AIMC Topic: Models, Neurological

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Attention modeled as information in learning multisensory integration.

Neural networks : the official journal of the International Neural Network Society
Top-down cognitive processes affect the way bottom-up cross-sensory stimuli are integrated. In this paper, we therefore extend a successful previous neural network model of learning multisensory integration in the superior colliculus (SC) by top-down...

Developmental time windows for axon growth influence neuronal network topology.

Biological cybernetics
Early brain connectivity development consists of multiple stages: birth of neurons, their migration and the subsequent growth of axons and dendrites. Each stage occurs within a certain period of time depending on types of neurons and cortical layers....

A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition.

IEEE transactions on neural networks and learning systems
This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike...

A reduction for spiking integrate-and-fire network dynamics ranging from homogeneity to synchrony.

Journal of computational neuroscience
In this paper we provide a general methodology for systematically reducing the dynamics of a class of integrate-and-fire networks down to an augmented 4-dimensional system of ordinary-differential-equations. The class of integrate-and-fire networks w...

Spontaneous motion on two-dimensional continuous attractors.

Neural computation
Attractor models are simplified models used to describe the dynamics of firing rate profiles of a pool of neurons. The firing rate profile, or the neuronal activity, is thought to carry information. Continuous attractor neural networks (CANNs) descri...

Deep and shallow architecture of multilayer neural networks.

IEEE transactions on neural networks and learning systems
This paper focuses on the deep and shallow architecture of multilayer neural networks (MNNs). The demonstration of whether or not an MNN can be replaced by another MNN with fewer layers is equivalent to studying the topological conjugacy of its hidde...

Effects of long-term representations on free recall of unrelated words.

Learning & memory (Cold Spring Harbor, N.Y.)
Human memory stores vast amounts of information. Yet recalling this information is often challenging when specific cues are lacking. Here we consider an associative model of retrieval where each recalled item triggers the recall of the next item base...

Online learning and control of attraction basins for the development of sensorimotor control strategies.

Biological cybernetics
Imitation and learning from humans require an adequate sensorimotor controller to learn and encode behaviors. We present the Dynamic Muscle Perception-Action(DM-PerAc) model to control a multiple degrees-of-freedom (DOF) robot arm. In the original Pe...

On the role of astroglial syncytia in self-repairing spiking neural networks.

IEEE transactions on neural networks and learning systems
It has been shown that brain-like self-repair can arise from the interactions between neurons and astrocytes where endocannabinoids are synthesized and released from active neurons. This retrograde messenger feeds back to local synapses directly and ...

Energy-to-peak state estimation for Markov jump RNNs with time-varying delays via nonsynchronous filter with nonstationary mode transitions.

IEEE transactions on neural networks and learning systems
In this paper, the problem of energy-to-peak state estimation for a class of discrete-time Markov jump recurrent neural networks (RNNs) with randomly occurring nonlinearities (RONs) and time-varying delays is investigated. A practical phenomenon of n...