scholarly journals Multi-frequency activation of neuronal networks by coordinated reset stimulation

2010 ◽  
Vol 1 (1) ◽  
pp. 75-85 ◽  
Author(s):  
Borys Lysyansky ◽  
Oleksandr V. Popovych ◽  
Peter A. Tass
Keyword(s):  
Firing Rate ◽  
Neuronal Networks ◽  
Epileptic Seizures ◽  
Neuronal Population ◽  
Arnold Tongues ◽  
Clustering Pattern ◽  
Coordinated Reset

We computationally study whether it is possible to stimulate a neuronal population in such a way that its mean firing rate increases without an increase of the population's net synchronization. For this, we use coordinated reset (CR) stimulation, which has previously been developed to desynchronize populations of oscillatory neurons. Intriguingly, delivered to a population of predominantly silent FitzHugh–Nagumo or Hindmarsh–Rose neurons at sufficient stimulation amplitudes, CR robustly causes a multi-frequency activation: different Arnold tongues such as 1 : 1 or n  :  m entrained neuronal clusters emerge, which consist of phase-shifted sub clusters. Owing to the clustering pattern the neurons' timing is well balanced, so that in total there is no synchronization. Our findings may contribute to the development of novel and safe stimulation treatments that specifically counteract cerebral hypo-activity without promoting pathological synchronization or inducing epileptic seizures.

scholarly journals A deep convolutional visual encoding model of neuronal responses in the LGN

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eslam Mounier ◽  
Bassem Abdullah ◽  
Hani Mahdi ◽  
Seif Eldawlatly
Keyword(s):  
Firing Rate ◽  
Visual Information ◽  
Visual Stimulation ◽  
Neuronal Population ◽  
Neuronal Firing ◽  
Electrode Arrays ◽  
Deep Convolutional Neural Networks ◽  
Firing Rates ◽  
Spatiotemporal Representation

AbstractThe Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

scholarly journals High-Frequency Synchronization Improves Firing Rate Contrast and Information Transmission Efficiency in E/I Neuronal Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Fang Han ◽  
Zhijie Wang ◽  
Hong Fan ◽  
Yaopeng Zhang
Keyword(s):  
Information Transmission ◽  
Firing Rate ◽  
Contrast Enhancement ◽  
High Frequency ◽  
Functional Role ◽  
Neuronal Networks ◽  
Time Window ◽  
Frequency Synchronization ◽  
Neuronal System ◽  
Information Encoding

High-frequency synchronization has been found in many real neural systems and is confirmed by excitatory/inhibitory (E/I) network models. However, the functional role played by it remains elusive. In this paper, it is found that high-frequency synchronization in E/I neuronal networks could improve the firing rate contrast of the whole network, no matter if the network is fully connected or randomly connected, with noise or without noise. It is also found that the global firing rate contrast enhancement can prevent the number of spikes of the neurons measured within the limited time window from being confused by noise, thereby enhancing the information encoding efficiency (quantified by entropy theory here) of the neuronal system. The mechanism of firing rate contrast enhancement is also investigated. Our work implies a possible functional role in information transmission of high-frequency synchronization in neuronal systems.

scholarly journals Intrinsic Dynamics in Neuronal Networks. I. Theory

2000 ◽  
Vol 83 (2) ◽  
pp. 808-827 ◽  
Author(s):  
P. E. Latham ◽  
B. J. Richmond ◽  
P. G. Nelson ◽  
S. Nirenberg
Keyword(s):  
Firing Rate ◽  
Firing Pattern ◽  
Neuronal Networks ◽  
Network Connectivity ◽  
Excitatory Input ◽  
High Rate ◽  
Inhibitory Neurons ◽  
Inhibitory Input ◽  
Dynamic Interactions ◽  
Firing Patterns

Many networks in the mammalian nervous system remain active in the absence of stimuli. This activity falls into two main patterns: steady firing at low rates and rhythmic bursting. How are these firing patterns generated? Specifically, how do dynamic interactions between excitatory and inhibitory neurons produce these firing patterns, and how do networks switch from one firing pattern to the other? We investigated these questions theoretically by examining the intrinsic dynamics of large networks of neurons. Using both a semianalytic model based on mean firing rate dynamics and simulations with large neuronal networks, we found that the dynamics, and thus the firing patterns, are controlled largely by one parameter, the fraction of endogenously active cells. When no endogenously active cells are present, networks are either silent or fire at a high rate; as the number of endogenously active cells increases, there is a transition to bursting; and, with a further increase, there is a second transition to steady firing at a low rate. A secondary role is played by network connectivity, which determines whether activity occurs at a constant mean firing rate or oscillates around that mean. These conclusions require only conventional assumptions: excitatory input to a neuron increases its firing rate, inhibitory input decreases it, and neurons exhibit spike-frequency adaptation. These conclusions also lead to two experimentally testable predictions: 1) isolated networks that fire at low rates must contain endogenously active cells and 2) a reduction in the fraction of endogenously active cells in such networks must lead to bursting.

