Memristor-based circuit implementation of Competitive Neural Network based on online unsupervised Hebbian learning rule for pattern recognition

2021 ◽  
Author(s):  
Mian Li ◽  
Qinghui Hong ◽  
Xiaoping Wang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Circuit Implementation ◽  
Competitive Neural Network ◽  
Hebbian Learning Rule

Hebbian learning rule restraining catastrophic forgetting in pulse neural network

2005 ◽  
Vol 151 (3) ◽  
pp. 50-60
Author(s):  
Makoto Motoki ◽  
Tomoki Hamagami ◽  
Seiichi Koakutsu ◽  
Hironori Hirata
Keyword(s):  
Neural Network ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Hebbian Learning Rule

Efficient Associative Memory Based on a Nonlinear Function Constitution and Dynamic Synapses

2011 ◽  
Vol 225-226 ◽  
pp. 479-482
Author(s):  
Min Xia ◽  
Ying Cao Zhang ◽  
Xiao Ling Ye
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Hebbian Learning ◽  
Nonlinear Function ◽  
Learning Rule ◽  
Hopfield Neural Network ◽  
Connection Weight ◽  
Outer Product ◽  
Hebbian Learning Rule ◽  
Dynamic Synapses

Nonlinear function constitution and dynamic synapses, against spurious state for Hopfield neural network are proposed. The model of the dynamical connection weight and the updating scheme of the states of neurons are given. Nonlinear function constitution improves the conventional Hebbian learning rule with linear outer product method. Simulation results show that both nonlinear function constitution and dynamic synapses can effectively increase the ability of error tolerance; furthermore, associative memory of neural network with the new method can both enlarge attractive basin and increase storage capacity.

scholarly journals Hebbian Learning Rule Restraining Catastrophic Forgetting in Pulse Neural Network

2003 ◽  
Vol 123 (6) ◽  
pp. 1124-1133 ◽  
Author(s):  
Makoto Motoki ◽  
Tomoki Hamagami ◽  
Seiichi Koakutsu ◽  
Hironori Hirata
Keyword(s):  
Neural Network ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Hebbian Learning Rule

scholarly journals Synaptic memory devices from CoO/Nb:SrTiO 3 junction

2019 ◽  
Vol 6 (4) ◽  
pp. 181098 ◽  
Author(s):  
Le Zhao ◽  
Jie Xu ◽  
Xiantao Shang ◽  
Xue Li ◽  
Qiang Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Charge Trapping ◽  
Spike Timing ◽  
Hebbian Learning Rule ◽  
Synaptic Functions ◽  
Neuromorphic System ◽  
Reduce Energy Consumption ◽  
Novel Design

Non-volatile memristors are promising for future hardware-based neurocomputation application because they are capable of emulating biological synaptic functions. Various material strategies have been studied to pursue better device performance, such as lower energy cost, better biological plausibility, etc. In this work, we show a novel design for non-volatile memristor based on CoO/Nb:SrTiO 3 heterojunction. We found the memristor intrinsically exhibited resistivity switching behaviours, which can be ascribed to the migration of oxygen vacancies and charge trapping and detrapping at the heterojunction interface. The carrier trapping/detrapping level can be finely adjusted by regulating voltage amplitudes. Gradual conductance modulation can therefore be realized by using proper voltage pulse stimulations. And the spike-timing-dependent plasticity, an important Hebbian learning rule, has been implemented in the device. Our results indicate the possibility of achieving artificial synapses with CoO/Nb:SrTiO 3 heterojunction. Compared with filamentary type of the synaptic device, our device has the potential to reduce energy consumption, realize large-scale neuromorphic system and work more reliably, since no structural distortion occurs.

ASSOCIATIVE MEMORY IN NEURAL NETWORKS WITH THE HEBBIAN LEARNING RULE

1989 ◽  
Vol 03 (07) ◽  
pp. 555-560 ◽  
Author(s):  
M.V. TSODYKS
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Network Activity ◽  
Hebbian Learning Rule ◽  
Level Of Activity ◽  
The Mean ◽  
Full Network ◽  
Simultaneous Activity

We consider the Hopfield model with the most simple form of the Hebbian learning rule, when only simultaneous activity of pre- and post-synaptic neurons leads to modification of synapse. An extra inhibition proportional to full network activity is needed. Both symmetric nondiluted and asymmetric diluted networks are considered. The model performs well at extremely low level of activity p<K−1/2, where K is the mean number of synapses per neuron.

scholarly journals A Reward-Modulated Hebbian Learning Rule Can Explain Experimentally Observed Network Reorganization in a Brain Control Task

2010 ◽  
Vol 30 (25) ◽  
pp. 8400-8410 ◽  
Author(s):  
R. Legenstein ◽  
S. M. Chase ◽  
A. B. Schwartz ◽  
W. Maass
Keyword(s):  
Hebbian Learning ◽  
Learning Rule ◽  
Control Task ◽  
Hebbian Learning Rule

A Nonlinear Hebbian Network that Learns to Detect Disparity in Random-Dot Stereograms

1996 ◽  
Vol 8 (3) ◽  
pp. 545-566 ◽  
Author(s):  
Christopher W. Lee ◽  
Bruno A. Olshausen
Keyword(s):  
Hebbian Learning ◽  
Learning Rule ◽  
Coincidence Detection ◽  
Input Stream ◽  
Hebbian Learning Rule ◽  
Pairwise Correlations ◽  
Input Layer ◽  
Random Dot Stereograms ◽  
Rule Analysis ◽  
Shed Light

An intrinsic limitation of linear, Hebbian networks is that they are capable of learning only from the linear pairwise correlations within an input stream. To explore what higher forms of structure could be learned with a nonlinear Hebbian network, we constructed a model network containing a simple form of nonlinearity and we applied it to the problem of learning to detect the disparities present in random-dot stereograms. The network consists of three layers, with nonlinear sigmoidal activation functions in the second-layer units. The nonlinearities allow the second layer to transform the pixel-based representation in the input layer into a new representation based on coupled pairs of left-right inputs. The third layer of the network then clusters patterns occurring on the second-layer outputs according to their disparity via a standard competitive learning rule. Analysis of the network dynamics shows that the second-layer units' nonlinearities interact with the Hebbian learning rule to expand the region over which pairs of left-right inputs are stable. The learning rule is neurobiologically inspired and plausible, and the model may shed light on how the nervous system learns to use coincidence detection in general.

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