We simulate the dynamics of a multineuron model (RS model) with an energy function given by the product between the squared distances in phase space between the state of the net and the stored patterns. We obtain the relative frequency $f(m_0)$ that an arbitrary pattern is retrieved from an initial overlap $m_0$ and estimate the size of the basins of attraction for different activities $a$. Two limit cases are taken into account : when patterns and antipatterns are stored (p.a.s.) and when only the patterns are stored (o.p.s.). For the $a=0.5$, p.a.s. nets a limit for the load parameter was not found, but for the other cases ($a\neq 0.5$ or o.p.s. configuration) the relative size of the basins of attraction may become too small
<p>(a) For the AM dynamics, we start the network with a state that differs 10% from one of the store...
<p>The cue corresponding to pattern one is given by setting the activity of neurons inside a 15×15 s...
We solve the dynamics of Hopfield-type neural networks which store sequences of patterns, close to s...
The static and dynamical properties of neural networks having many-neuron interactions are studied a...
We study generalizations of the Hopfield model for associative memory which contain interactions of ...
Abstract. The typical fraction of the space of interactions between each pair of N Ising spins which...
In this study, we investigate the correspondence between dynamic patterns of behavior in two types o...
We performed a systematic study of the sizes of the basins of attraction in a Hebbian-type neural ne...
<p>Each simulation uses 10 neural PING oscillator nodes with the connection probability and weight b...
<p>The network is comprised of N = 70×70 = 4900 neurons, each connected to <i>C</i> = 0.05<i>N</i> o...
A) Our model can be straightforwardly extended to code for more than 2 stimuli. Here, we trained a n...
abstract: The Morris-Lecar two-dimensional conductance-based model for an excitable membrane can be ...
We investigate a model of randomly copuled neurons. The elements are FitzHgh-Nagumo excitable neuron...
<p>The black line shows the resulting multiunit pattern of two components with peak activity at ZT 9...
This paper models the dynamics of a large set of interacting neurons within the framework of statist...
<p>(a) For the AM dynamics, we start the network with a state that differs 10% from one of the store...
<p>The cue corresponding to pattern one is given by setting the activity of neurons inside a 15×15 s...
We solve the dynamics of Hopfield-type neural networks which store sequences of patterns, close to s...
The static and dynamical properties of neural networks having many-neuron interactions are studied a...
We study generalizations of the Hopfield model for associative memory which contain interactions of ...
Abstract. The typical fraction of the space of interactions between each pair of N Ising spins which...
In this study, we investigate the correspondence between dynamic patterns of behavior in two types o...
We performed a systematic study of the sizes of the basins of attraction in a Hebbian-type neural ne...
<p>Each simulation uses 10 neural PING oscillator nodes with the connection probability and weight b...
<p>The network is comprised of N = 70×70 = 4900 neurons, each connected to <i>C</i> = 0.05<i>N</i> o...
A) Our model can be straightforwardly extended to code for more than 2 stimuli. Here, we trained a n...
abstract: The Morris-Lecar two-dimensional conductance-based model for an excitable membrane can be ...
We investigate a model of randomly copuled neurons. The elements are FitzHgh-Nagumo excitable neuron...
<p>The black line shows the resulting multiunit pattern of two components with peak activity at ZT 9...
This paper models the dynamics of a large set of interacting neurons within the framework of statist...
<p>(a) For the AM dynamics, we start the network with a state that differs 10% from one of the store...
<p>The cue corresponding to pattern one is given by setting the activity of neurons inside a 15×15 s...
We solve the dynamics of Hopfield-type neural networks which store sequences of patterns, close to s...