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Hebbian Learning from Spiking Neural P Systems View
[chapter]
2009
Lecture Notes in Computer Science
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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
2014
Behavioral and Brain Sciences
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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
2002
Biological cybernetics
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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?
2016
Current Opinion in Behavioral Sciences
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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
2001
Neurocomputing
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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
2014
Biosystems (Amsterdam. Print)
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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
2017
Neurocomputing
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2016 pre-print arXiv:1606.00802v2 fatcat:qgayb5mgurf4bg3zq6vwnpthnmOther Versions
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
2017
Current Opinion in Neurobiology
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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
2017
Current Opinion in Neurobiology
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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
2009
2009 International Joint Conference on Neural Networks
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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
2017
Journal of Neurophysiology
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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
2007
Neural Computation
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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
2014
Philosophical Transactions of the Royal Society of London. Biological Sciences
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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
2019
2019 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH)
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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
2019
Micromachines
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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
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