Neural networks : the official journal of the International Neural Network Society
Nov 27, 2018
We observe and study a self-organized phenomenon whereby the activity in a network of spiking neurons spontaneously terminates. We consider different types of populations, consisting of bistable model neurons connected electrically by gap junctions, ...
Task-related functional magnetic resonance imaging (fMRI) is a widely-used tool for studying the neural processing correlates of human behavior in both healthy and clinical populations. There is growing interest in mapping individual differences in f...
Medical & biological engineering & computing
Nov 23, 2018
The aim of medical image fusion is to improve the clinical diagnosis accuracy, so the fused image is generated by preserving salient features and details of the source images. This paper designs a novel fusion scheme for CT and MRI medical images bas...
Threshold-linear networks (TLNs) are models of neural networks that consist of simple, perceptron-like neurons and exhibit nonlinear dynamics determined by the network's connectivity. The fixed points of a TLN, including both stable and unstable equi...
In this work, we address the neuronal encoding problem from a Bayesian perspective. Specifically, we ask whether neuronal responses in an in vitro neuronal network are consistent with ideal Bayesian observer responses under the free energy principle....
Scale-invariant timing has been observed in a wide range of behavioral experiments. The firing properties of recently described time cells provide a possible neural substrate for scale-invariant behavior. Earlier neural circuit models do not produce ...
We present Modular Memory Units (MMUs), a new class of memory-augmented neural network. MMU builds on the gated neural architecture of Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTMs), to incorporate an external memory block, similar t...
Neurophysiological features like event-related potentials (ERPs) have long been used to identify different cognitive sub-processes that may contribute to task performance. It has however remained unclear whether "classical" ERPs are truly the best re...
Spiking neural P systems (SNP systems) are parallel and non-deterministic models of computation, inspired by the neural system of the brain. A variant of SNP systems known as SNP systems with structural plasticity (SNPSP systems) includes the feature...
Computational intelligence and neuroscience
Nov 1, 2018
A twin-multistate quaternion Hopfield neural network (TMQHNN) is a multistate Hopfield model and can store multilevel information, such as image data. Storage capacity is an important problem of Hopfield neural networks. Jankowski et al. approximated...
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