AIMC Topic: Neurons

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An astrocyte neuromorphic circuit that influences neuronal phase synchrony.

IEEE transactions on biomedical circuits and systems
Neuromorphic circuits are designed and simulated to emulate the role of astrocytes in phase synchronization of neuronal activity. We emulate, to a first order, the ability of slow inward currents (SICs) evoked by the astrocyte, acting on extrasynapti...

Intrinsic excitability state of local neuronal population modulates signal propagation in feed-forward neural networks.

Chaos (Woodbury, N.Y.)
Reliable signal propagation across distributed brain areas is an essential requirement for cognitive function, and it has been investigated extensively in computational studies where feed-forward network (FFN) is taken as a generic model. But it is s...

A universal concept based on cellular neural networks for ultrafast and flexible solving of differential equations.

IEEE transactions on neural networks and learning systems
This paper develops and validates a comprehensive and universally applicable computational concept for solving nonlinear differential equations (NDEs) through a neurocomputing concept based on cellular neural networks (CNNs). High-precision, stabilit...

Learning feature representations with a cost-relevant sparse autoencoder.

International journal of neural systems
There is an increasing interest in the machine learning community to automatically learn feature representations directly from the (unlabeled) data instead of using hand-designed features. The autoencoder is one method that can be used for this purpo...

Graph degree sequence solely determines the expected hopfield network pattern stability.

Neural computation
We analyze the effect of network topology on the pattern stability of the Hopfield neural network in the case of general graphs. The patterns are randomly selected from a uniform distribution. We start the Hopfield procedure from some pattern v. An e...