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.

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 Biologically Supported Error-Correcting Learning Rule

1991 ◽  
Vol 3 (2) ◽  
pp. 201-212 ◽  
Author(s):  
Peter J. B. Hancock ◽  
Leslie S. Smith ◽  
William A. Phillips
Keyword(s):  
Synaptic Plasticity ◽  
Visual Cortex ◽  
Associative Memory ◽  
False Positive ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Synaptic Activity ◽  
Highly Active ◽  
Hebbian Learning Rule ◽  
Better Than

We show that a form of synaptic plasticity recently discovered in slices of the rat visual cortex (Artola et al. 1990) can support an error-correcting learning rule. The rule increases weights when both pre- and postsynaptic units are highly active, and decreases them when pre-synaptic activity is high and postsynaptic activation is less than the threshold for weight increment but greater than a lower threshold. We show that this rule corrects false positive outputs in feedforward associative memory, that in an appropriate opponent-unit architecture it corrects misses, and that it performs better than the optimal Hebbian learning rule reported by Willshaw and Dayan (1990).

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

scholarly journals Pseudo-Orthogonalization of Memory Patterns for Complex-Valued and Quaternionic Associative Memories

2017 ◽  
Vol 7 (4) ◽  
pp. 257-264 ◽  
Author(s):  
Toshifumi Minemoto ◽  
Teijiro Isokawa ◽  
Haruhiko Nishimura ◽  
Nobuyuki Matsui
Keyword(s):  
Associative Memory ◽  
Hebbian Learning ◽  
Recall Performance ◽  
Computational Cost ◽  
Learning Rule ◽  
Complex Numbers ◽  
Associative Memories ◽  
Hebbian Learning Rule ◽  
Highly Correlated ◽  
Complex Valued

AbstractHebbian learning rule is well known as a memory storing scheme for associative memory models. This scheme is simple and fast, however, its performance gets decreased when memory patterns are not orthogonal each other. Pseudo-orthogonalization is a decorrelating method for memory patterns which uses XNOR masking between the memory patterns and randomly generated patterns. By a combination of this method and Hebbian learning rule, storage capacity of associative memory concerning non-orthogonal patterns is improved without high computational cost. The memory patterns can also be retrieved based on a simulated annealing method by using an external stimulus pattern. By utilizing complex numbers and quaternions, we can extend the pseudo-orthogonalization for complex-valued and quaternionic Hopfield neural networks. In this paper, the extended pseudo-orthogonalization methods for associative memories based on complex numbers and quaternions are examined from the viewpoint of correlations in memory patterns. We show that the method has stable recall performance on highly correlated memory patterns compared to the conventional real-valued method.

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.

Sign in / Sign up

Export Citation Format

Share Document