A New Associative Model with Dynamical Synapses

Abstract

The brain is not a huge fixed neural network, but a dynamic, changing neural network that continuously adapts to meet the demands of communication and computational needs. In classical neural networks approaches, particularly associative memory models, synapses are only adjusted during the training phase. After this phase, synapses are no longer adjusted. In this paper we describe a new dynamical model where synapses of the associative memory could be adjusted even after the training phase as a response to an input stimulus. We provide some propositions that guarantee perfect and robust recall of the fundamental set of associations. In addition, we describe the behavior of the proposed associative model under noisy versions of the patterns. At last, we present some experiments aimed to show the accuracy of the proposed model.

Tuning the parameters of an integrate and fire neuron via a genetic algorithm for solving pattern recognition problems

“…Different combinations for the genetic algorithm parameters were tested based on their meaning and on common literature recommended values [19][20][21][22], but the best results were achieved by the parameters described here.…”

“…In [21], a 100% efficiency was achieved for this dataset. Table 19 shows the classification efficiencies for the Screw dataset using feedforward neural networks (FNN).…”

Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

“…The object recognition problem was taken from [26], and the spiral, synthetic 1, and synthetic 2 datasets were developed in our laboratory. The pattern dispersions of these datasets are shown in Figure 2.…”

Acquisition system and approximation of brain signals

“…There are two kinds of signal approximation: one is the estimation of parameters [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], and the other is the interpolation [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”

“…In [11], a gas chamber and four defect models were designed. In [12], the authors describe a new dynamic model where synapses of the associative memory could be adjusted. In [13,14], the authors introduce new bidirectional hetero-associative memory models for true-colour patterns.…”