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Universal Nonlinear Spiking Neural P Systems with Delays and Weights on Synapses

Liping Wang, Xiyu Liu, Yuzhen Zhao, José Alfredo Hernández-Pérez
2021 Computational Intelligence and Neuroscience  
The nonlinear spiking neural P systems (NSNP systems) are new types of computation models, in which the state of neurons is represented by real numbers, and nonlinear spiking rules handle the neuron's  ...  In this work, in order to improve computing performance, the weights and delays are introduced to the NSNP system, and universal nonlinear spiking neural P systems with delays and weights on synapses (  ...  □ Conclusions and Further Work e nonlinear spiking neural P (NSNP) systems are variants of spiking neural P (SNP) systems. Nonlinear functions are used flexibly in NSNP systems.  ... 
doi:10.1155/2021/3285719 pmid:34484319 pmcid:PMC8413071 fatcat:lqdoc3xg65aqfp7ldear4jkeva

Probability-based nonlinear modeling of neural dynamical systems with point-process inputs and outputs

Roman Sandler, Dong Song, Robert E Hampson, Sam A Deadwyler, Theodore Berger, Vasilis Marmarelis
2014 BMC Neuroscience  
This task, however, is made difficult by the inherent complexity of neural systems which are highly nonlinear, interconnected, dynamic, and subject to stochastic variations.  ...  Here we present a novel and intuitive methodology of modeling nonlinear dynamic systems with point process inputs and outputs, such as interconnected neuronal ensembles.  ...  an output spike given either one of those input spikes individually, i.e.: PBV2 (τ1, τ2) = P y[t] -x[t − τ1] ∩ x[t − τ2] −P(y[t]|x[t−τ1])−P(y[t]|x[t−τ2])+P(y[t]) (2) This method may be extended to describe  ... 
doi:10.1186/1471-2202-15-s1-p102 pmcid:PMC4124976 fatcat:uevnoty32zamzmy25y3dvv6rtq

Better than least squares: comparison of objective functions for estimating linear-nonlinear models

Tatyana O. Sharpee
2007 Neural Information Processing Systems  
In this model, the neural firing rate is a nonlinear function of a small number of relevant stimulus components.  ...  This paper compares a family of methods for characterizing neural feature selectivity with natural stimuli in the framework of the linear-nonlinear model.  ...  Introduction The application of system identification techniques to the study of sensory neural systems has a long history.  ... 
dblp:conf/nips/Sharpee07 fatcat:m65qgfyvcfh5df6nzvsxuurxlu

Identifying dendritic processing in a [Filter]-[Hodgkin Huxley] circuit

Aurel A Lazar, Yevgeniy B Slutskiy
2011 BMC Neuroscience  
Specifically, we show that the above identification methods can be readily applied to any neural (nonlinear dynamical) system with a limit cycle, including FitzHugh-Nagumo, Morris-Lecar and I Na,p + I  ...  In block-diagram form, these neural circuit models are of the [Filter]-[Spiking Neuron] type and as such represent a fundamental departure from the standard Linear-Nonlinear-Poisson (LNP) model that has  ...  Specifically, we show that the above identification methods can be readily applied to any neural (nonlinear dynamical) system with a limit cycle, including FitzHugh-Nagumo, Morris-Lecar and I Na,p + I  ... 
doi:10.1186/1471-2202-12-s1-p306 pmcid:PMC3240419 fatcat:mo3rddw3brd6pecyfmha6bjq5e

Synchrony-Division Neural Multiplexing: An Encoding Model [article]

Mohammad R. Rezaei, Milos R. Popovic, Steven A Prescott, Milad Lankarany
2021 medRxiv   pre-print
Second, we utilize linear nonlinear (LNL) cascade models and calculate temporal filters and static nonlinearities of differentially synchronized spikes.  ...  We demonstrate that these filters and nonlinearities are distinct for synchronous and asynchronous spikes.  ...  Materials and Methods Simulated mixed input According to the feasibility of neural systems to multiplexed coding, we simulated the activity of a homogeneous neural ensemble in response to a mixed-stimulus  ... 
doi:10.1101/2021.10.29.21265658 fatcat:k5lav76xq5gh5hiy7aqfcznuve

A Monte Carlo Sequential Estimation for Point Process Optimum Filtering

Yiwen Wang, A.R.C. Paiva, J.C. Principe
2006 The 2006 IEEE International Joint Conference on Neural Network Proceedings  
The previous decoding algorithms for Brain Machine Interfaces are normally utilized to estimate animal's movement from binned spike rates, which loses spike timing resolution and may exclude rich neural  ...  dynamics due to single spikes.  ...  Through the dynamic system model at each time index, the noise was randomly generated according to ) (η p .  ... 
doi:10.1109/ijcnn.2006.246904 dblp:conf/ijcnn/WangPP06 fatcat:hbxhobp65rfrtm4com32huanj4

