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A self-organizing short-term dynamical memory network
2018
Neural Networks
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The prior work, however, requires precisely constructed synaptic connectivity matrices, without explaining how this would arise in a biological neural network. ...
We identified a synaptic plasticity mechanism that overcomes this fine-tuning problem, enabling neural networks to learn to form stable representations. ...
This work was supported by a Canadian Institute for Advanced Research (CIFAR), Azrieli Global Scholar Award, and a Google Faculty Research Award. ...
doi:10.1016/j.neunet.2018.06.008
pmid:30007123
fatcat:3kz7fwaonbex7nw6ganmq4d77u
Impact of Synaptic Device Variations on Pattern Recognition Accuracy in a Hardware Neural Network
2018
Scientific Reports
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Interestingly, in neuromorphic systems, the processing and storing of information can be performed simultaneously by modulating the connection strength of a synaptic device (i.e., synaptic weight). ...
Neuromorphic systems (hardware neural networks) derive inspiration from biological neural systems and are expected to be a computing breakthrough beyond conventional von Neumann architecture. ...
neural network contribute to the learning process, a precise adjustment of all synaptic weights is necessarily required. ...
doi:10.1038/s41598-018-21057-x
pmid:29422641
pmcid:PMC5805704
fatcat:a365ghghejfmhk23yi77scqu3u
A New Computational Model for Astrocytes and Their Role in Biologically Realistic Neural Networks
2018
Computational Intelligence and Neuroscience
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In this article, we aim to study the role of astrocytes in synaptic plasticity by exploring whether tripartite synapses are capable of improving the performance of a neural network. ...
This research provides computational evidence to begin elucidating the possible beneficial role of astrocytes in synaptic plasticity and performance of a neural network. ...
Some of this work has been presented in the Mathematical and Computational Cognitive Science weekly colloquium in the Department of Psychological Sciences at Purdue University by Zahra Sajedinia. ...
doi:10.1155/2018/3689487
pmid:30073021
pmcid:PMC6057343
fatcat:vaqvqskmbfa6zntgjz3aqz6iw4
Beyond Hebb: Exclusive-OR and Biological Learning
2000
Physical Review Letters
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2000 accepted arXiv:adap-org/9909005v2 fatcat:7465u3drffhz5dp6jpgzr2l5syOther Versions
A learning algorithm for multilayer neural networks based on biologically plausible mechanisms is studied. ...
Motivated by findings in experimental neurobiology, we consider synaptic averaging in the induction of plasticity changes, which happen on a slower time scale than firing dynamics. ...
To summarize, we studied a biologically motivated model for goal-directed learning in multilayer neural networks. ...
doi:10.1103/physrevlett.84.3013
pmid:11018999
fatcat:xtzu5mfxsfgrfgcpwz4qzuzivi
A Neuromorphic Architecture for Object Recognition and Motion Anticipation Using Burst-STDP
2012
PLoS ONE
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We demonstrate the abilities of our design in a simple environment with distractor objects, multiple objects moving concurrently, and in the presence of noise. ...
STDP is responsible for the strengthening (or weakening) of synapses in relation to pre-and post-synaptic spike times and has been described as a Hebbian paradigm taking place both in vitro and in vivo ...
of the biologically inspired neural network. ...
doi:10.1371/journal.pone.0036958
pmid:22615855
pmcid:PMC3352850
fatcat:dly775oddnanhl7iwstlyzntvq
Synaptic depression in deep neural networks for speech processing
2016
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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In addition, we show that when synaptic depression is included in a deep neural network trained for phoneme classification, the performance of the network improves under noisy conditions not included in ...
We observed that when synaptic depression is added to the hidden layers of a neural network, it reduces the effect of changing background activity in the node activations. ...
INTRODUCTION One of the major differences between biological neurons and the neuron models used in artificial neural networks is the ability of synaptic weights to change dynamically [1, 2, 3, 4, 5] ...
doi:10.1109/icassp.2016.7472802
pmid:28286424
pmcid:PMC5344995
fatcat:pij3dyt6ufb4bdbry4htj7d2ze
Indirect training of a spiking neural network for flight control via spike-timing-dependent synaptic plasticity
2010
49th IEEE Conference on Decision and Control (CDC)
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Recently, spiking neural networks (SNNs) have been shown capable of approximating the dynamics of biological neuronal networks, and of being trainable by biologicallyplausible learning mechanisms, such ...
This paper presents a novel indirect training approach to modulate spike-timing-dependent plasticity (STDP) in an action SNN that serves as a flight controller without directly manipulating its weights ...