Global firing rate contrast enhancement in E/I neuronal networks by recurrent synchronized inhibition

2018 ◽  
Vol 28 (10) ◽  
pp. 106324 ◽  
Author(s):  
Fang Han ◽  
Xiaochun Gu ◽  
Zhijie Wang ◽  
Hong Fan ◽  
Jinfeng Cao ◽  
...  
Keyword(s):  
Firing Rate ◽  
Contrast Enhancement ◽  
Neuronal Networks

Discharge properties of pontine reticulospinal neurons during sleep-waking cycle

1978 ◽  
Vol 41 (3) ◽  
pp. 821-834 ◽  
Author(s):  
P. W. Wyzinski ◽  
R. W. McCarley ◽  
J. A. Hobson
Keyword(s):  
Spinal Cord ◽  
Firing Rate ◽  
Neuronal Population ◽  
Spinal Reflex ◽  
Reticular Nucleus ◽  
Medullary Reticular Formation ◽  
Reticulospinal Neurons ◽  
Reflex Pathways ◽  
Naturally Occurring ◽  
Generator Function

1. Reticulospinal neurons were identified by antidromic invasion from spinal cord electrodes chronically implanted at C4 in cats. 2. Most of the neuronal population studied lay within the medial portion of the giant cell field from the anterior pontine and to the anterior medullary reticular formation (FTG). A few cells were found in the tegmental reticular nucleus (TRC) which has not previously been known to project to the spinal cord. 3. Extracellular action potentials from the neuronal somata of the identified neurons were recorded continuously throughout naturally occurring sleep-waking cycles. 4. The identified reticulospinal neurons shared three properties, suggesting a generator function in desynchronized sleep (D) (with previously recorded but unidentified FTG neurons): selectivity (or concentration of discharge in D); tonic latency (or firing rate increases beginning several minutes prior to D); and phasic latency (or firing rate increases occurring prior to eye movements within D). 5. The location, discharge properties, and spinal projections of FTG neurons are, thus, all consistent with the hypothesis that they may directly mediate some of the descending excitatory and inhibitory influences on spinal reflex pathways in desynchronized sleep.

scholarly journals The Role of Chemical Factors in the Occurrence of the Nervous System Disorders Posttraumatic epilepsy

2019 ◽  
Vol 70 (1) ◽  
pp. 228-232
Author(s):  
Gheorghe Raftu ◽  
Mihaela Debita ◽  
Ciprian Dinu ◽  
Alexandra Pangal ◽  
Diana Hodorog ◽  
...  
Keyword(s):  
Epileptic Seizure ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Neuronal Population ◽  
Time Interval ◽  
Posttraumatic Epilepsy ◽  
Additional Information ◽  
Chemical Factors ◽  
Post Traumatic ◽  
Post Traumatic Epilepsy

Because an epilepsy occurring at a time interval after a cranio-cerebral trauma can be considered post-traumatic epilepsy, so in a causal relationship with the trauma, it must meet some conditions. Epilepsy is a chronic cerebral disorder manifested by recurrent, spontaneous epileptic seizures; with a sudden episode, a stereotype of motor, sensitive, sensory, behavioural manifestation, and / or alteration of the state of consciousness due to sudden, unprovoked activation of a neuronal population. To support the diagnosis of the first epileptic seizure, additional information is required from the anamnesis, the general and neurological clinical examination of the patient. The diagnostic approach continues, under the direct supervision of the neurologist, by identifying the aetiology of epilepsy. The study includes 27 epilepsy cases studied in 2014-2018. The most common type of epileptic seizure is the tonic-clonic one that affects the entire brain, and this is also the most visible form of epilepsy that manifests itself in the form of a generalized crisis, identified by involuntary convulsions that lead to partial or total loss of consciousness during the crisis. Epilepsy is caused by an explosion of intense electrical activity that suddenly occurs in the brain, and the resulting convulsions can occur in several forms depending on the area where this intense brain activity occurs.

Transition Dynamics of Epileptic Seizures in the Coupled Thalamocortical Network Model

2018 ◽  
Vol 28 (08) ◽  
pp. 1850104
Author(s):  
Ying Cao ◽  
Xiaoyan He ◽  
Yuqing Hao ◽  
Qingyun Wang
Keyword(s):  
Chaotic Dynamics ◽  
Epileptic Seizures ◽  
Neuronal Population ◽  
State Transitions ◽  
Transition Dynamics ◽  
Model Network ◽  
Single Compartment ◽  
Evolution Dynamics ◽  
Inhibitory Coupling ◽  
Coupling Strengths

In this paper, based on the two-compartment unidirectionally coupled thalamocortical model network, we investigated the transition dynamics of epileptic seizures, by considering the inhibitory coupling strength from cortical inhibitory interneuronal (IN) population to excitatory pyramidal (PY) neuronal population as the key bifurcation parameter. The results show that in the single compartment thalamocortical model, inner-compartment inhibitory functions of IN can make the system transit from the absence seizures to the tonic oscillations. In the case of two-compartment coupled thalamocortical model network, the inter-compartment inhibitory coupling functions from the first compartment can drive the second compartment to more easily initiate the absence and tonic seizures at the lower inhibitory coupling strengths, respectively. Also, the driven functions can make the amplitudes of these seizures vary irregularly. Detailed investigations reveal that along with the various state transitions, the system consecutively undergoes Hopf bifurcations, fold of cycles bifurcations and torus bifurcations, respectively. In particular, the reinforcing inter-compartment inhibitory coupling function can induce the chaotic dynamics. We highlight the unidirectional coupling functions between two compartments which might give new insights into the propagation and evolution dynamics of epileptic seizures.

