Neural Ensemble Activity From Multiple Brain Regions Predicts Kinematic and Dynamic Variables in a Multiple Force Field Reaching Task

2006 ◽  
Vol 14 (2) ◽  
pp. 172-174 ◽  
Author(s):  
J.T. Francis ◽  
J.K. Chapin
Keyword(s):  
Force Field ◽  
Brain Regions ◽  
Reaching Task ◽  
Neural Ensemble ◽  
Ensemble Activity ◽  
Dynamic Variables

scholarly journals The organization of spatial coding in the hippocampus: a study of neural ensemble activity

1989 ◽  
Vol 9 (8) ◽  
pp. 2764-2775 ◽  
Author(s):  
H Eichenbaum ◽  
SI Wiener ◽  
ML Shapiro ◽  
NJ Cohen
Keyword(s):  
Spatial Coding ◽  
Neural Ensemble ◽  
Ensemble Activity

Neural Ensemble Coding during Working Memory Task in Rat Prefrontal Cortex

2011 ◽  
Vol 467-469 ◽  
pp. 1291-1296
Author(s):  
Wen Wen Bai ◽  
Xin Tian
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Firing Rate ◽  
Memory Task ◽  
Neural Population ◽  
Average Firing Rate ◽  
Population Activity ◽  
Ensemble Coding ◽  
Neural Ensemble ◽  
Ensemble Activity

Working memory is one of important cognitive functions and recent studies demonstrate that prefrontal cortex plays an important role in working memory. But the issue that how neural activity encodes during working memory task is still a question that lies at the heart of cognitive neuroscience. The aim of this study is to investigate neural ensemble coding mechanism via average firing rate during working memory task. Neural population activity was measured simultaneously from multiple electrodes placed in prefrontal cortex while rats were performing a working memory task in Y-maze. Then the original data was filtered by a high-pass filtering, spike detection and spike sorting, spatio-temporal trains of neural population were ultimately obtained. Then, the average firing rates were computed in a selected window (500ms) with a moving step (125ms). The results showed that the average firing rate were higher during workinig memory task, along with obvious ensemble activity. Conclusion: The results indicate that the working memory information is encoded with neural ensemble activity.

Application of Virtual Environments to Assessment of Human Motor Learning During Reaching Movements

2009 ◽  
Vol 18 (2) ◽  
pp. 112-124 ◽  
Author(s):  
Ali Asadi Nikooyan ◽  
Amir Abbas Zadpoor
Keyword(s):  
Force Field ◽  
Force Fields ◽  
Physical World ◽  
Reaching Movements ◽  
Viscous Force ◽  
Virtual Object ◽  
Learning Mechanisms ◽  
Reaching Task ◽  
Third Phase ◽  
Virtual Force

This paper studies learning of reaching movements in a dynamically variable virtual environment specially designed for this purpose. Learning of reaching movements in the physical world has been extensively studied by several researchers. In most of those studies, the subjects are asked to exercise reaching movements while being exposed to real force fields exerted through a robotic manipulandum. Those studies have contributed to our understanding of the mechanisms used by the human cognitive system to learn reaching movements in the physical world. The question that remains to be answered is how the learning mechanism in the physical world relates to its counterpart in the virtual world where the real force fields are replaced by virtual force fields. A limited number of studies have already addressed this question and have shown that there are, actually, quite a number of relationships between the learning mechanisms in these two different environments. In this study, we are focused on gaining a more in-depth understanding of these relationships. In our experiments, the subjects are asked to guide a virtual object to a desired target on a computer screen using a mouse. The movement of the virtual object is affected by a viscous virtual force field that is sensed by the examinees through their visual system. Three groups of examinees are used for the experiments. All the examinees are first trained in the null-field condition. Then, the viscous force field is introduced either suddenly (for the two first groups) or gradually (for the last group). While the first and third groups of the examinees used their dominant arm to guide the virtual object in the second step, the second group used their nondominant arm. Generalization of the learning from the dominant to the nondominant arm and vice versa was studied in the third phase of the experiments. Finally, the force field was removed and the examinees were asked to repeat the reaching task to study the so-called aftereffects phenomenon. The results of the experiments are compared with the studies performed in the physical world. It is shown that the trends of learning and generalization are similar to what is observed in the physical world for a sudden application of the virtual force field. However, the generalization behavior of the examinees is somewhat different from the physical world if the force field is gradually applied.

