Mapping binary associative memories onto sigmoidal neural networks using a modified projection learning rule

1994 ◽  
Vol 41 (7) ◽  
pp. 474-477
Author(s):  
R. Perfetti
Keyword(s):  
Neural Networks ◽  
Learning Rule ◽  
Associative Memories

Chapter 9. Spike-Based Symbolic Computations on Bit Strings and Numbers

2021 ◽  
Author(s):  
Ceca Kraišniković ◽  
Wolfgang Maass ◽  
Robert Legenstein
Keyword(s):  
Neural Networks ◽  
Energy Efficient ◽  
Learning Rule ◽  
Spiking Neural Networks ◽  
New Paradigm ◽  
Symbolic Computations ◽  
Neuromorphic Hardware ◽  
State Of Research ◽  
Higher Cognitive Functions ◽  
The Brain

The brain uses recurrent spiking neural networks for higher cognitive functions such as symbolic computations, in particular, mathematical computations. We review the current state of research on spike-based symbolic computations of this type. In addition, we present new results which show that surprisingly small spiking neural networks can perform symbolic computations on bit sequences and numbers and even learn such computations using a biologically plausible learning rule. The resulting networks operate in a rather low firing rate regime, where they could not simply emulate artificial neural networks by encoding continuous values through firing rates. Thus, we propose here a new paradigm for symbolic computation in neural networks that provides concrete hypotheses about the organization of symbolic computations in the brain. The employed spike-based network models are the basis for drastically more energy-efficient computer hardware – neuromorphic hardware. Hence, our results can be seen as creating a bridge from symbolic artificial intelligence to energy-efficient implementation in spike-based neuromorphic hardware.

scholarly journals An approximate backpropagation learning rule for memristor based neural networks using synaptic plasticity

2017 ◽  
Vol 237 ◽  
pp. 193-199 ◽  
Author(s):  
D. Negrov ◽  
I. Karandashev ◽  
V. Shakirov ◽  
Yu. Matveyev ◽  
W. Dunin-Barkowski ◽  
...  
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Learning Rule ◽  
Backpropagation Learning

scholarly journals Biologically Plausible Sequence Learning with Spiking Neural Networks

2020 ◽  
Vol 34 (02) ◽  
pp. 1316-1323
Author(s):  
Zuozhu Liu ◽  
Thiparat Chotibut ◽  
Christopher Hillar ◽  
Shaowei Lin
Keyword(s):  
Neural Networks ◽  
Sequence Learning ◽  
Nervous Activity ◽  
Learning Rule ◽  
Generative Models ◽  
Spike Timing ◽  
Hopfield Network ◽  
Spiking Neural Networks ◽  
Theoretical Ground ◽  
Time Model

Motivated by the celebrated discrete-time model of nervous activity outlined by McCulloch and Pitts in 1943, we propose a novel continuous-time model, the McCulloch-Pitts network (MPN), for sequence learning in spiking neural networks. Our model has a local learning rule, such that the synaptic weight updates depend only on the information directly accessible by the synapse. By exploiting asymmetry in the connections between binary neurons, we show that MPN can be trained to robustly memorize multiple spatiotemporal patterns of binary vectors, generalizing the ability of the symmetric Hopfield network to memorize static spatial patterns. In addition, we demonstrate that the model can efficiently learn sequences of binary pictures as well as generative models for experimental neural spike-train data. Our learning rule is consistent with spike-timing-dependent plasticity (STDP), thus providing a theoretical ground for the systematic design of biologically inspired networks with large and robust long-range sequence storage capacity.

A simple selectionist learning rule for neural networks

1986 ◽  
Author(s):  
L. Personnaz ◽  
I. Guyon ◽  
A. Johannet ◽  
G. Dreyfus ◽  
G. Toulouse
Keyword(s):  
Neural Networks ◽  
Learning Rule
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