenetics. We measured the structure of pairwise correlations in the output layer 2/3 using extracellular recordings. We find that despite a lack of organization in mouse V1 seen in other species, the specificity of connections preserves a correlation structure on multiple timescales. To investigate the role of corticogeniculate projections, we utilize a transgenic mouse line to specifically and reversibly manipulate these projections with millisecond precision. We find that activity of these cells results a mix of inhibition and excitation in the thalamus, is not spatiotemporally specific, and can affect correlated activity. Finally, we classify mouse thalamic cells according to stimuli used for cell classification in primates and cats, finding some, but not complete, homology to the processing streams of primate thalamus and further highlighting fundamentals of mammalian visual system organization

Dynamic Functional Connectivity Between Cortex and Muscles

The motor-cortex is recognized as the origin of the major direct path from cortex to muscles. Although it has been studied for over a century, relatively little is known about how the motor cortex facilitates reach-to-grasp movements. We collected a rich dataset from monkeys trained to reach and grasp objects of different shapes, presented at various orientations and spatial locations. We simultaneously recorded single-unit activity from motor cortical areas (mainly the caudal bank of the pre-central gyrus), EMG activity from selected muscles (in the arm, wrist and hand) and high-resolution kinematic data from the wrist and hand. We show that motor-cortical neurons modulate their activity in an object specific manner, resulting in object specific co-activation of muscles and joint movements. We studied the multivariate relationships between the firing rates of individual neurons, EMG, joint angles and joint angle velocities and found that both EMG and kinematic features were encoded in the neural firing rates. Kinematic features were much better predictors of neural firing rates than EMG. We found that the best predictors of neural firing rates were neither individual muscles or joints, nor kinematic or EMG synergies extracted using PCA/ICA, but neuron-specific combinations of EMG and kinematic features. We show better predictions of both muscle activations and JA values by combining the activity of a few tens of sequentially recorded neurons; suggesting that neural activity contains synergistic information related to EMG, not independently present in individual neurons. By using functional connectivity, defined as the probability of observing changes in EMG following spikes from a trigger neuron, we further elucidated motor cortical activity to muscle activation. By studying both the short-time scale functional connectivity, on the order of milliseconds; and long-time scale functional connectivity, on the order of hundreds of milliseconds, we found that flexible long-time scale functional connections between individual neurons and muscles were modulated by kinematic features that could account for the relatively weaker neural firing rate relation to EMG. To support our findings, we show examples of simultaneous short-time scale functional connectivity and conclude that neuronal-muscular functional connectivity is flexible and task-dependent