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significant element in neural coding. Such codes, that communicate via the time between spikes are also referred to as interpulse interval codes, and have been...

decoding project to construct brain-wide neural codes. Implicit about the decoding hypothesis is the assumption that neural spiking in the brain somehow represents...

polar codes are a linear block error-correcting codes. The code construction is based on a multiple recursive concatenation of a short kernel code which...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

Spiking neural networks (SNNs) are artificial neural networks (ANN) that mimic natural neural networks. These models leverage timing of discrete spikes...

Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory...

The neural correlates of consciousness (NCC) are the minimal set of neuronal events and mechanisms sufficient for the occurrence of the mental states to...

Coding theory is the study of the properties of codes and their respective fitness for specific applications. Codes are used for data compression, cryptography...

A neural circuit is a population of neurons interconnected by synapses to carry out a specific function when activated. Multiple neural circuits interconnect...

sensory system formed a neural code for efficiently representing sensory information. By efficient it is understood that the code minimized the number of...

many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used...

A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep...

(2007). "Sparse distributed memory using rank-order neural codes". IEEE Transactions on Neural Networks. 18 (3): 648–659. CiteSeerX 10.1.1.686.6196....

of highly diverse neural information in the forming of one's cohesive experience. The neural binding hypothesis states that neural signals are paired...

unary codes can be read and instantaneously decoded in either direction: For unary values where the maximum is known, one can use canonical unary codes that...

Recurrent neural networks (RNNs) are a class of artificial neural networks designed for processing sequential data, such as text, speech, and time series...

of nervous system cells (or cultured neurons) involved in a particular neural computation. The concept of neuronal ensemble dates back to the work of...

is a subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation...

replace, or enhance neural systems. Neural engineers are uniquely qualified to solve design problems at the interface of living neural tissue and non-living...

minimum entropy codes. Neural Computation, 1:412-423, 1989. Jürgen Schmidhuber. Learning factorial codes by predictability minimization. Neural Computation...