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A Closed Form Solution for Multiple-Input Spike Based Adaptive Filters

Il Park, Antonio R. C. Paiva, Jose C. Principe, John G. Harris
2007 Neural Networks (IJCNN), International Joint Conference on  
We propose a simple multiple-input spike based adaptive filter which is based on an integrate-and-fire neuron model.  ...  The optimal closed solution is derived, and the performance is analyzed with respect to noise in various parameters and measurement.  ...  P. thanks Karl Dockendorf and Jie Xu for insightful discussion. This work was partially supported by NSF grant ECS-0422718. A. R. C.  ... 
doi:10.1109/ijcnn.2007.4371276 dblp:conf/ijcnn/ParkPPH07 fatcat:uagvbmnzfbehdm3o4aatvtr2h4

Intrinsic Gain Modulation and Adaptive Neural Coding

Sungho Hong, Brian Nils Lundstrom, Adrienne L. Fairhall, Karl J. Friston
2008 PLoS Computational Biology  
In some forms of sensory adaptation, these linear filters and gain curve adjust very rapidly to changes in the variance of a randomly varying driving input.  ...  In many cases, the computation of a neural system can be reduced to a receptive field, or a set of linear filters, and a thresholding function, or gain curve, which determines the firing probability; this  ...  Note that the solution of Equation 3 generalizes a classical result [33] based on a binary nonlinearity to a simple closed form which applies to any type of nonlinearity.  ... 
doi:10.1371/journal.pcbi.1000119 pmid:18636100 pmcid:PMC2440820 fatcat:eoqwreprlfej7mzls7jf3iu4w4

Bayesian Filtering in Spiking Neural Networks: Noise, Adaptation, and Multisensory Integration

Omer Bobrowski, Ron Meir, Yonina C. Eldar
2009 Neural Computation  
A key requirement facing organisms acting in uncertain dynamic environments is the real-time estimation and prediction of environmental states, based on which effective actions can be selected.  ...  The framework is applicable to many situations of common interest, including noisy observations, non-Poisson spike trains (incorporating adaptation), multisensory integration, and state prediction.  ...  Acknowledgments The work of R.M. was partially supported by a grant from the Center for Complexity Science and by a grant from the Israel Science Foundation (no. 665/08).  ... 
doi:10.1162/neco.2008.01-08-692 pmid:19018706 fatcat:egq66cltdje4rnijyvbei23424

Indirect Adaptive Attenuation of Multiple Narrow-Band Disturbances Applied to Active Vibration Control

Tudor-Bogdan Airimitoaie, Ioan Dore Landau
2014 IEEE Transactions on Control Systems Technology  
As before, a multiple band-stop filter, (13) , should be computed based on the frequencies of the multiple narrowband disturbance.  ...  to consider a Youla-Kučera filter of the form Q(z −1 ) P BSF (z −1 ) (which will automatically introduce P BSF (z −1 ) as part of the closed loop poles).  ... 
doi:10.1109/tcst.2013.2257782 fatcat:tcg3figpgvcdrlhhizyhialuuy

Implementing homeostatic plasticity in VLSI networks of spiking neurons

Chiara Bartolozzi, Olga Nikolayeva, Giacomo Indiveri
2008 2008 15th IEEE International Conference on Electronics, Circuits and Systems  
We show experimental results where a homeostatic control is implemented as a hybrid SoftWare/HardWare (SW/HW) solution, and present analog circuits for a complete on-chip stand-alone solution, validated  ...  In this paper we propose analog circuits for implementing homeostatic plasticity mechanisms in VLSI spiking neural networks, compatible with local spike-based learning mechanisms.  ...  General homeostatic control scheme for a typical VLSI integrate and fire neuron: synaptic input spikes drive "fast" spike-based learning circuits; post-synaptic spikes are used to modulate global synaptic  ... 
doi:10.1109/icecs.2008.4674945 dblp:conf/icecsys/BartolozziNI08 fatcat:mksu6hq2a5hm7bq472wiiyvnwi

Adaptive probabilistic neural coding from deterministic spiking neurons: analysis from first principles [article]

Michael Famulare, Adrienne Fairhall
2011 arXiv   pre-print
We derive asymptotic closed-form expressions for the linear filter and estimates for the nonlinear decision function of the linear/nonlinear model.  ...  A neuron transforms its input into output spikes, and this transformation is the basic unit of computation in the nervous system.  ...  We thank Sean Trettel for helpful analysis regarding the maximally informative filter.  ... 
arXiv:1111.0097v2 fatcat:z642ujpm3bcxbolst36enrbsoe

Design of a silicon cochlea system with biologically faithful response

Shiwei Wang, Thomas Jacob Koickal, Godwin Enemali, Luiz Gouveia, Lei Wang, Alister Hamilton
2015 2015 International Joint Conference on Neural Networks (IJCNN)  
A cochlea filter-bank based on the improved three-stage filter cascade structure is used to model the frequency decomposition function of the basilar membrane; a filter tuning block is designed to model  ...  As shown in the simulation results, the system has biologically faithful frequency response, impulse response, and active adaptation behavior; also the system outputs multiple band-pass channels of spikes  ...  the filter gain and selectivity adaptively according to the sound input intensity.  ... 
doi:10.1109/ijcnn.2015.7280828 dblp:conf/ijcnn/WangKEGWH15 fatcat:2i4mj2k46bgrxpwmio5zhkjs5m

Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks [article]

