scholarly journals Mechanisms Explaining Transitions between Tonic and Phasic Firing in Neuronal Populations as Predicted by a Low Dimensional Firing Rate Model

PLoS ONE ◽  
2010 ◽  
Vol 5 (9) ◽  
pp. e12695 ◽  
Author(s):  
Anca R. Radulescu
BIOPHYSICS ◽  
2010 ◽  
Vol 55 (4) ◽  
pp. 592-599 ◽  
Author(s):  
A. Yu. Buchin ◽  
A. V. Chizhov
Keyword(s):  

1991 ◽  
Vol 65 (3) ◽  
pp. 572-589 ◽  
Author(s):  
T. M. Wannier ◽  
M. A. Maier ◽  
M. C. Hepp-Reymond

1. Single cell activity was investigated in the precentral motor (MI) and postcentral somatosensory (SI) cortex of the monkey to compare the neuronal activity related to the control of isometric force in the precision grip and to assess the participation of SI in motor control. 2. Three monkeys (Macaca fascicularis) were trained in a visual step-tracking paradigm to generate and precisely maintain force on a transducer held between thumb and index finger. Great care was taken to have the monkeys use only their fingers without moving the wrist or proximal joints. In two monkeys electromyographic (EMG) activity was checked in 23 muscles over several sessions. 3. Five similar classes of task-related firing patterns were found in both SI and MI cortical hand and finger representations, but their relative proportions differed. The majority of the SI neurons were phasically or phasic-tonically active (61%), whereas in MI the neurons that decreased their firing rate with force were most frequent (42%). 4. The timing of activity changes related to the onset of force increase from low to higher levels strongly differed in the two neuronal populations. In SI, only 14% of the task-related neurons increased or decreased their firing rate before the onset of force increase, in contrast to 56% in MI. Only 3% of the SI neurons showed changes before the earliest EMG activation. 5. In both SI and MI neurons with tonic and phasic-tonic, increasing or decreasing discharge patterns disclosed a relationship between neuronal activity and static force. Distinction was made between neurons modulating their activity in a monotonic way and those that were active only at one force level and had a kind of recruitment or deactivation threshold. The latter ones were more frequent in MI than in SI, and in the neuron population with decreasing firing patterns. For the neurons with increases in activity, statistically significant linear correlations between firing rate and force were found more frequently in MI than in SI, where the proportion of nonsignificant correlations was relatively high (35% vs. 15% in MI). In SI the indexes of force sensitivity, calculated from the slopes of the regression lines, covered a wider range than in MI; and their distribution was bimodal, with one mean of 30 Hz/N and the other of 155 Hz/N. In contrast, the mean rate-force slope in MI was 69 Hz/N.(ABSTRACT TRUNCATED AT 400 WORDS)


2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Aishwarya Parthasarathy ◽  
Cheng Tang ◽  
Roger Herikstad ◽  
Loong Fah Cheong ◽  
Shih-Cheng Yen ◽  
...  

Abstract Maintenance of working memory is thought to involve the activity of prefrontal neuronal populations with strong recurrent connections. However, it was recently shown that distractors evoke a morphing of the prefrontal population code, even when memories are maintained throughout the delay. How can a morphing code maintain time-invariant memory information? We hypothesized that dynamic prefrontal activity contains time-invariant memory information within a subspace of neural activity. Using an optimization algorithm, we found a low-dimensional subspace that contains time-invariant memory information. This information was reduced in trials where the animals made errors in the task, and was also found in periods of the trial not used to find the subspace. A bump attractor model replicated these properties, and provided predictions that were confirmed in the neural data. Our results suggest that the high-dimensional responses of prefrontal cortex contain subspaces where different types of information can be simultaneously encoded with minimal interference.


2007 ◽  
Vol 70 (10-12) ◽  
pp. 1902-1906 ◽  
Author(s):  
Margarita Zachariou ◽  
Dilshani W.N. Dissanayake ◽  
Markus R. Owen ◽  
Rob Mason ◽  
Stephen Coombes

2001 ◽  
Vol 1 (02) ◽  
pp. 135 ◽  
Author(s):  
Gregory D. Smith ◽  
Charles L. Cox ◽  
Murray S. Sherman ◽  
John Rinzel

2021 ◽  
Vol 15 ◽  
Author(s):  
Kai Yang ◽  
Xinyue Zhao ◽  
Changcai Wang ◽  
Cheng Zeng ◽  
Yan Luo ◽  
...  

L-DOPA is the criterion standard of treatment for Parkinson disease. Although it alleviates some of the Parkinsonian symptoms, long-term treatment induces L-DOPA–induced dyskinesia (LID). Several theoretical models including the firing rate model, the firing pattern model, and the ensemble model are proposed to explain the mechanisms of LID. The “firing rate model” proposes that decreasing the mean firing rates of the output nuclei of basal ganglia (BG) including the globus pallidus internal segment and substantia nigra reticulata, along the BG pathways, induces dyskinesia. The “firing pattern model” claimed that abnormal firing pattern of a single unit activity and local field potentials may disturb the information processing in the BG, resulting in dyskinesia. The “ensemble model” described that dyskinesia symptoms might represent a distributed impairment involving many brain regions, but the number of activated neurons in the striatum correlated most strongly with dyskinesia severity. Extensive evidence for circuit mechanisms in driving LID symptoms has also been presented. LID is a multisystem disease that affects wide areas of the brain. Brain regions including the striatum, the pallidal–subthalamic network, the motor cortex, the thalamus, and the cerebellum are all involved in the pathophysiology of LID. In addition, although both amantadine and deep brain stimulation help reduce LID, these approaches have complications that limit their wide use, and a novel antidyskinetic drug is strongly needed; these require us to understand the circuit mechanism of LID more deeply.


2020 ◽  
Author(s):  
Manasij Venkatesh ◽  
Joseph JaJa ◽  
Luiz Pessoa

AbstractInsights from functional Magnetic Resonance Imaging (fMRI), and more recently from recordings of large numbers of neurons through calcium imaging, reveal that many cognitive, emotional, and motor functions depend on the multivariate interactions of neuronal populations. To capture and characterize spatiotemporal properties of brain events, we propose an architecture based on long short-term memory (LSTM) networks to uncover distributed spatiotemporal signatures during dynamic experimental conditions1. We demonstrate the potential of the approach using naturalistic movie-watching fMRI data. We show that movie clips result in complex but distinct spatiotemporal patterns in brain data that can be classified using LSTMs (≈ 90% for 15-way classification), demonstrating that learned representations generalized to unseen participants. LSTMs were also superior to existing methods in predicting behavior and personality traits of individuals. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational properties of brain dynamics. Finally, we employed saliency maps to characterize spatiotemporally-varying brain-region importance. The spatiotemporal saliency maps revealed dynamic but consistent changes in fMRI activation data. We believe our approach provides a powerful framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions.


2012 ◽  
Vol 24 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Kenneth D. Miller ◽  
Francesco Fumarola

We demonstrate the mathematical equivalence of two commonly used forms of firing rate model equations for neural networks. In addition, we show that what is commonly interpreted as the firing rate in one form of model may be better interpreted as a low-pass-filtered firing rate, and we point out a conductance-based firing rate model.


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