scholarly journals Recurrence-mediated suprathreshold stochastic resonance

2021 ◽  
Author(s):  
Gregory Knoll ◽  
Benjamin Lindner
Keyword(s):  
Stochastic Resonance ◽  
Coupling Strength ◽  
Signal Transmission ◽  
Intrinsic Noise ◽  
Synaptic Coupling ◽  
Wide Range ◽  
Spiking Network ◽  
Suprathreshold Stochastic Resonance ◽  
Temporally Correlated ◽  
Optimal Strength

AbstractIt has previously been shown that the encoding of time-dependent signals by feedforward networks (FFNs) of processing units exhibits suprathreshold stochastic resonance (SSR), which is an optimal signal transmission for a finite level of independent, individual stochasticity in the single units. In this study, a recurrent spiking network is simulated to demonstrate that SSR can be also caused by network noise in place of intrinsic noise. The level of autonomously generated fluctuations in the network can be controlled by the strength of synapses, and hence the coding fraction (our measure of information transmission) exhibits a maximum as a function of the synaptic coupling strength. The presence of a coding peak at an optimal coupling strength is robust over a wide range of individual, network, and signal parameters, although the optimal strength and peak magnitude depend on the parameter being varied. We also perform control experiments with an FFN illustrating that the optimized coding fraction is due to the change in noise level and not from other effects entailed when changing the coupling strength. These results also indicate that the non-white (temporally correlated) network noise in general provides an extra boost to encoding performance compared to the FFN driven by intrinsic white noise fluctuations.

scholarly journals Inter-areal balanced amplification enhances signal propagation in a large-scale circuit model of the primate cortex

2017 ◽  
Author(s):  
Madhura R. Joglekar ◽  
Jorge F. Mejias ◽  
Guangyu Robert Yang ◽  
Xiao-Jing Wang
Keyword(s):  
Large Scale ◽  
Signal Transmission ◽  
Network Models ◽  
Signal Propagation ◽  
Wide Range ◽  
Spiking Network ◽  
Conscious Processing ◽  
Effective Signal ◽  
Feedback Connections ◽  
Stable Signal

AbstractReliable signal transmission represents a fundamental challenge for cortical systems, which display a wide range of weights of feedforward and feedback connections among heterogeneous areas. We re-examine the question of signal transmission across the cortex in network models based on recently available mesoscopic, directed‐ and weighted-inter-areal connectivity data of the macaque cortex. Our findings reveal that, in contrast to feed-forward propagation models, the presence of long-range excitatory feedback projections could compromise stable signal propagation. Using population rate models as well as a spiking network model, we find that effective signal propagation can be accomplished by balanced amplification across cortical areas while ensuring dynamical stability. Moreover, the activation of prefrontal cortex in our model requires the input strength to exceed a threshold, in support of the ignition model of conscious processing, demonstrating our model as an anatomically-realistic platform for investigations of the global primate cortex dynamics.

Noise-assisted signal transmission via stochastic resonance in a diode nonlinearity

1997 ◽  
Vol 33 (20) ◽  
pp. 1666 ◽  
Author(s):  
X. Godivier ◽  
J. Rojas-Varela ◽  
F. Chapeau-Blondeau
Keyword(s):  
Stochastic Resonance ◽  
Signal Transmission

Enhancing threshold neural network via suprathreshold stochastic resonance for pattern classification

2021 ◽  
pp. 127387
Author(s):  
Xiaojie Liu ◽  
Lingling Duan ◽  
Fabing Duan ◽  
François Chapeau-Blondeau ◽  
Derek Abbott
Keyword(s):  
Neural Network ◽  
Stochastic Resonance ◽  
Pattern Classification ◽  
Suprathreshold Stochastic Resonance

scholarly journals A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Karim El-Laithy ◽  
Martin Bogdan
Keyword(s):  
Reinforcement Learning ◽  
Spike Timing ◽  
Neural Representation ◽  
Model Parameters ◽  
Learning Framework ◽  
Reference Target ◽  
Wide Range ◽  
Spiking Network ◽  
Dynamic Synapses ◽  
Exclusive Or

