Mean-field theory of a plastic network of integrate-and-fire neurons

Chen, Chun-Chung; Jasnow, David

doi:10.1103/physreve.81.011907

Cited by 9 publications

(24 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the presence of plasticity, we assume that the weights evolve in time according to a nearest-neighbour STDP rule with soft bounds [2][3][4][5]8]. Therefore in the case of a post-(presynaptic) spike, emitted by neuron i (j) at time t, the weight w ij is potentiated (depressed) as…”

mentioning

confidence: 99%

“…(5) are all equal to one (apart from the autaptic terms which are set to zero). In presence of plasticity, we assume that the synaptic weights evolve in time according to a STDP rule with soft bounds, namely [2][3][4] …”

mentioning

confidence: 99%

“…By assuming that the synapses have memory just of the last emitted spike (nearest-neighbour STDP rule), the variables A j (resp. B i ) evolves as follows [2,4] …”

mentioning

confidence: 99%

See 2 more Smart Citations

Emergence of Slow Collective Oscillations in Neural Networks with Spike-Timing Dependent Plasticity

2013

View full text Add to dashboard Cite

The collective dynamics of excitatory pulse coupled neurons with spike timing dependent plasticity (STDP) is studied. The introduction of STDP induces persistent irregular oscillations between strongly and weakly synchronized states, reminiscent of brain activity during slow-wave sleep. We explain the oscillations by a mechanism, the Sisyphus Effect, caused by a continuous feedback between the synaptic adjustments and the coherence in the neural firing. Due to this effect, the synaptic weights have oscillating equilibrium values, and this prevents the system from relaxing into a stationary macroscopic state.

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Emergence of Slow Collective Oscillations in Neural Networks with Spike-Timing Dependent Plasticity

2013

View full text Add to dashboard Cite

show abstract

“…If the data is noisy, overtraining of the ANN may be of a concern as it may lose the ability to generalize and recognize the patterns. However, evolving ANNs are often used for modeling brain activity and artificial intelligence [28], space perception [29], and analysis of data requiring large inputs and large outputs [30] among other applications.…”

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

Sutariya¹,

Groshev²,

Sadana³

et al. 2013

TOBIOIJ

View full text Add to dashboard Cite

Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

show abstract

“…Recently, Chen and Jasnow [17] introduced the mean-field theory to study synaptic plasticity. In this theory, we need to introduce the "effective input" as a mean value of inputs to a population of several neurons, namely cluster neurons, from outside of the cluster neurons.…”

Section: Introductionmentioning

confidence: 99%

Analytical condition for synchrony in a neural network with two periodic inputs

Hashizume

Araki

2013

Phys. Rev. E

View full text Add to dashboard Cite

In this study, we apply a mean-field theory to the neural network model with two periodic inputs in order to clarify the conditions of synchronies. This mean-field theory yields a self-consistent condition for the synchrony and enables us to study the effects of synaptic connections for the behavior of neural networks. Then, we obtain a condition of synaptic connections for the synchrony with the cycle time T. The neurons in neural networks receive sensory inputs and top-down inputs from outside of the network. When the network neurons receive two or more inputs, their synchronization depends on the conditions of inputs. We also analyze this case using the mean-field theory. As a result, we clarify the following points: (i) The stronger synaptic connections enhance the shorter synchrony cycle of neurons. (ii) The cycle of the synchrony becomes longer as the cycle of external inputs becomes longer. (iii) The relationships among synaptic weights, the properties of input trains, and the cycle of synchrony are expressed by one equation, and there are two areas for asynchrony. With regard to the third point, the yielded equation is so simple for calculation that it can easily provide us with feasible and infeasible conditions for synchrony.

show abstract

Mean-field theory of a plastic network of integrate-and-fire neurons

Cited by 9 publications

References 43 publications

Emergence of Slow Collective Oscillations in Neural Networks with Spike-Timing Dependent Plasticity

Emergence of Slow Collective Oscillations in Neural Networks with Spike-Timing Dependent Plasticity

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

Analytical condition for synchrony in a neural network with two periodic inputs

Contact Info

Product

Resources

About