AIMC Topic: Neurons

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A Novel Adaptive Linear Neuron Based on DNA Strand Displacement Reaction Network.

IEEE/ACM transactions on computational biology and bioinformatics
Analog DNA strand displacement circuits can be used to build artificial neural network due to the continuity of dynamic behavior. In this study, DNA implementations of novel catalysis, novel degradation and adjustment reaction modules are designed an...

Lag Synchronization of Noisy and Nonnoisy Multiple Neurobiological Coupled FitzHugh-Nagumo Networks with and without Delayed Coupling.

Computational intelligence and neuroscience
This paper presents a methodology for synchronizing noisy and nonnoisy multiple coupled neurobiological FitzHugh-Nagumo (FHN) drive and slave neural networks with and without delayed coupling, under external electrical stimulation (EES), external dis...

Printed synaptic transistor-based electronic skin for robots to feel and learn.

Science robotics
An electronic skin (e-skin) for the next generation of robots is expected to have biological skin-like multimodal sensing, signal encoding, and preprocessing. To this end, it is imperative to have high-quality, uniformly responding electronic devices...

The geometry of robustness in spiking neural networks.

eLife
Neural systems are remarkably robust against various perturbations, a phenomenon that still requires a clear explanation. Here, we graphically illustrate how neural networks can become robust. We study spiking networks that generate low-dimensional r...

Emergence of associative learning in a neuromorphic inference network.

Journal of neural engineering
. In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of...

Programming Molecular Systems To Emulate a Learning Spiking Neuron.

ACS synthetic biology
Hebbian theory seeks to explain how the neurons in the brain adapt to stimuli to enable learning. An interesting feature of Hebbian learning is that it is an unsupervised method and, as such, does not require feedback, making it suitable in contexts ...

Evolving Connections in Group of Neurons for Robust Learning.

IEEE transactions on cybernetics
Artificial neural networks inspired from the learning mechanism of the brain have achieved great successes in machine learning, especially those with deep layers. The commonly used neural networks follow the hierarchical multilayer architecture with ...

Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models.

IEEE transactions on cybernetics
Fuzzy-rough cognitive networks (FRCNs) are recurrent neural networks (RNNs) intended for structured classification purposes in which the problem is described by an explicit set of features. The advantage of this granular neural system relies on its t...

Phase-locking patterns underlying effective communication in exact firing rate models of neural networks.

PLoS computational biology
Macroscopic oscillations in the brain have been observed to be involved in many cognitive tasks but their role is not completely understood. One of the suggested functions of the oscillations is to dynamically modulate communication between neural ci...

Machine learning sequence prioritization for cell type-specific enhancer design.

eLife
Recent discoveries of extreme cellular diversity in the brain warrant rapid development of technologies to access specific cell populations within heterogeneous tissue. Available approaches for engineering-targeted technologies for new neuron subtype...