Computational Identification of Receptive Fields

Tatyana O. Sharpee
2013 Annual Review of Neuroscience  
First, we discuss how such classic methods as reverse correlation/spike-triggered average and spike-triggered covariance can be generalized for use with natural stimuli to find the multiple relevant stimulus  ...  Second, ways to characterize neural feature selectivity while assuming that the neural responses exhibit a certain type of invariance, such as position invariance for visual neurons, are discussed.  ...  Miller, Michael P. Stryker, Ryan Rowekamp, and  ... 
doi:10.1146/annurev-neuro-062012-170253 pmid:23841838 pmcid:PMC3760488 fatcat:g3y5oim6avb4dacsq6mjglrsee

Learning Universal Computations with Spikes

Dominik Thalmeier, Marvin Uhlmann, Hilbert J. Kappen, Raoul-Martin Memmesheimer, Matthias Bethge
2016 PLoS Computational Biology  
Here we show how spiking neural networks may solve these different tasks.  ...  Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them.  ...  Developed the spiking network models and the learning methods: DT MU RMM. Performed numerical simulations: MU DT. Supervised the work: HJK RMM.  ... 
doi:10.1371/journal.pcbi.1004895 pmid:27309381 pmcid:PMC4911146 fatcat:oxs4rcxac5cwlarm5udw5iuqnu

Comparison of objective functions for estimating linear-nonlinear models [article]

Tatyana O. Sharpee
2008 arXiv   pre-print
In this model, the neural firing rate is a nonlinear function of a small number of relevant stimulus components.  ...  This paper compares a family of methods for characterizing neural feature selectivity with natural stimuli in the framework of the linear-nonlinear model.  ...  Introduction The application of system identification techniques to the study of sensory neural systems has a long history.  ... 
arXiv:0801.0311v1 fatcat:z3p3zuofrnbpxmsticg3ga5b6q

Reconstructing Stimulus-Driven Neural Networks from Spike Times

Duane Q. Nykamp
2002 Neural Information Processing Systems  
Although the method is based on a highly idealized linear-nonlinear approximation of neural response, we demonstrate via simulation that the approach can work with a more realistic, integrate-and-fire  ...  The distinction is computed from the spike times of the two neurons in response to a white noise stimulus.  ...  were an isolated linear-nonlinear system.  ... 
dblp:conf/nips/Nykamp02 fatcat:bmcshuzzmrg7zgmnx6fdspyblq

Correlation-distortion based identification of Linear-Nonlinear-Poisson models

Michael Krumin, Avner Shimron, Shy Shoham
2009 Journal of Computational Neuroscience  
systems.  ...  Recently, there has been rising interest in the second- and higher-order correlation structure of neural spike trains, and how it may be related to specific encoding relationships.  ...  using classical neural system identification experimental paradigms).  ... 
doi:10.1007/s10827-009-0184-0 pmid:19757006 fatcat:hxnuz5dnrzee5mwf27exktp74m

Approaching Optimal Nonlinear Dimensionality Reduction by a Spiking Neural Network

Álvaro Anzueto-Ríos, Felipe Gómez-Castañeda, Luis M. Flores-Nava, José A. Moreno-Cadenas
2021 Electronics  
This work deals with the presentation of a spiking neural network as a means for efficiently solving the reduction of dimensionality of data in a nonlinear manner.  ...  The underneath neural model, which can be integrated as neuromorphic hardware, becomes suitable for intelligent processing in edge computing within Internet of Things systems.  ...  For completeness, we mention alternative and recent strategies to create spiking systems that include transforming continuous deep neural systems into spiking systems [32] , using particular training  ... 
doi:10.3390/electronics10141679 fatcat:rz2stouydjdbramapjtarv346a

Signal transformation and coding in neural systems

V.Z. Marmarelis
1989 IEEE Transactions on Biomedical Engineering  
It incorporates nonlinear dynamics and spike generation mechanisms in a fairly general, yet parsimonious manner.  ...  The subject of signal transformation and coding in neural systems is fundamental in understanding information processing by the nervous system.  ...  One challenging aspect of neural system modeling concerns the role of nonlinearities.  ... 
doi:10.1109/10.16445 pmid:2646209 fatcat:o5sx6d7cazgudlpmzi4rxxbpzm

A novel cochlea partition model based on asynchronous bifurcation processor

Hironori Ishimoto, Masato Izawa, Hiroyuki Torikai
2015 Nonlinear Theory and Its Applications IEICE  
dumpers for the basilar membrane), inner hair cells (neural transducers), and spiral ganglion cells (parallel spikes density modulators).  ...  It is shown that the presented model can reproduce typical nonlinear responses of partitions of biological cochleae such as nonlinear DC response, nonlinear band-pass filtering, and adaptation.  ...  ., the OHC model and the BM-IHC model) and thus it can be regarded as a coupled system of two asynchronous cellular automaton oscillators.  ... 
doi:10.1587/nolta.6.207 fatcat:weihxgr7wnhbrdrlnyjqpggmqe

Page 5203 of Mathematical Reviews Vol. , Issue 2003g [page]

2003 Mathematical Reviews  
B. (3-LETH-P; Lethbridge, AB); Ali, M. K. (3-LETH-P; Lethbridge, AB) Nonlinear dynamics and chaos in information processing neural networks.  ...  Hamiltonian neural networks can be derived from attractor networks with bipartite connectivity [see P. De Wilde, Phys. Rev. E (3) 47 (1993), no. 2, 1392-1396].  ... 
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