Response of Anterior Parietal Cortex to Cutaneous Flutter Versus Vibration

1999 ◽  
Vol 82 (1) ◽  
pp. 16-33 ◽  
Author(s):  
M. Tommerdahl ◽  
K. A. Delemos ◽  
B. L. Whitsel ◽  
O. V. Favorov ◽  
C. B. Metz
Keyword(s):  
Firing Rate ◽  
Population Level ◽  
Early Response ◽  
Neuronal Population ◽  
Stimulus Onset ◽  
Skin Site ◽  
Intrinsic Signal ◽  
Optical Intrinsic Signal ◽  
Area 3B ◽  
Stimulation Of

The response of anesthetized squirrel monkey anterior parietal (SI) cortex to 25 or 200 Hz sinusoidal vertical skin displacement stimulation was studied using the method of optical intrinsic signal (OIS) imaging. Twenty-five-Hertz (“flutter”) stimulation of a discrete skin site on either the hindlimb or forelimb for 3–30 s evoked a prominent increase in absorbance within cytoarchitectonic areas 3b and 1 in the contralateral hemisphere. This response was confined to those area 3b/1 regions occupied by neurons with a receptive field (RF) that includes the stimulated skin site. In contrast, same-site 200-Hz stimulation (“vibration”) for 3–30 s evoked a decrease in absorbance in a much larger territory (most frequently involving areas 3b, 1, and area 3a, but in some subjects area 2 as well) than the region that undergoes an increase in absorbance during 25-Hz flutter stimulation. The increase in absorbance evoked by 25-Hz flutter developed quickly and remained relatively constant for as long as stimulation continued (stimulus duration never exceeded 30 s). At 1–3 s after stimulus onset, the response to 200-Hz stimulation, like the response to 25-Hz flutter, consisted of a localized increase in absorbance limited to the topographically appropriate region of area 3b and/or area 1. With continuing 200-Hz stimulation, however, the early response declined, and by 4–6 s after stimulus onset, it was replaced by a prominent and spatially extensive decrease in absorbance. The spike train responses of single quickly adapting (QA) neurons were recorded extracellularly during microelectrode penetrations that traverse the optically responding regions of areas 3b and 1. Onset of either 25- or 200-Hz stimulation at a site within the cutaneous RF of a QA neuron was accompanied by a substantial increase in mean spike firing rate. With continued 200-Hz stimulation, however, QA neuron mean firing rate declined rapidly (typically within 0.5–1.0 s) to a level below that recorded at the same time after onset of same-site 25-Hz stimulation. For some neurons, the mean firing rate after the initial 0.5–1 s of an exposure to 200-Hz stimulation of the RF decreased to a level below the level of background (“spontaneous”) activity. The decline in both the stimulus-evoked increases in absorbance in areas 3b/1 and spike discharge activity of area 3b/1 neurons within only a few seconds of the onset of 200-Hz skin stimulation raised the possibility that the predominant effect of continuous 200-Hz stimulation for >3 s is inhibition of area 3b/1 QA neurons. This possibility was evaluated at the neuronal population level by comparing the intrinsic signal evoked in areas 3b/1 by 25-Hz skin stimulation to the intrinsic signal evoked by a same-site skin stimulus containing both 25- and 200-Hz sinusoidal components (a “complex waveform stimulus”). Such experiments revealed that the increase in absorbance evoked in areas 3b/1 by a stimulus having both 25- and 200-Hz components was substantially smaller (especially at times >3 s after stimulus onset) than the increase in absorbance evoked by “pure” 25-Hz stimulation of the same skin site. It is concluded that within a brief time (within 1–3 s) after stimulus onset, 200-Hz skin stimulation elicits a powerful inhibitory action on area 3b/1 QA neurons. The findings appear generally consistent with the suggestion that the activity of neurons in cortical regions other than areas 3b and 1 play the leading role in the processing of high-frequency (≥200 Hz) vibrotactile stimuli.

scholarly journals Large-Scale Modeling of Epileptic Seizures: Scaling Properties of Two Parallel Neuronal Network Simulation Algorithms

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Lorenzo L. Pesce ◽  
Hyong C. Lee ◽  
Mark Hereld ◽  
Sid Visser ◽  
Rick L. Stevens ◽  
...  
Keyword(s):  
Large Scale ◽  
Neuronal Networks ◽  
Computation Time ◽  
Epileptic Seizures ◽  
Parallel Computer ◽  
Scaling Properties ◽  
Scale Modeling ◽  
Time Scaling ◽  
The Relationship ◽  
Large Scale Simulations

Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.

Sign in / Sign up

Export Citation Format

Share Document