291 research outputs found
Low-frequency cortical activity is a neuromodulatory target that tracks recovery after stroke.
Recent work has highlighted the importance of transient low-frequency oscillatory (LFO; <4βHz) activity in the healthy primary motor cortex during skilled upper-limb tasks. These brief bouts of oscillatory activity may establish the timing or sequencing of motor actions. Here, we show that LFOs track motor recovery post-stroke and can be a physiological target for neuromodulation. In rodents, we found that reach-related LFOs, as measured in both the local field potential and the related spiking activity, were diminished after stroke and that spontaneous recovery was closely correlated with their restoration in the perilesional cortex. Sensorimotor LFOs were also diminished in a human subject with chronic disability after stroke in contrast to two non-stroke subjects who demonstrated robust LFOs. Therapeutic delivery of electrical stimulation time-locked to the expected onset of LFOs was found to significantly improve skilled reaching in stroke animals. Together, our results suggest that restoration or modulation of cortical oscillatory dynamics is important for the recovery of upper-limb function and that they may serve as a novel target for clinical neuromodulation
Neuronal Variability during Handwriting: Lognormal Distribution
We examined time-dependent statistical properties of electromyographic (EMG) signals recorded from intrinsic hand muscles during handwriting. Our analysis showed that trial-to-trial neuronal variability of EMG signals is well described by the lognormal distribution clearly distinguished from the Gaussian (normal) distribution. This finding indicates that EMG formation cannot be described by a conventional model where the signal is normally distributed because it is composed by summation of many random sources. We found that the variability of temporal parameters of handwriting - handwriting duration and response time - is also well described by a lognormal distribution. Although, the exact mechanism of lognormal statistics remains an open question, the results obtained should significantly impact experimental research, theoretical modeling and bioengineering applications of motor networks. In particular, our results suggest that accounting for lognormal distribution of EMGs can improve biomimetic systems that strive to reproduce EMG signals in artificial actuators
Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control
It is widely accepted that the complex dynamics characteristic of recurrent
neural circuits contributes in a fundamental manner to brain function. Progress
has been slow in understanding and exploiting the computational power of
recurrent dynamics for two main reasons: nonlinear recurrent networks often
exhibit chaotic behavior and most known learning rules do not work in robust
fashion in recurrent networks. Here we address both these problems by
demonstrating how random recurrent networks (RRN) that initially exhibit
chaotic dynamics can be tuned through a supervised learning rule to generate
locally stable neural patterns of activity that are both complex and robust to
noise. The outcome is a novel neural network regime that exhibits both
transiently stable and chaotic trajectories. We further show that the recurrent
learning rule dramatically increases the ability of RRNs to generate complex
spatiotemporal motor patterns, and accounts for recent experimental data
showing a decrease in neural variability in response to stimulus onset
Balanced Input Allows Optimal Encoding in a Stochastic Binary Neural Network Model: An Analytical Study
Recent neurophysiological experiments have demonstrated a remarkable effect of attention on the underlying neural activity that suggests for the first time that information encoding is indeed actively influenced by attention. Single cell recordings show that attention reduces both the neural variability and correlations in the attended condition with respect to the non-attended one. This reduction of variability and redundancy enhances the information associated with the detection and further processing of the attended stimulus. Beyond the attentional paradigm, the local activity in a neural circuit can be modulated in a number of ways, leading to the general question of understanding how the activity of such circuits is sensitive to these relatively small modulations. Here, using an analytically tractable neural network model, we demonstrate how this enhancement of information emerges when excitatory and inhibitory synaptic currents are balanced. In particular, we show that the network encoding sensitivity -as measured by the Fisher information- is maximized at the exact balance. Furthermore, we find a similar result for a more realistic spiking neural network model. As the regime of balanced inputs has been experimentally observed, these results suggest that this regime is functionally important from an information encoding standpoint
Characterization of Neural Tuning: Visual Lead-in Movements Generalize in Speed and Distance
Prior work has shown that independent motor memories of opposing dynamics can be learned when the movements are preceded by unique lead-in movements, each associated with a different direction of dynamics. Here we examine generalization effects using visual lead-in movements. Specifically, we test how variations in lead-in kinematics, in terms of duration, speed and distance, effect the expression of the learned motor memory. We show that the motor system is more strongly affected by changes in the duration of the movement, whereas longer movement distances have no effect