Davide Zambrano, Sander M. Bohte
2016 arXiv   pre-print
Building on recent insights in neuroscience, we present an Adapting Spiking Neural Network (ASNN) based on adaptive spiking neurons.  ...  We demonstrate that this can also be successfully applied to a ReLU based deep convolutional neural network for classifying the MNIST dataset.  ...  Here we use the solution proposed in [14] based on a model of fast adaptation in spiking neurons: by dynamically adjusting the threshold, the size of the refractory responses can be controlled and the  ... 
arXiv:1609.02053v1 fatcat:lbivk4g65zaldergbrisxqs66y

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

Brian Gardner, André Grüning, Maurice J. Chacron
2016 PLoS ONE  
Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale  ...  As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one on a filtered  ...  Acknowledgments We are grateful to the reviewer Johanni Brea for his valuable comments, improving the quality of this paper. Author Contributions Conceptualization: BG AG. Data curation: BG AG.  ... 
doi:10.1371/journal.pone.0161335 pmid:27532262 pmcid:PMC4988787 fatcat:mnznc6phqbb3xft6atndub5k2e

Divisive suppression explains high-precision firing and contrast adaptation in retinal ganglion cells [article]

Yuwei Cui, Yanbin V. Wang, Silvia J. H. Park, Jonathan B. Demb, Daniel A. Butts
2016 biorxiv/medrxiv   pre-print
The full model accurately predicted spike responses with unprecedented millisecond precision, and accurately described contrast adaption of the spike train.  ...  Here, we present a circuit-inspired model of retinal ganglion cell computation, targeted to explain their temporal dynamics and adaptation to contrast.  ...  in the Guide for Care and Use of Laboratory Animals of the National Institutes of Health.  ... 
doi:10.1101/064592 fatcat:7dmalk536jatzgitijfqehlpri

Divisive suppression explains high-precision firing and contrast adaptation in retinal ganglion cells

Yuwei Cui, Yanbin V Wang, Silvia J H Park, Jonathan B Demb, Daniel A Butts
2016 eLife  
Here, we present a circuit-inspired model of retinal ganglion cell computation, targeted to explain their temporal dynamics and adaptation to contrast.  ...  The full model accurately predicted spike responses with unprecedented millisecond precision, and accurately described contrast adaptation of the spike train.  ...  in the Guide for Care and Use of Laboratory Animals of the National Institutes of Health.  ... 
doi:10.7554/elife.19460 pmid:27841746 pmcid:PMC5108594 fatcat:lrcg5wdbrfcfhnfgv2svip25eq

Temporal Contrast Adaptation in the Input and Output Signals of Salamander Retinal Ganglion Cells

Kerry J. Kim, Fred Rieke
2001 Journal of Neuroscience  
Contrast adaptation in the input currents of a ganglion cell, however, was unable to account for the extent of adaptation in the output spike trains of the cell, indicating that mechanisms intrinsic to  ...  Contrast adaptation differed for ON and OFF cells, with both the kinetics and amplitude of the light-evoked currents of OFF cells adapting more strongly than those of ON cells.  ...  An adaptation mechanism based on properties of voltage-activated Na ϩ channels could provide a simple solution to this general problem.  ... 
doi:10.1523/jneurosci.21-01-00287.2001 pmid:11150346 fatcat:5bj7fx6yyzewpkhe3v6mjtwqzm

Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation

Moritz Augustin, Josef Ladenbauer, Fabian Baumann, Klaus Obermayer, Ralf Haefner
2017 PLoS Computational Biology  
We evaluate the reduced models for a wide range of biologically plausible input statistics and find that both approximation approaches lead to spike rate models that accurately reproduce the spiking behavior  ...  decomposition of the Fokker-Planck operator, the other is based on a cascade of two linear filters and a nonlinearity, which are determined from the Fokker-Planck equation and semi-analytically approximated  ...  Derived low-dimensional spike rate models Author Contributions  ... 
doi:10.1371/journal.pcbi.1005545 pmid:28644841 pmcid:PMC5507472 fatcat:vkxwdfwk7jhodgupfjeualcv74

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

Michael Krumin, Avner Shimron, Shy Shoham
2009 Journal of Computational Neuroscience  
Linear-Nonlinear-Poisson (LNP) models are a popular and powerful tool for describing encoding (stimulus-response) transformations by single sensory as well as motor neurons.  ...  Here, we propose that LNP encoding models can potentially be identified strictly from the correlation transformations they induce, and develop a computational method for identifying minimum-phase single-neuron  ...  In this paper we propose and develop a new method for indirectly identifying neuron encoding models from input-output correlation transformations by adapting our recent correlation-distortion results for  ... 
doi:10.1007/s10827-009-0184-0 pmid:19757006 fatcat:hxnuz5dnrzee5mwf27exktp74m

A Spiking Neuron and Population Model based on the Growth Transform Dynamical System [article]

Ahana Gangopadhyay, Darshit Mehta, Shantanu Chakrabartty
2019 bioRxiv   pre-print
This paper introduces a new spiking neuron and population model based on a Growth Transform dynamical system.  ...  One such variant described in this paper is a network that adapts itself according to the global dynamics to encode the steady-state solution with a reduced number of spikes.  ...  We then introduce a form of the energy function for a non-spiking variant of a neuron model based on this dynamical system, and extend it to a spiking variant that stochastically minimizes the network  ... 
doi:10.1101/523944 fatcat:kxqrmxchezgebmchquecggjjgm
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