A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Stable memory with unstable synapses
2019
Nature Communications
Preserved Fulltext
We find that homeostatic stabilization of fluctuations differentially affects different aspects of network connectivity. ...
Specifically, memories stored as time-varying attractors of neural dynamics are more resilient to erosion than fixed-points. ...
In 23 the Hopfield model was studied in the presence of ongoing STDP; it was found that unstructured noise inserted into the neural state could stabilize memories with anti-symmetric, but not with symmetric ...
doi:10.1038/s41467-019-12306-2
pmid:31570719
pmcid:PMC6768856
fatcat:ataanits2zhs5khy6rwiiszm3q
A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties
2006
Biological cybernetics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2010; you can also visit the original URL.
The file type is application/pdf.
Networks of integrate-and-fire neurons behave in a wide variety of ways and have been used to model a variety of neural, physiological, and psychological phenomena. ...
Recent interest in the response of neurons to periodic input has in part arisen from the study of stochastic resonance, which is the noise-induced enhancement of the signalto-noise ratio. ...
Acknowledgments The author thanks Hamish Meffin and David Grayden for a critical reading of the manuscript and detailed comments. ...
doi:10.1007/s00422-006-0082-8
pmid:16821035
fatcat:vqn4v6b6b5dlrchg3sydv2n6mi
Beyond mean field theory: statistical field theory for neural networks
2013
Journal of Statistical Mechanics: Theory and Experiment
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Mean field theories have been a stalwart for studying the dynamics of networks of coupled neurons. They are convenient because they are relatively simple and possible to analyze. ...
However, classical mean field theory neglects the effects of fluctuations and correlations due to single neuron effects. ...
Acknowledgments This research was supported by the Intramural Research Program of NIH/NIDDK. ...
doi:10.1088/1742-5468/2013/03/p03003
pmid:25243014
pmcid:PMC4169078
fatcat:m6vgzazz7zadpl5emkql6t4qrq
Neuro-Inspired Speech Recognition Based on Reservoir Computing
[chapter]
2010
Advances in Speech Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
A detailed discussion about training of recurrent neural networks is provided by . ...
The pre processing is done in order to remove the noise from a signal and then coefficients were extracted at the frame rate of 20 ms and analysis is done by windowing the speech data with a window size ...
This book addresses a few of these applications. ...
doi:10.5772/10186
fatcat:ojsrto4ofraazaka7u76tt244i
Stable memory with unstable synapses
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Motivated by these observations, we explore the possibility of memory storage within a global component of network connectivity, while individual connections fluctuate. ...
Memory representations are stored as time-varying attractors in neural state-space and support associative retrieval of learned information. ...
In [24] the Hopfield model was studied in the presence of ongoing STDP; it was found that unstructured noise inserted into the neural state could stabilize memories with anti-symmetric, but not with ...
arXiv:1808.00756v3
fatcat:nza4zaxyprgvlbfifb5zqyvowa
Mathematical Theories and Applications for Nonlinear Control Systems
2019
Mathematical Problems in Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Wang studies adaptive synchronization for a class of uncertain delayed fractional-order Hopfield neural networks (FOHNNs) with external disturbances. ...
Zhao entitled "Modeling and Stability Analysis for Markov Jump Networked Evolutionary Games" investigates the algebraic formulation and stability analysis for a class of Markov jump networked evolutionary ...
The editors also wish to thank the anonymous reviewers for their careful reading of the manuscripts submitted to this special issue collection and their many insightful comments and suggestions. ...
doi:10.1155/2019/2065786
fatcat:qx66v5pu3fgo7ee4rt7rn6rcj4
Change-Based Inference in Attractor Nets: Linear Analysis
2010
Neural Computation
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2011; you can also visit the original URL.
The file type is application/pdf.
This way of performing computations is fast, accurate, readily learnable, and robust to various forms of noise. ...
We have recently suggested an alternative interpretation according to which computations are realized by systematic changes in the states of such networks over time. ...
Acknowledgments This work was funded by the Gatsby Charitable Foundation. We are grateful to Jeff Beck for helpful discussions and comments. ...
doi:10.1162/neco_a_00051
pmid:20858130
fatcat:w36p5km4wjdgbpmvimcvoypb3m
Change-based inference in attractor nets: Linear analysis
2009
Frontiers in Systems Neuroscience
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2011; you can also visit the original URL.
The file type is application/pdf.
This way of performing computations is fast, accurate, readily learnable, and robust to various forms of noise. ...
We have recently suggested an alternative interpretation according to which computations are realized by systematic changes in the states of such networks over time. ...
