Connection Strategies in Associative Memory Models

“The original publication is available at www.springerlink.com”. Copyright Springer.The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks.Peer reviewe

Association patterns and foraging behaviour in natural and artificial guppy shoals

Animal groups are often nonrandom assemblages of individuals that tend to be assorted by factors such as sex, body size, relatedness and familiarity. Laboratory studies using fish have shown that familiarity among shoal members confers a number of benefits to individuals, such as increased foraging success. However, it is unclear whether fish in natural shoals obtain these benefits through association with familiars. We investigated whether naturally occurring shoals of guppies, Poecilia reticulata, are more adept at learning a novel foraging task than artificial (in which we selected shoal members randomly) shoals. We used social network analysis to compare the structures of natural and artificial shoals and examined whether shoal organization predicts patterns of foraging behaviour. Fish in natural shoals benefited from increased success in the novel foraging task compared with fish in artificial shoals. Individuals in natural shoals showed a reduced latency to approach the novel feeder, followed more and formed smaller subgroups compared to artificial shoals. Our findings show that fish in natural shoals do gain foraging benefits and that this may be facilitated by a reduced perception of risk among familiarized individuals and/or enhanced social learning mediated by following other individuals and small group sizes. Although the structure of shoals was stable over time, we found no direct relationship between shoal social structure and patterns of foraging behaviour

Backpropagation Artificial Neural Network To Detect Hyperthermic Seizures In Rats

A three-layered feed-forward back-propagation Artificial Neural Network was used to classify the seizure episodes in rats. Seizure patterns were induced by subjecting anesthetized rats to a Biological Oxygen Demand incubator at 45-47ºC for 30 to 60 minutes. Selected fast Fourier transform data of one second epochs of electroencephalogram were used to train and test the network for the classification of seizure and normal patterns. The results indicate that the present network with the architecture of 40-12-1 (input-hidden-output nodes) agrees with manual scoring of seizure and normal patterns with a high recognition rate of 98.6%

A Hardware Implementation of Artificial Neural Network Using Field Programmable Gate Arrays

An artificial neural network algorithm is implemented using a field programmable gate array hardware. One hidden layer is used in the feed-forward neural network structure in order to discriminate one class of patterns from the other class in real time. With five 8-bit input patterns, six hidden nodes, and one 8-bit output, the implemented hardware neural network makes decision on a set of input patterns in 11 clocks and the result is identical to what to expect from off-line computation. This implementation may be used in level 1 hardware triggers in high energy physics experimentsComment: 13 pages, 4 figures, submitted to Nucl. Instr. Meth.

Lenia and Expanded Universe

We report experimental extensions of Lenia, a continuous cellular automata family capable of producing lifelike self-organizing autonomous patterns. The rule of Lenia was generalized into higher dimensions, multiple kernels, and multiple channels. The final architecture approaches what can be seen as a recurrent convolutional neural network. Using semi-automatic search e.g. genetic algorithm, we discovered new phenomena like polyhedral symmetries, individuality, self-replication, emission, growth by ingestion, and saw the emergence of "virtual eukaryotes" that possess internal division of labor and type differentiation. We discuss the results in the contexts of biology, artificial life, and artificial intelligence.Comment: 8 pages, 5 figures, 1 table; submitted to ALIFE 2020 conferenc

Learning and discrimination through STDP in a top-down modulated associative memory

This article underlines the learning and discrimination capabilities of a model of associative memory based on artificial networks of spiking neurons. Inspired from neuropsychology and neurobiology, the model implements top-down modulations, as in neocortical layer V pyramidal neurons, with a learning rule based on synaptic plasticity (STDP), for performing a multimodal association learning task. A temporal correlation method of analysis proves the ability of the model to associate specific activity patterns to different samples of stimulation. Even in the absence of initial learning and with continuously varying weights, the activity patterns become stable enough for discrimination