Sentence examples for encode input from inspiring English sources

Each bit produced by the bitplane coder is encoded by the binary arithmetic coder MQ, which employs contextual information to adaptively encode input data.

To the best of our knowledge, we are the first to demonstrate the use of oscillations and the sub-threshold frequency shift as a mechanism which provides brain networks with the enhanced ability to encode input patterns.

Type I neurons, so called integrators can encode input strength, whereas Type II neurons, so-called resonators, cannot.

We utilize multiple disorder predictions, predicted B-factors and RSA, evolutionary profiles and selected amino acid indices to encode inputs to the SVM.

A PIM symbol which encodes input bits, where is the number of symbols, is represented by a constant power pulse in the "ON" chip followed by the "OFF" chips.

As an integrated environment including spiking trains encoding, input variable mapping, unsupervised reservoir training and supervised classifier training, the NeuCube consists of a spiking neural network reservoir (SNNr) and a dynamic evolving spiking neural network classifier (deSNN).

Indeed, a neuron model with a multiple time-scale current efficiently encodes input history over several time-scales [55].

In these models (commonly referred to as "state-dependent networks" or "liquid state machines") the distributed synaptic dynamics encodes input history in an analogous way as we discussed in this paper for a single neuron: each input to the network will have an effect that depends upon the previous network history.

The model presented here predicts two possible causes for such a difference in β: (1) There may be a higher variability in the encoding of the proprioceptive information about one's own movement than in the encoding of observed motions and/or (2) the variability in the context-encoding input might be smaller in the first case.

We have shown that this cause is the fact that the action encoding inputs can explain most of the possible variability in the overall input (leading to primitive-encoding areas) but not all of it (leading to goal-specific neurons within these areas).

The theoretical reason for this, discussed in " Goal-Specificity in the SOM and in Monkeys", was found to be that under these conditions, the motion-encoding inputs alone were almost, but not quite, sufficient for an adequate explanation of the distances (and thus similarities) between different inputs.