2,761 Hits in 4.5 sec

Hebbian Learning from Spiking Neural P Systems View [chapter]

Miguel A. Gutiérrez-Naranjo, Mario J. Pérez-Jiménez
2009 Lecture Notes in Computer Science  
In this paper we present a first model for Hebbian learning in the framework of spiking neural P systems by using concepts borrowed from neuroscience and artificial neural network theory.  ...  Spiking neural P systems and artificial neural networks are computational devices which share a biological inspiration based on the flow of information among neurons.  ...  an extended spiking neural P system with thresholds taken from [6] .  ... 
doi:10.1007/978-3-540-95885-7_16 fatcat:qd4f5joicnakfmydhau4fl5o7a

Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons

Christian Keysers, David I. Perrett, Valeria Gazzola
2014 Behavioral and Brain Sciences  
and causality, and (b) Hebbian Learning generates valuable predictions about the neural properties of mirror neurons.  ...  Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: through Hebbian Learning mirror neurons become dynamic networks that calculate predictions and prediction  ...  Hebbian Learning and ASL are not "synonyms" (7.1) ASL takes a holistic, systems perspective.  ... 
doi:10.1017/s0140525x13002343 pmid:24775162 pmcid:PMC4563904 fatcat:o6crydq4dbbvbcsy6hb3nx5c5m

Spike timing dependent synaptic plasticity in biological systems

Patrick D. Roberts, Curtis C. Bell
2002 Biological cybernetics  
Different synapses show varying forms of such spike-timing dependent learning rules.  ...  Association of a presynaptic spike with a postsynaptic spike can lead to changes in synaptic efficacy that are highly dependent on the relative timing of the preand postsynaptic spikes.  ...  to the associative learning rates,P P ¼ a=k.  ... 
doi:10.1007/s00422-002-0361-y pmid:12461629 fatcat:wcygwt6n4vfwxbzvanykemns3a

Does computational neuroscience need new synaptic learning paradigms?

Johanni Brea, Wulfram Gerstner
2016 Current Opinion in Behavioral Sciences  
We take learning and synaptic plasticity as an example and point to open questions, such as one-shot learning and acquiring internal representations of the world for flexible planning.  ...  neuroscience is dominated by a few paradigmatic models, but it remains an open question whether the existing modelling frameworks are sufficient to explain observed behavioural phenomena in terms of neural  ...  Neural Comput 1998, 10:671-716. 38. Kempter R, Gerstner W, van Hemmen JL: Hebbian learning and spiking neurons. Phys Rev E 1999, 59:4498-4514. 39.  ... 
doi:10.1016/j.cobeha.2016.05.012 fatcat:ybxhnabw7vbgza7hcdorelmxre

Long and short-term synaptic plasticity and the formation of working memory: A case study

P.Del Giudice, M. Mattia
2001 Neurocomputing  
We study the collective behaviour of recurrent networks of integrate-and-"re neurons, connected by synapses whose e$cacies are subjected to a spike-driven, and rate sensitive, Hebbian LTP and a homosynaptic  ...  We discuss constraints relevant to the stability of the &learning' process, and the related role of long-term synaptic depression as well as the e!ects of inclusion of STD.  ...  Introduction Hebbian-like mechanisms of synaptic plasticity, suitable for implementing stimulus speci"c, persistent states of neural activity in recurrent networks of spiking neurons have been mostly studied  ... 
doi:10.1016/s0925-2312(01)00557-4 fatcat:xahiwdmjsfbojlxqkxs3q55c2y

Computational modeling of neural plasticity for self-organization of neural networks

Joseph Chrol-Cannon, Yaochu Jin
2014 Biosystems (Amsterdam. Print)  
learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence.  ...  of neural networks for accomplishing machine learning tasks such as classication and regression.  ...  Gene regulated neural plasticity in spiking neural networks have been applied to machine learning tasks.  ... 
doi:10.1016/j.biosystems.2014.04.003 pmid:24769242 fatcat:mqkncfb2ordzrokovnfspitgym

A spiking network that learns to extract spike signatures from speech signals

Amirhossein Tavanaei, Anthony S. Maida
2017 Neurocomputing  
Spiking neural networks (SNNs) with adaptive synapses reflect core properties of biological neural networks.  ...  We present a simple, novel, and efficient nonrecurrent SNN that learns to convert a speech signal into a spike train signature.  ...  Spike timing- learning method for spiking neural networks dependent plasticity: a Hebbian learning rule. with adaptive structure.  ... 
doi:10.1016/j.neucom.2017.01.088 fatcat:ts3wnoqck5crvlcs6hnoezzwmq

Functional consequences of inhibitory plasticity: homeostasis, the excitation-inhibition balance and beyond

Henning Sprekeler
2017 Current Opinion in Neurobiology  
Yger P, Stimberg M, Brette R: Fast learning with weak synaptic plasticity. J Neurosci 2015, 35:13351-13362. 34.  ...  (c) In traditional neural networks, a decorrelation of neural responses can be achieved by Hebbian plasticity in recurrent inhibitory connections.  ... 
doi:10.1016/j.conb.2017.03.014 pmid:28500933 fatcat:tbomihx77jchtabvadadrrtsaq

