Biologically Modeled Noise Stabilizing Neurodynamics for Pattern Recognition

1998 ◽  
Vol 08 (02) ◽  
pp. 321-345 ◽  
Author(s):  
Hung-Jen Chang ◽  
Walter J. Freeman ◽  
Brian C. Burke
Keyword(s):  
Gaussian Noise ◽  
Olfactory System ◽  
Additive Noise ◽  
Neural System ◽  
External Input ◽  
State Transitions ◽  
Anterior Olfactory Nucleus ◽  
Significant Stimulus ◽  
Unpredictable Environments ◽  
Hybrid Dynamics

We present a distributed KIII model for the olfactory neural system. Low-level Gaussian noise is introduced to the receptors and anterior olfactory nucleus, which biologically models the peripheral and central sources of noise. The additive noise numerically makes the model stable and robust in respect to repeated input-induced state transitions, while improving the simulations of EEG potentials and multiunit activity from the olfactory system. This hybrid dynamics generates a 1/f aperiodic state, which provides an unpatterned basal state for every module to stay in while there is no significant stimulus. Any external input may guide the system to a certain patterned state. The mechanism is fast, fully parallel, under modulatory control, and flexible in absorbing new patterns from unpredictable environments.

scholarly journals Stochastic resonance in FHN neural system driven by non-Gaussian noise

2012 ◽  
Vol 61 (13) ◽  
pp. 130502
Author(s):  
Zhang Jing-Jing ◽  
Jin Yan-Fei
Keyword(s):  
Stochastic Resonance ◽  
Gaussian Noise ◽  
Neural System ◽  
Non Gaussian

Stochastic Kinetics for a FitzHugh–Nagumo Neural System with Time Delay Driven by Non-Gaussian Noise and a Multiplicative Periodic Signal

2019 ◽  
Vol 18 (04) ◽  
pp. 1950027 ◽  
Author(s):  
Kang-Kang Wang ◽  
De-Cai Zong ◽  
Hui Ye ◽  
Ya-Jun Wang
Keyword(s):  
Time Delay ◽  
Probability Density ◽  
Gaussian Noise ◽  
Correlation Time ◽  
Neural System ◽  
Noise Correlation ◽  
Stable States ◽  
The Stability ◽  
Non Gaussian ◽  
System With Time Delay

In the present paper, the stability and the phenomena of stochastic resonance (SR) for a FitzHugh–Nagumo (FHN) system with time delay driven by a multiplicative non-Gaussian noise and an additive Gaussian white noise are investigated. By using the fast descent method, unified colored noise approximation and the two-state theory for the SR, the expressions for the stationary probability density function (SPDF) and the signal-to-noise ratio (SNR) are obtained. The research results show that the two noise intensities and time delay can always decrease the probability density at the two stable states and impair the stability of the neural system; while the noise correlation time [Formula: see text] can increase the probability density around both stable states and consolidate the stability of the neural system. Furthermore, the other noise correlation time [Formula: see text] can increase the probability at the resting state, but reduce that around the excited state. With respect to the SNR, it is discovered that the two noise strengths can both weaken the SR effect, while time delay [Formula: see text] and the departure parameter [Formula: see text] will always amplify the SR phenomenon. Moreover, the noise correlation time [Formula: see text] can motivate the SR effect, but not alter the peak value of the SNR. What’s most interesting is that the other noise correlation time [Formula: see text] can not only stimulate the SR phenomenon, but also results in the occurrence of two resonant peaks, whose heights are simultaneously improved because of the action of [Formula: see text].

Lyapunov Theory-Based Fusion Neural Networks for the Identification of Dynamic Nonlinear Systems

2019 ◽  
Vol 29 (09) ◽  
pp. 1950015 ◽  
Author(s):  
Spyridon Plakias ◽  
Yiannis S. Boutalis
Keyword(s):  
Nonlinear Systems ◽  
Additive Noise ◽  
Learning Algorithm ◽  
Neural System ◽  
Lyapunov Theory ◽  
Identification Process ◽  
Neural Architecture ◽  
Parameter Variations ◽  
Error System ◽  
Task Simulation

This paper introduces a novel fusion neural architecture and the use of a novel Lyapunov theory-based algorithm, for the online approximation of the dynamics of nonlinear systems. The proposed neural system, in combination with the proposed update rule of the neural weights, achieves fast convergence of the identification process, ensuring at the same time stability of the error system in the sense of Lyapunov theory. The fusion neural system combines the features that are extracted from two-independent neural streams, a feedforward and a diagonal recurrent one, satisfying different design criteria of the identification task. Simulation results for five cases reveal the approximation strength of both proposed fusion neural architecture and proposed learning algorithm. Also, additional experiments demonstrate the effectiveness in cases of parameter variations and additive noise.

