neural ensemble

See Neural ensemble for grandmother neurons which perform this type of function.

Neurons in a neural ensemble rarely all fire at exactly the same moment, i.e. fully synchronized.

Miguel Nicolelis and colleagues demonstrated that the activity of large neural ensembles can predict arm position.

Neurons are locally connected, forming small clusters that are called neural ensembles.

A neural ensemble is a population of nervous system cells (or cultured neurons) involved in a particular neural computation.

Neural ensembles can generate oscillatory activity endogenously through local interactions between excitatory and inhibitory neurons.

If a group of neurons engages in synchronized oscillatory activity, the neural ensemble can be mathematically represented as a single oscillator.

Different neural ensembles are coupled through long-range connections and form a network of weakly coupled oscillators at the next spatial scale.

Such neural ensembles are said to reduce the variability in output produced by single electrodes, which could make it difficult to operate a BCI.

In addition to local synchronization, oscillatory activity of distant neural structures (single neurons or neural ensembles) can synchronize.

In large-scale oscillations, amplitude changes are considered to result from changes in synchronization within a neural ensemble, also referred to as local synchronization.

Donald Hebb theoretically developed the concept of neural ensemble in his famous book "The Organization of Behavior" (1949).

Different configurations must have been caused by different active neural ensembles, and thus different microstates assumedly implement different functions."

Nicolelis MAL, ed (1999) Methods for Neural Ensemble Recordings.

Several groups have been able to capture complex brain motor cortex signals by recording from neural ensembles (groups of neurons) and using these to control external devices.

Neuronal tuning can be strong and sharp, as observed in primary visual cortex (area V1), or weak and broad, as observed in neural ensembles.

At the level of neural ensembles, synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in the electroencephalogram (EEG).

Neural ensembles of the higher brain structures such as the cerebral cortex, basal ganglia and cerebellum are not completely understood, despite the vast literature on the neuroanatomy of these regions.

Investigators of neural ensembles and those who especially support the theory of distributed processing, propose that large neuronal populations effectively decrease noise by averaging out the noise in individual neurons.

It captures the activity of a local system (e.g., a single neuron or neural ensemble) by its circular phase alone and hence ignores the amplitude of oscillations (amplitude is constant).

In particular, inhibitory interneurons play an important role in producing neural ensemble synchrony by generating a narrow window for effective excitation and rhythmically modulating the firing rate of excitatory neurons.

This effort bridges scales and will help determine how significant functions are encoded robustly in neural ensembles, and how those functions can nevertheless depend in specific ways on the detailed biophysics of particular component physiology.

Along with colleagues Hatsopoulos, Paninski, Fellows and Serruya, they first showed that neural ensembles could be used to control external interfaces by having a monkey control a cursor on a computer screen with its mind (2002).

The concept of neural ensemble dates back to the work of Charles Sherrington who described the functioning of the CNS as the system of reflex arcs, each composed of interconnected excitatory and inhibitory neurons.

Miguel Nicolelis worked with John Chapin, Johan Wessberg, Mark Laubach, Jose Carmena, Mikhail Lebedev, Antonio Pereira, Jr., Sidarta Ribeiro and other colleagues showed that activity of large neural ensembles can predict arm position.