Sentence examples for neuron computation from inspiring English sources

Single neuron computation is more complex than what can be shown with a single compartment model.

At the level of single neuron computations such property can provide an active "spike source" compensating "spike dissipation" due to the integrate-and-fire N to 1 response.

This property is discussed as a significant step toward the solution of a fundamental problem in neuroscience: how is neuronal computation in separate neurons and remote brain areas unified as an instance of experience in consciousness?

These oscillations are produced by large ensembles of neurons oscillating in synchrony and are considered to be important for neuronal computation responsible for e.g. perception, memory, and attention [1] [4].

We show that such a learning-by-example system is well-suited to power efficient neuron-based computation (60 W for all quarter of a million neurons), that it can learn quickly (a few tens of seconds), and that its learning generalizes well to novel situations.

(Theories of consciousness based on emergence from complex neuron-based computation have yet to produce any testable predictions, much less validations).

The location of an inverse model in M1 appeals to evidence that M1 neurons perform computations that are compatible with the outputs of an optimal controller or inverse model; for example, some M1 neurons have been shown to integrate multi-joint torque information (Pruszynski et al. 2011).

The neuron model's computations are performed in arithmetic pipelines.

This suggests that passive neurons are better suited to rate-based computation, while neurons with subthreshold oscillations are advantageous for information processing in a temporal coding scheme.

This suggests that passive neurons are better suited to rate-based computation, while neurons with subthreshold oscillations are advantageous in a temporal coding scheme.

Furthermore, since the learning algorithm runs in a massively parallel environment (i.e., general-purpose GPU), the approach can benefit a large number of computation units (neurons) compared to when CPU is used.