Dopaminergic dysregulation can cause motor dysfunction, but the mechanisms underlying dopamine-related motor disorders remain under debate. We used an inducible and reversible pharmacogenetic approach in dopamine transporter knockout mice to investigate the simultaneous activity of neuronal ensembles in the dorsolateral striatum and primary motor cortex during hyperdopaminergia ( approximately 500% of controls) with hyperkinesia, and after rapid and profound dopamine depletion (<0.2%) with akinesia in the same animal. Surprisingly, although most cortical and striatal neurons ( approximately 70%) changed firing rate during the transition between dopamine-related hyperkinesia and akinesia, the overall cortical firing rate remained unchanged. Conversely, neuronal oscillations and ensemble activity coordination within and between cortex and striatum did change rapidly between these periods. During hyperkinesia, corticostriatal activity became largely asynchronous, while during dopamine-depletion the synchronicity increased. Thus, dopamine-related disorders like Parkinson's disease may not stem from changes in the overall levels of cortical activity, but from dysfunctional activity coordination in corticostriatal circuits.

Multielectrode arrays (MEAs) allow for acquisition of multisite electrophysiological activity with submillisecond temporal resolution from neural preparations. The signal to noise ratio from such arrays has recently been improved by substrate perforations that allow negative pressure to be applied to the tissue; however, such arrays are not optically transparent, limiting their potential to be combined with optical-based technologies. We present here multi-suction electrode arrays (MSEAs) in quartz that yield a substantial increase in the detected number of units and in signal to noise ratio from mouse cortico-hippocampal slices and mouse retina explants. This enables the visualization of stronger cross correlations between the firing rates of the various sources. Additionally, the MSEA's transparency allows us to record voltage sensitive dye activity from a leech ganglion with single neuron resolution using widefield microscopy simultaneously with the electrode array recordings. The combination of enhanced electrical signals and compatibility with optical-based technologies should make the MSEA a valuable tool for investigating neuronal circuits.

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BackgroundRecent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increases abruptly upon starting to operate the interface. In contrast, neural modulations that are correlated with the profile of the trajectory remain relatively unchanged. Furthermore, the enhanced modulations subside with further training, mirroring the trend in task performance, which degraded when starting to operate the interface and improved gradually with training [1]. The interpretation of the enhanced modulations and the characterization of the signals that they may encode are of major interest for understanding human motor learning and control, the improvement of future BMIs and the development of effective rehabilitation programs. Results: experimentalHere we investigate the hypothesis that the enhanced modulations reflect internal representation of estimation errors, and the consequent correction or explorative commands. This hypothesis is supported by further BMI experiments involving center-out movements to randomly located targets on a fixed circle. These BMI experiments facilitate the evaluation of neural modulations (across different reaching movements) at a fixed delay after target appearance. The analysis demonstrates that the relative variance of neural modulations is low during the inter-trials, increases during the initial part of the movement -after target onset -and remains flat in the later part of the movement. When operating the BMI, the relative variance of the neural modulations increases to higher levels and the trials extend longer. Results: modeling and simulationsThe observed enhancement in neural modulations may be interpreted in the context of three computational motor control models: (i) hybrid control, (ii) optimal control, and (iii) dual control. The optimal control model is investigated in detail by simulating neural populations that are tuned to both the open-loop and close-loop estimation and control signals. Simulation results demonstrate that this model can explain the observed enhancement in the neural modulations when switching from skilled to BMI operation. Both the modeling and experimental results support the hypothesis that the enhanced neural modulations reflect error processing and suggest that they may be used to improve the operation of BMIs.

IntroductionThe wide interest in spike-train variability stems mainly from its limiting effect on the accuracy of neural coding and thus the reliability of behavioral responses [1,2]. Consequently, most investigations have focused on determining spike-train variability under identical conditions. However, during natural and novel conditions, spiketrain variability reflects also the variability of the underlying rate. Under the assumption of rate-coding, it is this variability that can reflect the changes in the encoded signals and thus is of major interest for neural decoding in general and Brain Machine Interfaces (BMI) in particular.During planning and execution of reaching movements, the firing rate of cortical motor neurons encodes multiple motor, sensory, and cognitive variables [3][4][5]. In this context, rate variability is considered the signal, while only the inherent variability of the spike trains, beyond rate variability, is considered the noise (i.e., 'neural noise'). These two components can be estimated from the recorded neural activity under the assumption that the spike trains are realizations of doubly stochastic Poisson processes -the simplest point processes that can encode stochastic signals [6]. Analyzing spike-trains recorded during BMI experiments, we have demonstrated that the fraction of the variance that is attributed to rate-variability is higher when the monkeys operate the BMI [6].Here we focus on investigating the signal-to-noise ratio (SNR) in the neural activity, i.e., the ratio between ratevariability and noise-variability -the two components of spike-train variability -and how it varies with the binwidth (BW). Theoretical analysis indicates that the SNR should increase with the BW; increasing linearly for short BWs before saturating for long BWs. Since increasing BW has an adverse effect on the update rate, we suggest that the ratio SNR/BW captures the trade-off between SNR and update rate. Furthermore, this ratio is related to the capacity of the neural channel under different assumptions.Analysis of neural spike-trains recorded during BMI experiments from different cortical areas indicates that the SNR indeed increases with the BW as expected, except for very short BWs. At very short BWs the SNR increases faster than expected, possibility due to dead-time effects or other deviations from the theoretical assumption. Thus the SNR/BW curves exhibit a broad peak, and it is possible to define an optimal BW that maximizes the SNR/BW. Interestingly, for the mean SNR/BW, the optimal BW is around 100 msec -the BW that was selected by trial and error for decoding the neural activity in the BMI. Within the context of the theoretical analysis this can be interpreted as optimizing the trade-off between the SNR and update rate, or alternatively as maximizing the capacity of the neural channel.