AIMC Topic: Neurons

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Memristor-based analogue computing for brain-inspired sound localization with in situ training.

Nature communications
The human nervous system senses the physical world in an analogue but efficient way. As a crucial ability of the human brain, sound localization is a representative analogue computing task and often employed in virtual auditory systems. Different fro...

Artificial Olfactory Neuron for an In-Sensor Neuromorphic Nose.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
A neuromorphic module of an electronic nose (E-nose) is demonstrated by hybridizing a chemoresistive gas sensor made of a semiconductor metal oxide (SMO) and a single transistor neuron (1T-neuron) made of a metal-oxide-semiconductor field-effect tran...

A Layered Spiking Neural System for Classification Problems.

International journal of neural systems
Biological brains have a natural capacity for resolving certain classification tasks. Studies on biologically plausible spiking neurons, architectures and mechanisms of artificial neural systems that closely match biological observations while giving...

A Photoelectric Spiking Neuron for Visual Depth Perception.

Advanced materials (Deerfield Beach, Fla.)
The biological visual system encodes optical information into spikes and processes them by the neural network, which enables the perception with high throughput of visual processing with ultralow energy budget. This has inspired a wide spectrum of de...

A Neural Network-Based Model for Predicting Saybolt Color of Petroleum Products.

Sensors (Basel, Switzerland)
Saybolt color is a standard measurement scale used to determine the quality of petroleum products and the appropriate refinement process. However, the current color measurement methods are mostly laboratory-based, thereby consuming much time and bein...

A framework for preparing a stochastic nonlinear integrate-and-fire model for integrated information theory.

Network (Bristol, England)
This paper presents a framework for spiking neural networks to be prepared for the Integrated Information Theory (IIT) analysis, using a stochastic nonlinear integrate-and-fire model. The model includes the crucial dynamics of the all-or-none law and...

Constructing Accurate and Efficient Deep Spiking Neural Networks With Double-Threshold and Augmented Schemes.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs) are considered as a potential candidate to overcome current challenges, such as the high-power consumption encountered by artificial neural networks (ANNs); however, there is still a gap between them with respect to the...

On the relationship between predictive coding and backpropagation.

PloS one
Artificial neural networks are often interpreted as abstract models of biological neuronal networks, but they are typically trained using the biologically unrealistic backpropagation algorithm and its variants. Predictive coding has been proposed as ...

Neuromorphic behaviour in discontinuous metal films.

Nanoscale horizons
Physical systems that exhibit brain-like behaviour are currently under intense investigation as platforms for neuromorphic computing. We show that discontinuous metal films, comprising irregular flat islands on a substrate and formed using simple eva...

Agreement in Spiking Neural Networks.

Journal of computational biology : a journal of computational molecular cell biology
We study the problem of binary agreement in a spiking neural network (SNN). We show that binary agreement on inputs can be achieved with of auxiliary neurons. Our simulation results suggest that agreement can be achieved in our network in time. We...