scholarly journals Recurrent Infomax Generates Cell Assemblies, Neuronal Avalanches, and Simple Cell-Like Selectivity

2009 ◽  
Vol 21 (4) ◽  
pp. 1038-1067 ◽  
Author(s):  
Takuma Tanaka ◽  
Takeshi Kaneko ◽  
Toshio Aoyagi
Keyword(s):  
Learning Algorithm ◽  
Recurrent Network ◽  
External Input ◽  
Point Of View ◽  
State Transitions ◽  
Natural Scenes ◽  
Wide Range ◽  
Theoretic Point

Recently multineuronal recording has allowed us to observe patterned firings, synchronization, oscillation, and global state transitions in the recurrent networks of central nervous systems. We propose a learning algorithm based on the process of information maximization in a recurrent network, which we call recurrent infomax (RI). RI maximizes information retention and thereby minimizes information loss through time in a network. We find that feeding in external inputs consisting of information obtained from photographs of natural scenes into an RI-based model of a recurrent network results in the appearance of Gabor-like selectivity quite similar to that existing in simple cells of the primary visual cortex. We find that without external input, this network exhibits cell assembly–like and synfire chain–like spontaneous activity as well as a critical neuronal avalanche. In addition, we find that RI embeds externally input temporal firing patterns to the network so that it spontaneously reproduces these patterns after learning. RI provides a simple framework to explain a wide range of phenomena observed in in vivo and in vitro neuronal networks, and it will provide a novel understanding of experimental results for multineuronal activity and plasticity from an information-theoretic point of view.

scholarly journals A Quartet Neural System Model Orchestrating Sleep and Wakefulness Mechanisms

2006 ◽  
Vol 95 (4) ◽  
pp. 2055-2069 ◽  
Author(s):  
Yuichi Tamakawa ◽  
Akihiro Karashima ◽  
Yoshimasa Koyama ◽  
Norihiro Katayama ◽  
Mitsuyuki Nakao
Keyword(s):  
Brain Stem ◽  
Eye Movement ◽  
Rem Sleep ◽  
Nrem Sleep ◽  
Neural System ◽  
State Transitions ◽  
Model Parameters ◽  
Hypothalamic Neurons ◽  
Quantitative Understanding ◽  
Group 2

Physiological knowledge of the neural mechanisms regulating sleep and wakefulness has been advanced by the recent findings concerning sleep/wakefulness-related preoptic/anterior hypothalamic and perifornical (orexin-containing)/posterior hypothalamic neurons. In this paper, we propose a mathematical model of the mechanisms orchestrating a quartet neural system of sleep and wakefulness composed of the following: 1) sleep-active preoptic/anterior hypothalamic neurons (N-R group); 2) wake-active hypothalamic and brain stem neurons exhibiting the highest rate of discharge during wakefulness and the lowest rate of discharge during paradoxical or rapid eye movement (REM) sleep (WA group); 3) brain stem neurons exhibiting the highest rate of discharge during REM sleep (REM group); and 4) basal forebrain, hypothalamic, and brain stem neurons exhibiting a higher rate of discharge during both wakefulness and REM sleep than during nonrapid eye movement (NREM) sleep (W-R group). The WA neurons have mutual inhibitory couplings with the REM and N-R neurons. The W-R neurons have mutual excitatory couplings with the WA and REM neurons. The REM neurons receive unidirectional inhibition from the N-R neurons. In addition, the N-R neurons are activated by two types of sleep-promoting substances (SPS), which play different roles in the homeostatic regulation of sleep and wakefulness. The model well reproduces the actual sleep and wakefulness patterns of rats in addition to the sleep-related neuronal activities across state transitions. In addition, human sleep-wakefulness rhythms can be simulated by manipulating only a few model parameters: inhibitions from the N-R neurons to the REM and WA neurons are enhanced, and circadian regulation of the N-R and WA neurons is exaggerated. Our model could provide a novel framework for the quantitative understanding of the mechanisms regulating sleep and wakefulness.

scholarly journals Optogenetic Manipulation of Olfactory Responses in Transgenic Zebrafish: A Neurobiological and Behavioral Study

2021 ◽  
Vol 22 (13) ◽  
pp. 7191
Author(s):  
Yun-Mi Jeong ◽  
Tae-Ik Choi ◽  
Kyu-Seok Hwang ◽  
Jeong-Soo Lee ◽  
Robert Gerlai ◽  
...  
Keyword(s):  
Olfactory System ◽  
Neural Circuits ◽  
Transgenic Fish ◽  
Neural System ◽  
Transgenic Zebrafish ◽  
Olfactory Responses ◽  
Non Invasive ◽  
Zebrafish Larvae ◽  
Calcium Indicator

