scholarly journals A comparative analysis of a firing-rate model and a conductance-based neural population model

2007 ◽  
Vol 369 (1-2) ◽  
pp. 31-36 ◽  
Author(s):  
Anton V. Chizhov ◽  
Serafim Rodrigues ◽  
John R. Terry
Keyword(s):  
Comparative Analysis ◽  
Firing Rate ◽  
Population Model ◽  
Neural Population ◽  
Rate Model

scholarly journals Next-generation neural mass and field modeling

2020 ◽  
Vol 123 (2) ◽  
pp. 726-742 ◽  
Author(s):  
Áine Byrne ◽  
Reuben D. O’Dea ◽  
Michael Forrester ◽  
James Ross ◽  
Stephen Coombes
Keyword(s):  
Firing Rate ◽  
Population Model ◽  
Large Scale ◽  
Brain Activity ◽  
Large Population ◽  
Power Spectra ◽  
Neural Population ◽  
Next Generation ◽  
Mean Field Model ◽  
Human Connectome Project

The Wilson–Cowan population model of neural activity has greatly influenced our understanding of the mechanisms for the generation of brain rhythms and the emergence of structured brain activity. As well as the many insights that have been obtained from its mathematical analysis, it is now widely used in the computational neuroscience community for building large-scale in silico brain networks that can incorporate the increasing amount of knowledge from the Human Connectome Project. Here, we consider a neural population model in the spirit of that originally developed by Wilson and Cowan, albeit with the added advantage that it can account for the phenomena of event-related synchronization and desynchronization. This derived mean-field model provides a dynamic description for the evolution of synchrony, as measured by the Kuramoto order parameter, in a large population of quadratic integrate-and-fire model neurons. As in the original Wilson–Cowan framework, the population firing rate is at the heart of our new model; however, in a significant departure from the sigmoidal firing rate function approach, the population firing rate is now obtained as a real-valued function of the complex-valued population synchrony measure. To highlight the usefulness of this next-generation Wilson–Cowan style model, we deploy it in a number of neurobiological contexts, providing understanding of the changes in power spectra observed in electro- and magnetoencephalography neuroimaging studies of motor cortex during movement, insights into patterns of functional connectivity observed during rest and their disruption by transcranial magnetic stimulation, and to describe wave propagation across cortex.

scholarly journals Dynamics of the Auditory Continuity Illusion

2021 ◽  
Vol 15 ◽  
Author(s):  
Qianyi Cao ◽  
Noah Parks ◽  
Joshua H. Goldwyn
Keyword(s):  
Firing Rate ◽  
Noise Burst ◽  
Auditory Scene Analysis ◽  
Quantitative Model ◽  
Neural Dynamics ◽  
Sufficient Evidence ◽  
Neural Population ◽  
Neural Basis ◽  
Continuous Responses ◽  
Conceptual Explanations

Illusions give intriguing insights into perceptual and neural dynamics. In the auditory continuity illusion, two brief tones separated by a silent gap may be heard as one continuous tone if a noise burst with appropriate characteristics fills the gap. This illusion probes the conditions under which listeners link related sounds across time and maintain perceptual continuity in the face of sudden changes in sound mixtures. Conceptual explanations of this illusion have been proposed, but its neural basis is still being investigated. In this work we provide a dynamical systems framework, grounded in principles of neural dynamics, to explain the continuity illusion. We construct an idealized firing rate model of a neural population and analyze the conditions under which firing rate responses persist during the interruption between the two tones. First, we show that sustained inputs and hysteresis dynamics (a mismatch between tone levels needed to activate and inactivate the population) can produce continuous responses. Second, we show that transient inputs and bistable dynamics (coexistence of two stable firing rate levels) can also produce continuous responses. Finally, we combine these input types together to obtain neural dynamics consistent with two requirements for the continuity illusion as articulated in a well-known theory of auditory scene analysis: responses persist through the noise-filled gap if noise provides sufficient evidence that the tone continues and if there is no evidence of discontinuities between the tones and noise. By grounding these notions in a quantitative model that incorporates elements of neural circuits (recurrent excitation, and mutual inhibition, specifically), we identify plausible mechanisms for the continuity illusion. Our findings can help guide future studies of neural correlates of this illusion and inform development of more biophysically-based models of the auditory continuity illusion.

Firing-rate model of a population of adaptive neurons

BIOPHYSICS ◽  
2010 ◽  
Vol 55 (4) ◽  
pp. 592-599 ◽  
Author(s):  
A. Yu. Buchin ◽  
A. V. Chizhov
Keyword(s):  
Firing Rate ◽  
Rate Model

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.

scholarly journals The role of cannabinoids in the neurobiology of sensory gating: A firing rate model study

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

scholarly journals Optimal Control of an Epileptic Neural Population Model

2014 ◽  
Vol 47 (3) ◽  
pp. 3116-3121 ◽  
Author(s):  
Justin Ruths ◽  
Peter Neal Taylor ◽  
Justin Dauwels
Keyword(s):  
Optimal Control ◽  
Population Model ◽  
Neural Population
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