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Models, Neurological

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A realistic bi-hemispheric model of the cerebellum uncovers the purpose of the abundant granule cells during motor control.

Frontiers in neural circuits
The cerebellar granule cells (GCs) have been proposed to perform lossless, adaptive spatio-temporal coding of incoming sensory/motor information required by downstream cerebellar circuits to support motor learning, motor coordination, and cognition. ...

Eigenspectrum bounds for semirandom matrices with modular and spatial structure for neural networks.

Physical review. E, Statistical, nonlinear, and soft matter physics
The eigenvalue spectrum of the matrix of directed weights defining a neural network model is informative of several stability and dynamical properties of network activity. Existing results for eigenspectra of sparse asymmetric random matrices neglect...

A network model comprising 4 segmental, interconnected ganglia, and its application to simulate multi-legged locomotion in crustaceans.

Journal of computational neuroscience
Inter-segmental coordination is crucial for the locomotion of animals. Arthropods show high variability of leg numbers, from 6 in insects up to 750 legs in millipedes. Despite this fact, the anatomical and functional organization of their nervous sys...

Turn Down That Noise: Synaptic Encoding of Afferent SNR in a Single Spiking Neuron.

IEEE transactions on biomedical circuits and systems
We have added a simplified neuromorphic model of Spike Time Dependent Plasticity (STDP) to the previously described Synapto-dendritic Kernel Adapting Neuron (SKAN), a hardware efficient neuron model capable of learning spatio-temporal spike patterns....

Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment.

Neural networks : the official journal of the International Neural Network Society
The paper presents a methodology for the analysis of functional changes in brain activity across different conditions and different groups of subjects. This analysis is based on the recently proposed NeuCube spiking neural network (SNN) framework and...

Memory dynamics in attractor networks.

Computational intelligence and neuroscience
As can be represented by neurons and their synaptic connections, attractor networks are widely believed to underlie biological memory systems and have been used extensively in recent years to model the storage and retrieval process of memory. In this...

A priori data-driven multi-clustered reservoir generation algorithm for echo state network.

PloS one
Echo state networks (ESNs) with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in r...

Uncovering phase-coupled oscillatory networks in electrophysiological data.

Human brain mapping
Phase consistent neuronal oscillations are ubiquitous in electrophysiological recordings, and they may reflect networks of phase-coupled neuronal populations oscillating at different frequencies. Because neuronal oscillations may reflect rhythmic mod...

Learning Receptive Fields and Quality Lookups for Blind Quality Assessment of Stereoscopic Images.

IEEE transactions on cybernetics
Blind quality assessment of 3D images encounters more new challenges than its 2D counterparts. In this paper, we propose a blind quality assessment for stereoscopic images by learning the characteristics of receptive fields (RFs) from perspective of ...

Comparison of Seven Methods for Boolean Factor Analysis and Their Evaluation by Information Gain.

IEEE transactions on neural networks and learning systems
An usual task in large data set analysis is searching for an appropriate data representation in a space of fewer dimensions. One of the most efficient methods to solve this task is factor analysis. In this paper, we compare seven methods for Boolean ...