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Neurons

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Artificial Tactile Perception System Based on Spiking Tactile Neurons and Spiking Neural Networks.

ACS applied materials & interfaces
The artificial tactile perception system of this work utilizes a fully connected spiking neural network (SNN) comprising two layers. Its architecture is streamlined and energy-efficient as it directly integrates spiking tactile neurons with piezoresi...

A thermodynamical model of non-deterministic computation in cortical neural networks.

Physical biology
Neuronal populations in the cerebral cortex engage in probabilistic coding, effectively encoding the state of the surrounding environment with high accuracy and extraordinary energy efficiency. A new approach models the inherently probabilistic natur...

Network attractors and nonlinear dynamics of neural computation.

Current opinion in neurobiology
The importance of understanding the nonlinear dynamics of neural systems, and the relation to cognitive systems more generally, has been recognised for a long time. Approaches that analyse neural systems in terms of attractors of autonomous networks ...

There is a fundamental, unbridgeable gap between DNNs and the visual cortex.

The Behavioral and brain sciences
Deep neural networks (DNNs) are not just inadequate models of the visual system but are so different in their structure and functionality that they are not even on the same playing field. DNN units have almost nothing in common with neurons, and, unl...

Memristor-induced hyperchaos, multiscroll and extreme multistability in fractional-order HNN: Image encryption and FPGA implementation.

Neural networks : the official journal of the International Neural Network Society
Fractional-order differentiation (FOD) can record information from the past, present, and future. Compared with integer-order systems, FOD systems have higher complexity and more accurate ability to describe the real world. In this paper, two types o...

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation.

Nature methods
Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convoluti...

Energy controls wave propagation in a neural network with spatial stimuli.

Neural networks : the official journal of the International Neural Network Society
Nervous system has distinct anisotropy and some intrinsic biophysical properties enable neurons present various firing modes in neural activities. In presence of realistic electromagnetic fields, non-uniform radiation activates these neurons with ene...

Metamodelling of a two-population spiking neural network.

PLoS computational biology
In computational neuroscience, hypotheses are often formulated as bottom-up mechanistic models of the systems in question, consisting of differential equations that can be numerically integrated forward in time. Candidate models can then be validated...

Beyond spiking networks: The computational advantages of dendritic amplification and input segregation.

Proceedings of the National Academy of Sciences of the United States of America
The brain can efficiently learn a wide range of tasks, motivating the search for biologically inspired learning rules for improving current artificial intelligence technology. Most biological models are composed of point neurons and cannot achieve st...

Enhancing diagnosis of Hirschsprung's disease using deep learning from histological sections of post pull-through specimens: preliminary results.

Pediatric surgery international
PURPOSE: Accurate histological diagnosis in Hirschsprung disease (HD) is challenging, due to its complexity and potential for errors. In this study, we present an artificial intelligence (AI)-based method designed to identify ganglionic cells and hyp...