An integration of both the Hebbian-based and reinforcement learning (RL) rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

scholarly journals Use of peroxiredoxin for preconditioning of heterotopic heart transplantation in a rat

2020 ◽  
Vol 22 (2) ◽  
pp. 158-164
Author(s):  
N. V. Grudinin ◽  
V. K. Bogdanov ◽  
M. G. Sharapov ◽  
N. S. Bunenkov ◽  
N. P. Mozheiko ◽  
...  
Keyword(s):  
Heart Transplantation ◽  
Reperfusion Injury ◽  
Mammalian Cells ◽  
Ischemia Reperfusion Injury ◽  
Isolated Heart ◽  
Ischemia Reperfusion ◽  
Signal Transmission ◽  
Redox Homeostasis ◽  
Heterotopic Heart Transplantation ◽  
Wide Range

Peroxiredoxin 6 (Prdx6) is an antioxidant enzyme in the human body that performs a number of important functions in the cell. Prdx6 restores a wide range of peroxide substrates, thus playing a leading role in maintaining redox homeostasis in mammalian cells. In addition to peroxidase activity, Prdx6 has an activity of phospholipase A2, thus taking part in membrane phospholipid metabolism. Due to its peroxidase and phospholipase activity, Prdx6 participates in intracellular and intercellular signal transmission, thereby facilitating the initiation of regenerative processes in the cell, suppression of apoptosis and activation of cell proliferation. Given the functions performed, Prdx6 can effectively deal with oxidative stress caused by various factors, including ischemia-reperfusion injury. On an animal model of rat heterotopic heart transplantation, we showed the cardioprotective potential of exogenous recombinant Prdx6, introduced before transplantation and subsequent reperfusion injury of the heart. It has been demonstrated that exogenous Prdx6 effectively alleviates the severity of ischemia-reperfusion injury of the heart by 2–3 times, providing normalization of its structural and functional state during heterotopic transplantation. The use of recombinant Prdx6 can be an effective approach in preventing/alleviating ischemia-reperfusion injury of the heart, as well as in maintaining an isolated heart during transplantation.

scholarly journals Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance

2017 ◽  
Vol 4 (9) ◽  
pp. 160889 ◽  
Author(s):  
Liyan Xu ◽  
Fabing Duan ◽  
Xiao Gao ◽  
Derek Abbott ◽  
Mark D. McDonnell
Keyword(s):  
Stochastic Resonance ◽  
Recursive Algorithm ◽  
Signal Strength ◽  
Distinct Form ◽  
Mean Square ◽  
Least Mean Square ◽  
Complete Knowledge ◽  
The Mean ◽  
Suprathreshold Stochastic Resonance ◽  
Over Time

Suprathreshold stochastic resonance (SSR) is a distinct form of stochastic resonance, which occurs in multilevel parallel threshold arrays with no requirements on signal strength. In the generic SSR model, an optimal weighted decoding scheme shows its superiority in minimizing the mean square error (MSE). In this study, we extend the proposed optimal weighted decoding scheme to more general input characteristics by combining a Kalman filter and a least mean square (LMS) recursive algorithm, wherein the weighted coefficients can be adaptively adjusted so as to minimize the MSE without complete knowledge of input statistics. We demonstrate that the optimal weighted decoding scheme based on the Kalman–LMS recursive algorithm is able to robustly decode the outputs from the system in which SSR is observed, even for complex situations where the signal and noise vary over time.

Suprathreshold stochastic resonance: decoding

2009 ◽  
pp. 167-232
Author(s):  
Mark D. McDonnell ◽  
Nigel G. Stocks ◽  
Charles E. M. Pearce ◽  
Derek Abbott
Keyword(s):  
Stochastic Resonance ◽  
Suprathreshold Stochastic Resonance
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