Thus, the synaptic weights of NN c , defined as w ij , are directly adjustable, while the synaptic efficacies of NN a can only be modified through a simulated STDP mechanism which is modulated by the input ...
doi:10.1109/cdc.2010.5717260
dblp:conf/cdc/FoderaroHF10
fatcat:jscvpkontvenho2etp6icgqngi
Lessons from connectionism in differentiating knowledge types
2014
e-mentor
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synaptic damage occurring within the biological neural networks of the brain. ...
weights of any neural system, whether synthetic or biological. ...
doi:10.15219/em55.1112
fatcat:g6mwdwzkfncmdhbk3bjr4kpxu4
Situation-based memory in spiking neuron-astrocyte network
[article]
2022
arXiv
pre-print
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Our results show that such a system tested on synthesized data with selective astrocyte-induced modulation of neuronal activity provides an enhancement of retrieval quality in comparison to standard spiking ...
neural networks trained via Hebbian plasticity only. ...
Acknowledgements The reported study was funded by the Ministry of Science and Higher Education of the Russian Federation (project no. 075-15-2020-808). ...
arXiv:2202.07218v1
fatcat:blhnm6ukxnctjmkhka47ipmjni
Phase Change Memtransistive Synapse
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2105.13861v1 fatcat:5cm2jma5afc7vfo4kkjchiluni
We show that such biomimetic synapses can enable some powerful cognitive frameworks, such as the short-term spike-timing-dependent plasticity (ST-STDP) and stochastic Hopfield neural networks. ...
In the mammalian nervous system, various synaptic plasticity rules act, either individually or synergistically, and over wide-ranging timescales to dictate the processes that enable learning and memory ...
Figure 5a shows a bioinspired Hopfield neural network (HNN), comprising phase change memtransistors as synaptic weights. ...
arXiv:2105.13861v2
fatcat:7yuz23lfw5egpfn4jng75kzrpe
Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise
2020
PLoS ONE
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In this study, the scale-free spiking neural network (SFSNN) is constructed, in which the Izhikevich neuron model is employed as a node, and the synaptic plasticity model including excitatory and inhibitory ...
SFSNNs with the low ACC. (2) The neural information processing of the SFSNN is the linkage effect of dynamic changes in neuron firing, synaptic weight and topological characteristics. (3) The synaptic ...
In this study, the SFSNN is a network with dynamic regulation of synaptic weight. ...
doi:10.1371/journal.pone.0244683
pmid:33382788
fatcat:w3as6htmtjbbzift3gh4fjdyxy
Analog implementation of pulse-coupled neural networks
1999
IEEE Transactions on Neural Networks
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A computational style described in this article mimics a biological neural network using pulse-stream signaling and analog summation and multiplication. ...
This paper presents a compact architecture for analog CMOS hardware implementation of voltage-mode pulsecoupled neural networks (PCNN's). ...
In this scheme both excitatory and inhibitory synaptic weights are controlled, as in natural biological neural networks. ...
doi:10.1109/72.761710
pmid:18252551
fatcat:nqpvcxsseze4vjeimbgx537njy
PAX: A mixed hardware/software simulation platform for spiking neural networks
2010
Neural Networks
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In this paper we present a mixed hardware-software platform, specifically designed for the simulation of spiking neural networks, using conductance-based models of neurons and synaptic connections with ...
max 300 words) Many hardware-based solutions now exist for the simulation of bio-like neural networks. ...
Lower plots: synaptic weights of the 36 synapses versus time. A) Uncorrelated input synaptic noise, = 0. B) Correlation of input synaptic noise, = 0.8. ...
doi:10.1016/j.neunet.2010.02.006
pmid:20434309
fatcat:2dtewzdc3zh3tatompvknggmmq
A hardware friendly unsupervised memristive neural network with weight sharing mechanism
2019
Neurocomputing
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2019 pre-print arXiv:1901.00100v1 fatcat:z6ayr7pzhze4plpzhwmwo7g2ge
A weight sharing mechanism is proposed to bridge the gap of network scale and hardware resource. ...
Memristive neural networks (MNNs), which use memristors as neurons or synapses, have become a hot research topic recently. ...
(2042017gf0052 and 2042016kf0189) and the Natural Science Foundation of Hubei Province, China (2017CFB660). ...
doi:10.1016/j.neucom.2018.12.049
fatcat:6vbdmdurybcsfb5uty62hxzh5y
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation
2012
Neural Networks
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Citation: SOLTOGGIO, A. and STANLEY, K.O., 2012. From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation. Neural Networks, 34 pp. 28-41. ...
The neural model learns, memorizes and modifies different behaviors that lead to positive modulation in a variety of settings. ...
The role of noise This section shows that neural noise is essential to exploring all network states in networks of one neuron to networks of many. ...
doi:10.1016/j.neunet.2012.06.005
pmid:22796669
fatcat:zuddmbqbtzf5lgxbl6dtc2lizy
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