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Neural ensemble

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Title: Neural ensemble  
Author: World Heritage Encyclopedia
Language: English
Subject: Neural oscillation, Brain–computer interface, Neural coding, Neuronal noise, Computational Neuroscience
Collection: Neural Coding, Neurology
Publisher: World Heritage Encyclopedia

Neural ensemble

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


  • Background 1
  • Encoding 2
  • Location and function 3
  • Real-time decoding 4
  • See also 5
  • References 6


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. In Sherrington's scheme, α-motoneurons are the final common path of a number of neural circuits of different complexity: motoneurons integrate a large number of inputs and send their final output to muscles.

In the 1980s, Apostolos Georgopoulos and his colleagues Ron Kettner, Andrew Schwartz, and Kenneth Johnson formulated a

  • Carmena JM, Lebedev MA, Crist RE, O'Doherty JE, Santucci DM, Dimitrov DF, Patil PG, Henriquez CS, Nicolelis MA (2003) Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 1:E42.
  • Georgopoulos AP, Lurito JT, Petrides M, Schwartz AB, Massey JT (1989) Mental rotation of the neuronal population vector. Science 243: 234-236.
  • Georgopoulos AP, Kettner RE, Schwartz AB. (1988) Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population. J Neurosci. 8: 2928-2937.
  • Fingelkurts An.A., Fingelkurts Al.A. (2004) Making complexity simpler: Multivariability and metastability in the brain // International Journal of Neuroscience. 114(7): 843-862. Url:
  • Fingelkurts An.A., Fingelkurts Al.A., Kähkönen S.A. (2005) Functional connectivity in the brain – is it an elusive concept? // Neuroscience & Biobehavioral Reviews. 28(8): 827-836. Url:
  • Laubach M, Wessberg J, Nicolelis MA (2000) Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task. Nature 405: 567-571.
  • Lebedev MA, Carmena JM, O'Doherty JE, Zacksenhouse M, Henriquez CS, Principe JC, Nicolelis MA (2005) Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. J Neurosci. 25: 4681-4693.
  • Nicolelis MA, Ribeiro S (2002) Multielectrode recordings: the next steps. Curr Opin Neurobiol. 12: 602-606.
  • Wessberg J, Stambaugh CR, Kralik JD, Beck PD, Laubach M, Chapin JK, Kim J, Biggs SJ, Srinivasan MA, Nicolelis MA (2000) Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408: 361-365.

Journal Articles

  • Sherrington CS (1906) The Integrative Action of the Nervous System. New York: Charles Scribner's Sons.
  • Hebb DO (1949). The Organization of Behavior. New York: Wiley and Sons.
  • Nicolelis MAL, ed (1999) Methods for Neural Ensemble Recordings. CRC Press.


  1. ^ Fingelkurts An.A., Fingelkurts Al.A. (2004) Making complexity simpler: Multivariability and metastability in the brain // International Journal of Neuroscience. 114(7): 843-862. Url: Fingelkurts An.A., Fingelkurts Al.A., Kähkönen S.A. (2005) Functional connectivity in the brain – is it an elusive concept? // Neuroscience & Biobehavioral Reviews. 28(8): 827-836. Url:


See also

To address the question of how many neurons are needed to obtain an accurate read-out from the population activity, Mark Laubach in Nicolelis lab used neuron-dropping analysis. In this analysis, he measured neuronal read-out quality as a function of the number of neurons in the population. Read-out quality increased with the number of neurons—initially very notably, but then substantially larger neuronal quantities were needed to improve the read-out.

Miguel Nicolelis worked with John Chapin, Johan Wessberg, Mark Laubach, Jose Carmena, Mikhail Lebedev and other colleagues showed that activity of large neural ensembles can predict arm position. This work made possible creation of prefrontal cortex neurons that simultaneously encoded spatial locations that the monkeys attended to and those that they stored in short-term memory. Both attended and remembered locations could be decoded when these neurons were considered as population.

John Donoghue formed the company Cyberkinetics to facilitate commercialization of brain-machine interfaces. They bought the Utah array from Richard Normann. 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).

After the techniques of multielectrode recordings were introduced, the task of real-time decoding of information from large neuronal ensembles became feasible. If, as Georgopoulos showed, just a few primary motor neurons could accurately predict hand motion in two planes, reconstruction of the movement of an entire limb should be possible with enough simultaneous recordings. In parallel, with the introduction of an enormous Neuroscience boost from DARPA, several lab groups used millions of dollars to make brain-machine interfaces. Of these groups, two were successful in experiments showing that animals could control external interfaces with models based on their neural activity, and that once control was shifted from the hand to the brain-model, animals could learn to control it better. These two groups are led by John Donoghue and Miguel Nicolelis, and both are involved in towards human trials with their methods.

Real-time decoding

Relatively simple neuronal ensembles operate in the spinal cord where they control basic automatisms such as monosynaptic tendon reflex and reciprocal innervation of muscles. These include both excitatory and inhibitory neurons. Central pattern generations that reside in the spinal cord are more complex ensembles for coordination of limb movements during locomotion. 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.

Location and function

Neuronal oscillations that synchronize activity of the neurons in an ensemble appear to be an important encoding mechanism. For example, oscillations have been suggested to underlie visual feature binding (Gray, Singer and others). In additions, sleep stages and waking are associated with distinct oscillatory patterns.

Neuronal code or the 'language' that neuronal ensembles speak is very far from being understood. Currently, there are two main theories about neuronal code. The rate encoding theory states that individual neurons encode behaviorally significant parameters by their average firing rates, and the precise time of the occurrences of neuronal spikes is not important. The temporal encoding theory, on the contrary, states that precise timing of neuronal spikes is an important encoding mechanism.

The emergence of specific neural assemblies is thought to provide the functional elements of brain activity that execute the basic operations of informational processing (see Fingelkurts An.A. and Fingelkurts Al.A., 2004; 2005).[1]

An alternative to the ensemble hypothesis is the theory that there exist highly specialized neurons that serve as the mechanism of neuronal encoding. In the visual system, such cells are often referred to as grandmother cells because they would respond in very specific circumstances—such as when a person gazes at a photo of their grandmother. Neuroscientists have indeed found that some neurons provide better information than the others, and a population of such expert neurons has an improved signal to noise ratio . However, the basic principle of ensemble encoding holds: large neuronal populations do better than single neurons.

Neuronal ensembles encode information in a way somewhat similar to the principle of WorldHeritage operation - multiple edits by many participants. Neuroscientists have discovered that individual neurons are very noisy. For example, by examining the activity of only a single neuron in the visual cortex, it is very difficult to reconstruct the visual scene that the owner of the brain is looking at. Like a single WorldHeritage participant, an individual neuron does not 'know' everything and is likely to make mistakes. This problem is solved by the brain having billions of neurons. Information processing by the brain is population processing, and it is also distributed - in many cases each neuron knows a little bit about everything, and the more neurons participate in a job, the more precise the information encoding. In the distributed processing scheme, individual neurons may exhibit neuronal noise, but the population as a whole averages this noise out.

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