Patterns of spatio-temporal correlations in the neural activity of the cat motor cortex during trained forelimb movements

Intracortical Microelectrode Array Unit Yield under Chronic Conditions: A Comparative Evaluation

While microelectrode arrays (MEAs) offer the promise of elucidating functional neural circuitry and serve as the basis for a cortical neuroprosthesis, the challenge of designing and demonstrating chronically reliable technology remains. Numerous studies report “chronic” data but the actual time spans and performance measures corresponding to the experimental work vary. In this study, we reviewed the experimental durations that constitute chronic studies across a range of MEA types and animal species to gain an understanding of the widespread variability in reported study duration. For rodents, which are the most commonly used animal model in chronic studies, we examined active electrode yield (AEY) for different array types as a means to contextualize the study duration variance, as well as investigate and interpret the performance of custom devices in comparison to conventional MEAs. We observed wide-spread variance within species for the chronic implantation period and an AEY that decayed linearly in rodent models that implanted commercially-available devices. These observations provide a benchmark for comparing the performance of new technologies and highlight the need for consistency in chronic MEA studies. Additionally, to fully derive performance under chronic conditions, the duration of abiotic failure modes, biological processes induced by indwelling probes, and intended application of the device are key determinants.

Investigation into machine learning algorithms as applied to motor cortex signals for classification of movement stages

Neuroinformatics has recently emerged as a powerful field for the statistical analysis of neural data. This study uses machine learning techniques to analyze neural spiking activities within a population of neurons with the aim of finding spiking patterns associated with different stages of movement. Neural data was recorded during many experimental trials of a cat performing a skilled reach and withdrawal task. Using Weka and the LibSVM classifier, movement stages of the skilled task were identified with a high degree of certainty achieving an area-under-curve (AUC) of the Receiver Operating Characteristic of between 0.900 and 0.997 for the combined data set. Through feature selection, the identification of significant neurons has been made easier. Given this encouraging classification performance, the extension to automatic classification and updating of control models for use with neural prostheses will enable regular adjustments capable of compensating for neural changes.

The use of dendrograms to describe the electrical activity of motoneurons underlying behaviors in leeches

The present manuscript aims at identifying patterns of electrical activity recorded from neurons of the leech nervous system, characterizing specific behaviors. When leeches are at rest, the electrical activity of neurons and motoneurons is poorly correlated. When leeches move their head and/or tail, in contrast, action potential (AP) firing becomes highly correlated. When the head or tail suckers detach, specific patterns of electrical activity are detected. During elongation and contraction the electrical activity of motoneurons in the Medial Anterior and Dorsal Posterior nerves increase, respectively, and several motoneurons are activated both during elongation and contraction. During crawling, swimming, and pseudo-swimming patterns of electrical activity are better described by the dendrograms of cross-correlations of motoneurons pairs. Dendrograms obtained from different animals exhibiting the same behavior are similar and by averaging these dendrograms we obtained a template underlying a given behavior. By using this template, the corresponding behavior is reliably identified from the recorded electrical activity. The analysis of dendrograms during different leech behavior reveals the fine orchestration of motoneurons firing specific to each stereotyped behavior. Therefore, dendrograms capture the subtle changes in the correlation pattern of neuronal networks when they become involved in different tasks or functions.

Motor cortical networks for skilled movements have dynamic properties that are related to accurate reaching

Neurons in the Primary Motor Cortex (MI) are known to form functional ensembles with one another in order to produce voluntary movement. Neural network changes during skill learning are thought to be involved in improved fluency and accuracy of motor tasks. Unforced errors during skilled tasks provide an avenue to study network connections related to motor learning. In order to investigate network activity in MI, microwires were implanted in the MI of cats trained to perform a reaching task. Spike trains from eight groups of simultaneously recorded cells (95 neurons in total) were acquired. A point process generalized linear model (GLM) was developed to assess simultaneously recorded cells for functional connectivity during reaching attempts where unforced errors or no errors were made. Whilst the same groups of neurons were often functionally connected regardless of trial success, functional connectivity between neurons was significantly different at fine time scales when the outcome of task performance changed. Furthermore, connections were shown to be significantly more robust across multiple latencies during successful trials of task performance. The results of this study indicate that reach-related neurons in MI form dynamic spiking dependencies whose temporal features are highly sensitive to unforced movement errors.

A Granger causality measure for point process models of ensemble neural spiking activity

The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron's spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains.

