scholarly journals Impacts of autapse on chaotic resonance in single neurons and small-world neuronal networks

2021 ◽  
Vol 379 (2198) ◽  
Author(s):  
Veli Baysal ◽  
Erdem Erkan ◽  
Ergin Yilmaz
Keyword(s):  
Neuronal Networks ◽  
Single Neuron ◽  
Small World ◽  
Chaotic Signal ◽  
Pacemaker Activity ◽  
Chemical Synapse ◽  
Single Neurons ◽  
Theme Issue ◽  
Pacemaker Neurons ◽  
Neuronal Systems

Chaotic resonance (CR) is a new phenomenon induced by an intermediate level of chaotic signal intensity in neuronal systems. In the current study, we investigated the effects of autapse on the CR phenomenon in single neurons and small-world (SW) neuronal networks. In single neurons, we assume that the neuron has only one autapse modelled as electrical, excitatory chemical and inhibitory chemical synapse, respectively. Then, we analysed the effects of each one on the CR, separately. Obtained results revealed that, regardless of its type, autapse significantly increases the chaotic resonance of the appropriate autaptic parameter’s values. It is also observed that, at the optimal chaotic current intensity, the multiple CR emerges depending on autaptic time delay for all the autapse types when the autaptic delay time or its integer multiples match the half period or period of the weak signal. In SW networks, we investigated the effects of chaotic activity on the prorogation of pacemaker activity, where pacemaker neurons have different kinds of autapse as considered in single neuron cases. Obtained results revealed that excitatory and electrical autapses prominently increase the prorogation of pacemaker activity, whereas inhibitory autapse reduces or does not change it. Also, the best propagation was obtained when the autapse was excitatory. This article is part of the theme issue ‘Vibrational and stochastic resonance in driven nonlinear systems (part 2)’.

scholarly journals HOW CRUCIAL IS SMALL WORLD CONNECTIVITY FOR DYNAMICS?

2006 ◽  
Vol 16 (09) ◽  
pp. 2767-2775 ◽  
Author(s):  
PRASHANT M. GADE ◽  
SUDESHNA SINHA
Keyword(s):  
Spectral Properties ◽  
Chaotic Systems ◽  
Signal To Noise Ratio ◽  
Dynamical Behavior ◽  
Small World ◽  
High Strength ◽  
Nonlocal Coupling ◽  
Small Noise ◽  
Optimal Value ◽  
Neuronal Systems

We study the dynamical behavior of the collective field of chaotic systems on small world lattices. Coupled neuronal systems as well as coupled logistic maps are investigated. We observe that significant changes in dynamical properties occur only at a reasonably high strength of nonlocal coupling. Further, spectral features, such as signal-to-noise ratio (SNR), change monotonically with respect to the fraction of random rewiring, i.e. there is no optimal value of the rewiring fraction for which spectral properties are most pronounced. We also observe that for small rewiring, results are similar to those obtained by adding small noise.

Effects of distance-dependent delay on small-world neuronal networks

2016 ◽  
Vol 93 (4) ◽  
Author(s):  
Jinjie Zhu ◽  
Zhen Chen ◽  
Xianbin Liu
Keyword(s):  
Neuronal Networks ◽  
Small World

Adaptive stochastic resonance in self-organized small-world neuronal networks with time delay

2015 ◽  
Vol 29 (1-3) ◽  
pp. 346-358 ◽  
Author(s):  
Haitao Yu ◽  
Xinmeng Guo ◽  
Jiang Wang ◽  
Chen Liu ◽  
Bin Deng ◽  
...  
Keyword(s):  
Time Delay ◽  
Stochastic Resonance ◽  
Neuronal Networks ◽  
Small World ◽  
Self Organized

Information coding in the rodent prefrontal cortex. II. Ensemble activity in orbitofrontal cortex

