3,607 research outputs found
Coupled-Oscillator Associative Memory Array Operation for Pattern Recognition
Operation of the array of coupled oscillators underlying the associative memory function is demonstrated for various interconnection schemes (cross-connect, star phase keying and star frequency keying) and various physical implementation of oscillators (van der Pol, phase-locked loop, spin torque). The speed of synchronization of oscillators and the evolution of the degree of matching is studied as a function of device parameters. The dependence of errors in association on the number of the memorized patterns and the distance between the test and the memorized pattern is determined for Palm, Furber and Hopfield association algorithms
Method of increasing the information capacity of associative memory of oscillator neural networks using high-order synchronization effect
Computational modelling of two- and three-oscillator schemes with thermally
coupled -switches is used to demonstrate a novel method of pattern
storage and recognition in an impulse oscillator neural network (ONN) based on
the high-order synchronization effect. The method ensures high information
capacity of associative memory, i.e. a large number of synchronous states
. Each state in the system is characterized by the synchronization order
determined as the ratio of harmonics number at the common synchronization
frequency. The modelling demonstrates attainment of of several orders
both for a three-oscillator scheme ~650 and for a two-oscillator scheme
~260. A number of regularities are obtained, in particular, an optimal
strength of oscillator coupling is revealed when has a maximum. A general
tendency toward information capacity decrease is shown when the coupling
strength and switch inner noise amplitude increase. An algorithm of pattern
storage and test vector recognition is suggested. It is also shown that the
coordinate number in each vector should be one less than the switch number to
reduce recognition ambiguity. The demonstrated method of associative memory
realization is a general one and it may be applied in ONNs with various
mechanisms and oscillator coupling topology.Comment: 18 pages, 8 figure
Synchronized Oscillations During Cooperative Feature Linking in a Cortical Model of Visual Perception
A neural network model of synchronized oscillator activity in visual cortex is presented in order to account for recent neurophysiological findings that such synchronization may reflect global properties of the stimulus. In these recent experiments, it was reported that synchronization of oscillatory firing responses to moving bar stimuli occurred not only for nearby neurons, but also occurred between neurons separated by several cortical columns (several mm of cortex) when these neurons shared some receptive field preferences specific to the stimuli. These results were obtained not only for single bar stimuli but also across two disconnected, but colinear, bars moving in the same direction. Our model and computer simulations obtain these synchrony results across both single and double bar stimuli. For the double bar case, synchronous oscillations are induced in the region between the bars, but no oscillations are induced in the regions beyond the stimuli. These results were achieved with cellular units that exhibit limit cycle oscillations for a robust range of input values, but which approach an equilibrium state when undriven. Single and double bar synchronization of these oscillators was achieved by different, but formally related, models of preattentive visual boundary segmentation and attentive visual object recognition, as well as nearest-neighbor and randomly coupled models. In preattentive visual segmentation, synchronous oscillations may reflect the binding of local feature detectors into a globally coherent grouping. In object recognition, synchronous oscillations may occur during an attentive resonant state that triggers new learning. These modelling results support earlier theoretical predictions of synchronous visual cortical oscillations and demonstrate the robustness of the mechanisms capable of generating synchrony.Air Force Office of Scientific Research (90-0175); Army Research Office (DAAL-03-88-K0088); Defense Advanced Research Projects Agency (90-0083); National Aeronautics and Space Administration (NGT-50497
The spectro-contextual encoding and retrieval theory of episodic memory.
The spectral fingerprint hypothesis, which posits that different frequencies of oscillations underlie different cognitive operations, provides one account for how interactions between brain regions support perceptual and attentive processes (Siegel etal., 2012). Here, we explore and extend this idea to the domain of human episodic memory encoding and retrieval. Incorporating findings from the synaptic to cognitive levels of organization, we argue that spectrally precise cross-frequency coupling and phase-synchronization promote the formation of hippocampal-neocortical cell assemblies that form the basis for episodic memory. We suggest that both cell assembly firing patterns as well as the global pattern of brain oscillatory activity within hippocampal-neocortical networks represents the contents of a particular memory. Drawing upon the ideas of context reinstatement and multiple trace theory, we argue that memory retrieval is driven by internal and/or external factors which recreate these frequency-specific oscillatory patterns which occur during episodic encoding. These ideas are synthesized into a novel model of episodic memory (the spectro-contextual encoding and retrieval theory, or "SCERT") that provides several testable predictions for future research
Reward prediction error and declarative memory
Learning based on reward prediction error (RPE) was originally proposed in the context of nondeclarative memory. We postulate that RPE may support declarative memory as well. Indeed, recent years have witnessed a number of independent empirical studies reporting effects of RPE on declarative memory. We provide a brief overview of these studies, identify emerging patterns, and discuss open issues such as the role of signed versus unsigned RPEs in declarative learning
Complete synchronization in coupled Type-I neurons
For a system of type-I neurons bidirectionally coupled through a nonlinear
feedback mechanism, we discuss the issue of noise-induced complete
synchronization (CS). For the inputs to the neurons, we point out that the rate
of change of instantaneous frequency with the instantaneous phase of the
stochastic inputs to each neuron matches exactly with that for the other in the
event of CS of their outputs. Our observation can be exploited in practical
situations to produce completely synchronized outputs in artificial devices.
For excitatory-excitatory synaptic coupling, a functional dependence for the
synchronization error on coupling and noise strengths is obtained. Finally we
report an observation of noise-induced CS between non-identical neurons coupled
bidirectionally through random non-zero couplings in an all-to- all way in a
large neuronal ensemble.Comment: 24 pages, 9 figure
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