AIMC Topic: Neurons

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Dendrite P systems.

Neural networks : the official journal of the International Neural Network Society
It was recently found that dendrites are not just a passive channel. They can perform mixed computation of analog and digital signals, and therefore can be abstracted as information processors. Moreover, dendrites possess a feedback mechanism. Motiva...

Neuronal mechanisms for sequential activation of memory items: Dynamics and reliability.

PloS one
In this article we present a biologically inspired model of activation of memory items in a sequence. Our model produces two types of sequences, corresponding to two different types of cerebral functions: activation of regular or irregular sequences....

Noise can speed backpropagation learning and deep bidirectional pretraining.

Neural networks : the official journal of the International Neural Network Society
We show that the backpropagation algorithm is a special case of the generalized Expectation-Maximization (EM) algorithm for iterative maximum likelihood estimation. We then apply the recent result that carefully chosen noise can speed the average con...

Extracting boolean and probabilistic rules from trained neural networks.

Neural networks : the official journal of the International Neural Network Society
This paper presents two approaches to extracting rules from a trained neural network consisting of linear threshold functions. The first one leads to an algorithm that extracts rules in the form of Boolean functions. Compared with an existing one, th...

Learning in the machine: To share or not to share?

Neural networks : the official journal of the International Neural Network Society
Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sh...

Before and beyond the Wilson-Cowan equations.

Journal of neurophysiology
The Wilson-Cowan equations represent a landmark in the history of computational neuroscience. Along with the insights Wilson and Cowan offered for neuroscience, they crystallized an approach to modeling neural dynamics and brain function. Although th...

Simulating Small Neural Circuits with a Discrete Computational Model.

Biological cybernetics
Simulations of neural activity are commonly based on differential equations. We address the question what can be achieved with a simplified discrete model. The proposed model resembles artificial neural networks enriched with additional biologically ...

Nonlinear Spiking Neural P Systems.

International journal of neural systems
This paper proposes a new variant of spiking neural P systems (in short, SNP systems), nonlinear spiking neural P systems (in short, NSNP systems). In NSNP systems, the state of each neuron is denoted by a real number, and a real configuration vector...

Probabilistic inference of binary Markov random fields in spiking neural networks through mean-field approximation.

Neural networks : the official journal of the International Neural Network Society
Recent studies have suggested that the cognitive process of the human brain is realized as probabilistic inference and can be further modeled by probabilistic graphical models like Markov random fields. Nevertheless, it remains unclear how probabilis...

Analysis of neurite length of hippocampal neurons cultured into 3D artificial network patterned microfluidic chips.

The International journal of neuroscience
The study aims to lay a foundational probe for the thorough application microfluidic chips in brain function research with microfluidic chips. Neuron slide culture is a common culture method , and the microfluidic chip with the artificial network pa...