Compositionality of arm movements can be realized by propagating synchrony

We present a biologically plausible spiking neuronal network model of free monkey scribbling that reproduces experimental findings on cortical activity and the properties of the scribbling trajectory. The model is based on the idea that synfire chains can encode movement primitives. Here, we map the propagation of activity in a chain to a linearly evolving preferred velocity, which results in parabolic segments that fulfill the two-thirds power law. Connections between chains that match the final velocity of one encoded primitive to the initial velocity of the next allow the composition of random sequences of primitives with smooth transitions. The model provides an explanation for the segmentation of the trajectory and the experimentally observed deviations of the trajectory from the parabolic shape at primitive transition sites. Furthermore, the model predicts low frequency oscillations (<10 Hz) of the motor cortex local field potential during ongoing movements and increasing firing rates of non-specific motor cortex neurons before movement onset.

Statistical inference for assessing functional connectivity of neuronal ensembles with sparse spiking data

The ability to accurately infer functional connectivity between ensemble neurons using experimentally acquired spike train data is currently an important research objective in computational neuroscience. Point process generalized linear models and maximum likelihood estimation have been proposed as effective methods for the identification of spiking dependency between neurons. However, unfavorable experimental conditions occasionally results in insufficient data collection due to factors such as low neuronal firing rates or brief recording periods, and in these cases, the standard maximum likelihood estimate becomes unreliable. The present studies compares the performance of different statistical inference procedures when applied to the estimation of functional connectivity in neuronal assemblies with sparse spiking data. Four inference methods were compared: maximum likelihood estimation, penalized maximum likelihood estimation, using either l(2) or l(1) regularization, and hierarchical Bayesian estimation based on a variational Bayes algorithm. Algorithmic performances were compared using well-established goodness-of-fit measures in benchmark simulation studies, and the hierarchical Bayesian approach performed favorably when compared with the other algorithms, and this approach was then successfully applied to real spiking data recorded from the cat motor cortex. The identification of spiking dependencies in physiologically acquired data was encouraging, since their sparse nature would have previously precluded them from successful analysis using traditional methods.

Differential involvement of excitatory and inhibitory neurons of cat motor cortex in coincident spike activity related to behavioral context

To assess temporal associations in spike activity between pairs of neurons in the primary motor cortex (MI) related to different behaviors, we compared the incidence of coincident spiking activity of task-related (TR) and non-task-related (NTR) neurons during a skilled motor task and sitting quietly in adult cats (Felis domestica). Chronically implanted microwires were used to record spike activity of MI neurons in four animals (two male and two female) trained to perform a skilled reaching task or sit quietly. Neurons were identified as TR if spike activity was modulated during the task (and NTR if not). Based on spike characteristics, they were also classified as either regular-spiking (RS, putatively excitatory) or fast-spiking (FS, putatively inhibitory) neurons. Temporal associations in the activities of simultaneously recorded neurons were evaluated using shuffle-corrected cross-correlograms. Pairs of NTR and TR neurons showed associations in their firing patterns over wide areas of MI (representing forelimb and hindlimb movements) during quiet sitting, more commonly involving RS neurons. During skilled task performance, however, significantly coincident firing was seen almost exclusively between TR neurons in a smaller part of MI (representing forelimb movements), involving mainly FS neurons. The findings of this study show evidence for widespread interactions in MI when the animal sits quietly, which changes to a more specific and restricted pattern of interactions during task performance. Different populations of excitatory and inhibitory neurons appear to be synchronized during skilled movement and quiet sitting.

Neural integration of reaching and posture: interhemispheric spike correlations in cat motor cortex

To study the interlimb coordination of reaching and postural movements, chronically implanted microelectrodes were used to record single unit activity from the primary motor cortex (MI) of cats during performance of a trained reaching task. Recordings were made from both cerebral hemispheres to record neurons that modulated their activity during reaching (reach-related neurons) and supportive (posture-related neurons) movements of either forelimb. Evidence of temporal associations in the activities of simultaneously recorded reach- and posture-related neurons was evaluated using shuffle-corrected cross correlograms. The spike activity of approximately 34% of reach-related neurons was temporally correlated with the spike activity of simultaneously recorded posture-related neurons in the opposite motor cortex. Significant associations in the spike activity of neurons recorded from homotopic representational areas of the motor cortex in opposite hemispheres have not previously been reported. These interactions may have an important role in the coordination of opposite forelimbs during reaching movements and postural actions.