Fine-Scale Temporal Dynamics of a Fragmented Lotic Microbial Ecosystem
Microbial ecosystems are often assumed to be relatively stable over short periods of time, but this assumption is seldom tested. An urban stream influenced by both flow and varying levels of anthropogenic influences is expected to have high temporal variability in microbial composition, and short-term ecological instability. Thus, we analyzed the bacterioplankton composition of a weir-fragmented urban stream using Automated rRNA Intergenic Spacer Analysis (ARISA). A total of 46 sequential samples were collected in July 2009 for 7 days, every 7β
hours, from both the up-stream side of the weir (stream water) and the downstream side of the weir (estuarine) water. Bray-Curtis similarity based analysis showed a clear division between upstream and downstream communities. A sudden pH drop induced change in both communities, but composition stability partially recovered within less than a day. Thus, our results show that microbial ecosystems can change rapidly, but re-establish a new equilibrium relatively quickly
Of Toasters and Molecular Ticker Tapes
Experiments in systems neuroscience can be seen as consisting of three steps: (1) selecting the signals we are interested in, (2) probing the system with carefully chosen stimuli, and (3) getting data out of the brain. Here I discuss how emerging techniques in molecular biology are starting to improve these three steps. To estimate its future impact on experimental neuroscience, I will stress the analogy of ongoing progress with that of microprocessor production techniques. These techniques have allowed computers to simplify countless problems; because they are easier to use than mechanical timers, they are even built into toasters. Molecular biology may advance even faster than computer speeds and has made immense progress in understanding and designing molecules. These advancements may in turn produce impressive improvements to each of the three steps, ultimately shifting the bottleneck from obtaining data to interpreting it
Monkey Steering Responses Reveal Rapid Visual-Motor Feedback
The neural mechanisms underlying primate locomotion are largely unknown. While behavioral and theoretical work has provided a number of ideas of how navigation is controlled, progress will require direct physiolgical tests of the underlying mechanisms. In turn, this will require development of appropriate animal models. We trained three monkeys to track a moving visual target in a simple virtual environment, using a joystick to control their direction. The monkeys learned to quickly and accurately turn to the target, and their steering behavior was quite stereotyped and reliable. Monkeys typically responded to abrupt steps of target direction with a biphasic steering movement, exhibiting modest but transient overshoot. Response latencies averaged approximately 300 ms, and monkeys were typically back on target after about 1 s. We also exploited the variability of responses about the mean to explore the time-course of correlation between target direction and steering response. This analysis revealed a broad peak of correlation spanning approximately 400 ms in the recent past, during which steering errors provoke a compensatory response. This suggests a continuous, visual-motor loop controls steering behavior, even during the epoch surrounding transient inputs. Many results from the human literature also suggest that steering is controlled by such a closed loop. The similarity of our results to those in humans suggests the monkey is a very good animal model for human visually guided steering
Neural Network Mechanisms Underlying Stimulus Driven Variability Reduction
It is well established that the variability of the neural activity across trials, as measured by the Fano factor, is elevated. This fact poses limits on information encoding by the neural activity. However, a series of recent neurophysiological experiments have changed this traditional view. Single cell recordings across a variety of species, brain areas, brain states and stimulus conditions demonstrate a remarkable reduction of the neural variability when an external stimulation is applied and when attention is allocated towards a stimulus within a neuron's receptive field, suggesting an enhancement of information encoding. Using an heterogeneously connected neural network model whose dynamics exhibits multiple attractors, we demonstrate here how this variability reduction can arise from a network effect. In the spontaneous state, we show that the high degree of neural variability is mainly due to fluctuation-driven excursions from attractor to attractor. This occurs when, in the parameter space, the network working point is around the bifurcation allowing multistable attractors. The application of an external excitatory drive by stimulation or attention stabilizes one specific attractor, eliminating in this way the transitions between the different attractors and resulting in a net decrease in neural variability over trials. Importantly, non-responsive neurons also exhibit a reduction of variability. Finally, this reduced variability is found to arise from an increased regularity of the neural spike trains. In conclusion, these results suggest that the variability reduction under stimulation and attention is a property of neural circuits
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