The temporal paradox of Hebbian learning and homeostatic plasticity

Friedemann Zenke, Wulfram Gerstner, Surya Ganguli
2017 Current Opinion in Neurobiology  
stabilize Hebbian learning.  ...  Hebbian plasticity, a synaptic mechanism which detects and amplifies co-activity between neurons, is considered a key ingredient underlying learning and memory in the brain.  ...  MacKay, The role of constraints in Hebbian learning, Neural Comput 6 [ 8 ] 8 E.  ... 
doi:10.1016/j.conb.2017.03.015 pmid:28431369 fatcat:y4kngqivybhetlea3usdxuqupi

Hebbian learning with winner take all for spiking neural networks

Ankur Gupta, Lyle N. Long
2009 2009 International Joint Conference on Neural Networks  
Learning methods for spiking neural networks are not as well developed as the traditional rate based networks, which widely use the back-propagation learning algorithm.  ...  We propose and implement an efficient Hebbian learning method with homeostasis for a network of spiking neurons. Similar to STDP, timing between spikes is used for synaptic modification.  ...  Spike time dependent plasticity (STDP) can be viewed as a more quantitative form of Hebbian learning. It emphasizes the importance of causality in synaptic strengthening or weakening.  ... 
doi:10.1109/ijcnn.2009.5178751 dblp:conf/ijcnn/GuptaL09 fatcat:aggxdkaj3zbqrfgm46if777vbu

Muscarinic acetylcholine receptors control baseline activity and Hebbian stimulus timing-dependent plasticity in fusiform cells of the dorsal cochlear nucleus

Roxana A. Stefanescu, Susan E. Shore
2017 Journal of Neurophysiology  
Consistent with StTDP alterations observed in tinnitus animals, atropine infusion induced a dominant pattern of inversion of StTDP mean population learning rule from a Hebbian to an anti-Hebbian profile  ...  This process can be viewed as a reduction in the noisy spiking activity of the fusiform cells in favor of a more significant activity.  ... 
doi:10.1152/jn.00270.2016 pmid:28003407 pmcid:PMC5349328 fatcat:jmod6nyvu5bufejocym3zusurq

Synergies Between Intrinsic and Synaptic Plasticity Mechanisms

Jochen Triesch
2007 Neural Computation  
with Hebbian Hebbian Learning Learning Question: [Triesch, NIPS 2005; Neural Comp., 2007] What is the interaction of Hebbian synaptic plasticity and intrinsic plasticity?  ...  ("top-down, computational (functional) view") Perspective B: The brain is a complex dynamical system with many non-linearly interacting parts.  ... 
doi:10.1162/neco.2007.19.4.885 pmid:17348766 fatcat:jfpflc56jrfh7olwomdn2kcsb4

Hebbian learning and predictive mirror neurons for actions, sensations and emotions

C. Keysers, V. Gazzola
2014 Philosophical Transactions of the Royal Society of London. Biological Sciences  
Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre-and postsynaptic activity  ...  Finally, we argue that Hebbian learning predicts mirror-like neurons for sensations and emotions and review evidence for the presence of such vicarious activations outside the motor system.  ...  We thank David Perrett and Rajat Thomas for fruitful discussions on Hebbian Learning.  ... 
doi:10.1098/rstb.2013.0175 pmid:24778372 pmcid:PMC4006178 fatcat:bpkutz4cprg5llqnmiiwnjnazm

Graphene Nanoribbon-based Synapses with Versatile Plasticity

H. Wang, N. Cucu Laurenciu, Y. Jiang, S.D. Cotofana
2019 2019 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH)  
neurophysiology, as it requires a small footprint, is energy effective, biocompatible, and versatile from the synaptic behaviour point of view.  ...  ., −50 mV to 50 mV pre-and post-synaptic spikes voltage range, and −60 ms to 60 ms time range.  ...  Synapses are the most ubiquitous neural system components which are ensuring information interchange between neurons.  ... 
doi:10.1109/nanoarch47378.2019.181301 dblp:conf/nanoarch/WangLJC19 fatcat:x52enfjb7rhhtgnsi3gfnu6xzm

Memristor Neural Network Training with Clock Synchronous Neuromorphic System

Sumin Jo, Wookyung Sun, Bokyung Kim, Sunhee Kim, Junhee Park, Hyungsoon Shin
2019 Micromachines  
With the system and memristor neural network, the image classification was successfully done using both the Hebbian unsupervised training and guide supervised training methods.  ...  In this study, a neuromorphic hardware system for multilayer unsupervised learning was designed, and unsupervised learning was performed with a memristor neural network.  ...  From a broad point of view, neuromorphic research has two main streams [6] .  ... 
doi:10.3390/mi10060384 pmid:31181763 pmcid:PMC6632029 fatcat:mg3ce526rzcmvhehckfriquxoy
« Previous Showing results 1 — 15 out of 2,761 results