1995 ◽  
Vol 74 (2) ◽  
pp. 751-762 ◽  
Author(s):  
G. Schoenbaum ◽  
H. Eichenbaum
Keyword(s):  
Orbitofrontal Cortex ◽  
Single Neuron ◽  
Activity Space ◽  
Behavioral Performance ◽  
Single Neurons ◽  
Reward Contingency ◽  
Neuron Firing ◽  
Firing Patterns ◽  
Neural Ensembles ◽  
Odor Discrimination Task

1. Neural activity was recorded from the orbitofrontal cortex (OF) of rats performing an eight-odor discrimination task that included predictable associations between particular odor pairs. A modified linear discriminant analysis was employed to characterize the population response in each trial of the task as a point in an N-dimensional activity space with the firing rate of each cell in the population represented on one of the N dimensions. The ability of the ensemble to discriminate among conditions of a variable was reflected in the tendency of population responses to cluster together in this activity space for repetitions of a given condition. We assessed coding of several variables describing the period of odor sampling, focusing on aspects of current, past, and future events reflected in single-neuron firing patterns, in ensembles composed of 22-138 cells active during the period when the rats sampled the discriminative stimulus in each trial. 2. OF ensembles performed well at discriminating variables with relevance to task demands represented in single-neuron firing patterns, specifically the physical attributes and assigned reward contingency of the current odor as well as the expectation of reward in the following trial that could be inferred from the predictable associations between particular pairs of odors. OF ensembles were able to correctly identify the identity and assigned reward contingency of the current odor in up to 52% (chance = 12.5%) and 99% (chance = 50%) of all trials, respectively, such that the observed behavioral performance required a population of 5,364 odor-responsive cells in the case of odor identity and only 40 cells in the case of valence. Expectations regarding upcoming rewards based on both assigned response contingency and associations between particular pairs of odors were correctly classified in up to 67% (chance = 20%) of all trials such that the observed level of behavioral performance required a population of 3,169 cells. 3. Other information represented in the single-neuron firing patterns, such as the identity and reward contingency of the preceding odor and specific odor-odor associations, was poorly encoded by OF ensembles. Thus neural ensembles in OF may represent only some of the information reflected in single-neuron activity. Stable coding of only the most useful and relevant information by the ensemble might emerge from the tuning properties of single neurons under the influence of the task at hand, producing in the well-trained animal the observed pattern of broad and diverse coding by single neurons and selective, task-relevant coding by neural ensembles in OF.

Spatiotemporal Behavior of Small-World Neuronal Networks Using a Map-Based Model

2016 ◽  
Vol 45 (2) ◽  
pp. 689-701 ◽  
Author(s):  
Jingyi Qu ◽  
Rubin Wang ◽  
Chuankui Yan ◽  
Ying Du
Keyword(s):  
Neuronal Networks ◽  
Small World ◽  
Spatiotemporal Behavior

Alternate Task Inhibits Single-neuron Category-selective Responses in the Human Hippocampus while Preserving Selectivity in the Amygdala

2009 ◽  
Vol 21 (2) ◽  
pp. 347-358 ◽  
Author(s):  
Peter N. Steinmetz
Keyword(s):  
Temporal Lobe ◽  
Video Game ◽  
Medial Temporal Lobe ◽  
Hippocampal Neurons ◽  
Single Neuron ◽  
Single Neurons ◽  
Human Epilepsy ◽  
Human Hippocampus ◽  
The Subject

One fifth of neurons in the medial-temporal lobe of human epilepsy patients respond selectively to categories of images, such as faces or cars. Here we show that responses of hippocampal neurons are rapidly modified as subjects alternate (over 60 sec) between two tasks (1) identifying images from a category, or (2) playing a simple video game superimposed on the same images. Category-selective responses, present when a subject identifies categories, are eliminated when the subject shifts to playing the game for 87% of category-selective hippocampal neurons. By contrast, responses in the amygdala are present during both tasks for 72% of category-selective amygdalar neurons. These results suggest that attention to images is required to evoke selective responses from single neurons in the hippocampus, but is not required by neurons in the amygdala.

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