AIMC Topic: Neurons

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A Prediction Model Based on Gated Nonlinear Spiking Neural Systems.

International journal of neural systems
Nonlinear spiking neural P (NSNP) systems are one of neural-like membrane computing models, abstracted by nonlinear spiking mechanisms of biological neurons. NSNP systems have a nonlinear structure and can show rich nonlinear dynamics. In this paper,...

Stereology neuron counts correlate with deep learning estimates in the human hippocampal subregions.

Scientific reports
Hippocampal subregions differ in specialization and vulnerability to cell death. Neuron death and hippocampal atrophy have been a marker for the progression of Alzheimer's disease. Relatively few studies have examined neuronal loss in the human brain...

Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture.

Scientific reports
The complexity of the cerebral cortex underlies its function and distinguishes us as humans. Here, we present a principled veridical data science methodology for quantitative histology that shifts focus from image-level investigations towards neuron-...

A deep learning network based on CNN and sliding window LSTM for spike sorting.

Computers in biology and medicine
Spike sorting plays an essential role to obtain electrophysiological activity of single neuron in the fields of neural signal decoding. With the development of electrode array, large numbers of spikes are recorded simultaneously, which rises the need...

Fully Complex-Valued Dendritic Neuron Model.

IEEE transactions on neural networks and learning systems
A single dendritic neuron model (DNM) that owns the nonlinear information processing ability of dendrites has been widely used for classification and prediction. Complex-valued neural networks that consist of a number of multiple/deep-layer McCulloch...

Comparing representations and computations in single neurons versus neural networks.

Trends in cognitive sciences
Single-neuron-level explanations have been the gold standard in neuroscience for decades. Recently, however, neural-network-level explanations have become increasingly popular. This increase in popularity is driven by the fact that the analysis of ne...

Neural-Like P Systems With Plasmids and Multiple Channels.

IEEE transactions on nanobioscience
Neural-like P systems with plasmids (NP P systems, in short) are a kind of distributed and parallel computing systems inspired by the activity that bacteria process DNA such as plasmids. An important biological fact is that one or more pili have exis...

Emergence of time persistence in a data-driven neural network model.

eLife
Establishing accurate as well as interpretable models of network activity is an open challenge in systems neuroscience. Here, we infer an energy-based model of the anterior rhombencephalic turning region (ARTR), a circuit that controls zebrafish swim...

Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications.

Advanced materials (Deerfield Beach, Fla.)
Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have...

A study of autoencoders as a feature extraction technique for spike sorting.

PloS one
Spike sorting is the process of grouping spikes of distinct neurons into their respective clusters. Most frequently, this grouping is performed by relying on the similarity of features extracted from spike shapes. In spite of recent developments, cur...