Olfaction is an important neural system for survival and fundamental behaviors such as predator avoidance, food finding, memory formation, reproduction, and social communication. However, the neural circuits and pathways associated with the olfactory system in various behaviors are not fully understood. Recent advances in optogenetics, high-resolution in vivo imaging, and reconstructions of neuronal circuits have created new opportunities to understand such neural circuits. Here, we generated a transgenic zebrafish to manipulate olfactory signal optically, expressing the Channelrhodopsin (ChR2) under the control of the olfactory specific promoter, omp. We observed light-induced neuronal activity of olfactory system in the transgenic fish by examining c-fos expression, and a calcium indicator suggesting that blue light stimulation caused activation of olfactory neurons in a non-invasive manner. To examine whether the photo-activation of olfactory sensory neurons affect behavior of zebrafish larvae, we devised a behavioral choice paradigm and tested how zebrafish larvae choose between two conflicting sensory cues, an aversive odor or the naturally preferred phototaxis. We found that when the conflicting cues (the preferred light and aversive odor) were presented together simultaneously, zebrafish larvae swam away from the aversive odor. However, the transgenic fish with photo-activation were insensitive to the aversive odor and exhibited olfactory desensitization upon optical stimulation of ChR2. These results show that an aversive olfactory stimulus can override phototaxis, and that olfaction is important in decision making in zebrafish. This new transgenic model will be useful for the analysis of olfaction related behaviors and for the dissection of underlying neural circuits.

CONSTRUCTIVE ACTION OF ADDITIVE NOISE IN OPTIMAL DETECTION

2005 ◽  
Vol 15 (09) ◽  
pp. 2985-2994 ◽  
Author(s):  
FRANÇOIS CHAPEAU-BLONDEAU ◽  
DAVID ROUSSEAU
Keyword(s):  
Information Processing ◽  
White Noise ◽  
Stochastic Resonance ◽  
Gaussian Noise ◽  
Additive Noise ◽  
Optimal Detection ◽  
Processing Conditions ◽  
Noise Optimization ◽  
Additive White Noise ◽  
Non Gaussian

The optimal detection of a signal of known form hidden in additive white noise is examined in the framework of stochastic resonance and noise-aided information processing. Conditions are exhibited where the performance in the optimal detection increases when the level of the additive (non-Gaussian bimodal) noise is raised. On the additive signal–noise mixture, when a threshold quantization is performed prior to the optimal detection, another form of improvement by noise can be obtained, with subthreshold signals and Gaussian noise. Optimization of the quantization threshold shows that even in symmetric detection settings, the optimal threshold can be away from the center of symmetry and in subthreshold configuration of the signals. These properties concerning non-Gaussian noise and nonlinear preprocessing in optimal detection, are meaningful to the current exploration of the various modalities and potentialities of stochastic resonance.

BURSTING NEAR BAUTIN BIFURCATION IN A NEURAL NETWORK WITH DELAY COUPLING

2009 ◽  
Vol 19 (05) ◽  
pp. 359-373 ◽  
Author(s):  
ZIGEN SONG ◽  
JIAN XU
Keyword(s):  
Neural Network ◽  
Hopf Bifurcation ◽  
Limit Cycle ◽  
Neural System ◽  
External Input ◽  
Quiescent State ◽  
Signal Encoding ◽  
Bautin Bifurcation ◽  
Bursting Phenomena ◽  
Limit Cycle Bifurcation

Bursting behavior is one of the most important firing activities of neural system and plays an important role in signal encoding and transmission. In the present paper, a neural network with delay coupling is modeled to investigate the generation mechanism of bursting behavior. The Andronov-Hopf bifurcation is firstly studied and then the degenerated Andronov-Hopf bifurcation, namely Bautin bifurcation, is analyzed with the external input varying. Classifying dynamics in the neighborhood of the Bautin bifurcation, we obtain the bifurcation sets where the supercritical/subcritical Andronov-Hopf, or the fold limit cycle bifurcation may happen in the system under consideration. For a periodic disturbance occurring in the neighborhood of the Bautin bifurcation, it is seen that the Andronov-Hopf bifurcation and fold limit cycle bifurcation may lead to the transition from quiescent state to firing activities. Complex bursting phenomena, including Hopf/Hopf bursting, Hopf/Fold cycle bursting, SubHopf/Hopf bursting and SubHopf/Fold cycle bursting are found in the firing area. The results show that the dynamical properties of different burstings are related to the dynamical behaviors derived from the bifurcations of the system. Finally, it is seen that the bursting disappears but the periodic spiking appears in the delayed neural network for large values of delay.

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