Acknowledgments This work was funded by the Gatsby Charitable Foundation. We are grateful to Jeff Beck for helpful discussions and comments. ...
doi:10.3389/conf.neuro.06.2009.03.020
fatcat:y6bkta2oxzardg34xmtlfpvqiu
Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
2002
Neural Computation
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2007; you can also visit the original URL.
The file type is application/pdf.
A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire ...
We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. ...
The work was supported by project # P15386 of the Austrian Science Fund, the NeuroCOLT project of the EU, the Office of Naval Research, HFSP, Dolfi & Ebner Center and the Edith Blum Foundation. ...
doi:10.1162/089976602760407955
pmid:12433288
fatcat:33ec7g6kojg3tdteeae3cjzumq
Sequential Activity in Asymmetrically Coupled Winner-Take-All Circuits
2014
Neural Computation
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Understanding the sequence generation and learning mechanisms used by recurrent neural networks in the nervous system is an important problem that has been studied extensively. ...
Understanding the sequence generation and learning mechanisms used by recurrent neural networks in the nervous system is an important problem that has been studied extensively. ...
Acknowledgments This work was supported by the European CHIST-ERA program, via the Plasticity in NEUral Memristive Architectures project and by the European Research Council, via the Neuromorphic Processors ...
doi:10.1162/neco_a_00619
pmid:24877737
fatcat:2y43r6pozvd2jl3uouti7bv5ce
Poisson Stability in Symmetrical Impulsive Shunting Inhibitory Cellular Neural Networks with Generalized Piecewise Constant Argument
2022
Symmetry
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Finally, comparing impulsive shunting inhibitory cellular neural networks with former neural network models, we discuss the significance of the components of our model. ...
The process is subdued to Poisson stable inputs, which cause the new type of recurrent signals. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/sym14091754
fatcat:2fw44ywd2jhx5jtzfcqp2qiq5a
Metastable dynamics of neural circuits and networks
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
a variety of neural signals, and (iii) recent neural network approaches, informed by experimental results, to model the emergence of metastable dynamics. ...
Cortical neurons emit seemingly erratic trains of action potentials, or "spikes", and neural network dynamics emerge from the coordinated spiking activity within neural circuits. ...
by the National Natural Science Foundation of China under award number NSFC 21721003 (HY). ...
arXiv:2110.03025v2
fatcat:fzzryumjjrc2royxs22uf2ylxu
A Dynamical Systems Hypothesis of Schizophrenia
2007
PLoS Computational Biology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In integrate-and-fire network simulations, a decrease in the NMDA receptor conductances, which reduces the depth of the attractor basins, decreases the stability of short-term memory states and increases ...
We show that a reduced depth in the basins of attraction of cortical attractor states destabilizes the activity at the network level due to the constant statistical fluctuations caused by the stochastic ...
GD contributed reagents/materials/analysis tools. The authors shared the research. Funding. ML was supported by the Boehringer Ingelheim Fonds. ...
doi:10.1371/journal.pcbi.0030228
pmid:17997599
pmcid:PMC2065887
fatcat:3n6ey3cfcbg45ab456dfroubpy
Boolean Network Approach to Negative Feedback Loops of the p53 Pathways: Synchronized Dynamics and Stochastic Limit Cycles
[article]
2009
arXiv
pre-print
Preserved Fulltext
The Internet Archive has a preservation copy of this work in our general collections.
The file type is application/pdf.
attracted to a closed cycle of the p53 dynamics after being perturbed by outside signal (e.g. ...
Our theoretical and numerical studies show that both the biological stationary state and the biological oscillation after being perturbed are stable for a wide range of noise level. ...
Acknowledgement The authors would like to thank Professor Minping Qian in Peking University for calling our attention to the p53 network. ...
arXiv:0904.2252v1
fatcat:2ny5zslcibbmtnnhle2ct6oe3q
Closed-Form Treatment of the Interactions between Neuronal Activity and Timing-Dependent Plasticity in Networks of Linear Neurons
2010
Frontiers in Computational Neuroscience
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Thus, here we develop for a linear differential Hebbian learning system a method by which we can analytically investigate the dynamics and stability of the connections in recurrent networks. ...
Stability in networks with STDP ...
analysIs of the network structures wIthout noIse First, we will investigate the structures, which we introduced in the beginning of this study, in the absence of noise. ...
doi:10.3389/fncom.2010.00134
pmid:21152348
pmcid:PMC2998049
fatcat:2jkn22fcpjfjbcfoujwq3vg3aa
« Previous
Showing results 1 — 15 out of 201 results