Sensorimotor encoding by synchronous neural ensemble activity at multiple levels of the somatosensory system

Science ◽  
1995 ◽  
Vol 268 (5215) ◽  
pp. 1353-1358 ◽  
Author(s):  
M. Nicolelis ◽  
L. Baccala ◽  
R. Lin ◽  
J. Chapin
Keyword(s):  
Somatosensory System ◽  
Neural Ensemble ◽  
Multiple Levels ◽  
Ensemble Activity

scholarly journals Enhanced population coding for rewarded choices in the medial frontal cortex of the mouse

2018 ◽  
Author(s):  
Michael J. Siniscalchi ◽  
Hongli Wang ◽  
Alex C. Kwan
Keyword(s):  
Frontal Cortex ◽  
Population Coding ◽  
Medial Frontal Cortex ◽  
Instrumental Behavior ◽  
Neural Representations ◽  
Neural Ensemble ◽  
Motor Region ◽  
Ensemble Activity ◽  
Insight Into ◽  
Selection Of

AbstractInstrumental behavior is characterized by the selection of actions based on the degree to which they lead to a desired outcome. However, we lack a detailed understanding of how rewarded actions are reinforced and preferentially implemented. In rodents, the medial frontal cortex is hypothesized to play an important role in this process, based in part on its capacity to encode chosen actions and their outcomes. We therefore asked how neural representations of choice and outcome might interact to facilitate instrumental behavior. To investigate this question, we imaged neural ensemble activity in layer 2/3 of the secondary motor region (M2) while mice engaged in a two-choice auditory discrimination task with probabilistic outcomes. Correct choices could result in one of three reward amounts (single-, double-, or omitted-reward), which allowed us to measure neural and behavioral effects of reward magnitude, as well as its categorical presence or absence. Single-unit and population decoding analyses revealed a consistent influence of outcome on choice signals in M2. Specifically, rewarded choices were more robustly encoded relative to unrewarded choices, with little dependence on the exact magnitude of reinforcement. Our results provide insight into the integration of past choices and outcomes in the rodent brain during instrumental behavior.

scholarly journals Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Duho Sin ◽  
Jinsoo Kim ◽  
Jee Hyun Choi ◽  
Sung-Phil Kim
Keyword(s):  
Neural Networks ◽  
Maximum Entropy ◽  
Brain Regions ◽  
Time Varying ◽  
Maximum Entropy Model ◽  
Firing Activity ◽  
Entropy Model ◽  
Neuronal Ensemble ◽  
Neural Information ◽  
Ensemble Activity

As advances in neurotechnology allow us to access the ensemble activity of multiple neurons simultaneously, many neurophysiologic studies have investigated how to decode neuronal ensemble activity. Neuronal ensemble activity from different brain regions exhibits a variety of characteristics, requiring substantially different decoding approaches. Among various models, a maximum entropy decoder is known to exploit not only individual firing activity but also interactions between neurons, extracting information more accurately for the cases with persistent neuronal activity and/or low-frequency firing activity. However, it does not consider temporal changes in neuronal states and therefore would be susceptible to poor performance for nonstationary neuronal information processing. To address this issue, we develop a novel decoder that extends a maximum entropy decoder to take time-varying neural information into account. This decoder blends a dynamical system model of neural networks into the maximum entropy model to better suit for nonstationary circumstances. From two simulation studies, we demonstrate that the proposed dynamic maximum entropy decoder could cope well with time-varying information, which the conventional maximum entropy decoder could not achieve. The results suggest that the proposed decoder may be able to infer neural information more effectively as it exploits dynamical properties of underlying neural networks.

scholarly journals Firing rate models for gamma oscillations

2019 ◽  
Vol 121 (6) ◽  
pp. 2181-2190 ◽  
Author(s):  
Stephen Keeley ◽  
Áine Byrne ◽  
André Fenton ◽  
John Rinzel
Keyword(s):  
Firing Rate ◽  
Computational Models ◽  
Network Models ◽  
Gamma Oscillations ◽  
Brain Regions ◽  
Dynamic Feature ◽  
Gamma Rhythm ◽  
Firing Rate Models ◽  
General Network ◽  
Dynamic Variables

Gamma oscillations are readily observed in a variety of brain regions during both waking and sleeping states. Computational models of gamma oscillations typically involve simulations of large networks of synaptically coupled spiking units. These networks can exhibit strongly synchronized gamma behavior, whereby neurons fire in near synchrony on every cycle, or weakly modulated gamma behavior, corresponding to stochastic, sparse firing of the individual units on each cycle of the population gamma rhythm. These spiking models offer valuable biophysical descriptions of gamma oscillations; however, because they involve many individual neuronal units they are limited in their ability to communicate general network-level dynamics. Here we demonstrate that few-variable firing rate models with established synaptic timescales can account for both strongly synchronized and weakly modulated gamma oscillations. These models go beyond the classical formulations of rate models by including at least two dynamic variables per population: firing rate and synaptic activation. The models’ flexibility to capture the broad range of gamma behavior depends directly on the timescales that represent recruitment of the excitatory and inhibitory firing rates. In particular, we find that weakly modulated gamma oscillations occur robustly when the recruitment timescale of inhibition is faster than that of excitation. We present our findings by using an extended Wilson-Cowan model and a rate model derived from a network of quadratic integrate-and-fire neurons. These biophysical rate models capture the range of weakly modulated and coherent gamma oscillations observed in spiking network models, while additionally allowing for greater tractability and systems analysis. NEW & NOTEWORTHY Here we develop simple and tractable models of gamma oscillations, a dynamic feature observed throughout much of the brain with significant correlates to behavior and cognitive performance in a variety of experimental contexts. Our models depend on only a few dynamic variables per population, but despite this they qualitatively capture features observed in previous biophysical models of gamma oscillations that involve many individual spiking units.

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