194:, it is very difficult to reconstruct the visual scene that the owner of the brain is looking at. Like a single Knowledge 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
32:
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studies: a sophisticated decoding algorithm can run for many hours on a computer cluster to reconstruct a 10-minute data piece. On-line algorithms decode (and, importantly, predict) behavioral parameters in real time. Moreover, the subject may receive a feedback about the results of decoding — the so-called closed-loop mode as opposed to the open-loop mode in which the subject does not receive any feedback.
168:
description of motor-cortex encoding of reach direction, and it was also capable to predict new effects. For example, Georgopoulos's population vector accurately described mental rotations made by the monkeys that were trained to translate locations of visual stimuli into spatially shifted locations of reach targets.
277:. 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
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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
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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
311:
Demonstrations of decoding of neuronal ensemble activity can be subdivided into two major classes: off-line decoding and on-line (real time) decoding. In the off-line decoding, investigators apply different algorithms to previously recorded data. Time considerations are usually not an issue in these
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As Hebb predicted, individual neurons in the population can contribute information about different parameters. For example, Miguel
Nicolelis and colleagues reported that individual neurons simultaneously encoded arm position, velocity and hand gripping force when the monkeys performed reaching and
247:
and reciprocal innervation of muscles. (Manjarrez E et al. 2000 Modulation of synaptic transmission from segmental afferents by spontaneous activity of dorsal horn spinal neurones in the cat. J Physiol. 529 Pt 2(Pt 2):445-60. doi: 10.1111/j.1469-7793.2000.00445.x) (Manjarrez E et al. 2002 Cortical
299:
Miguel
Nicolelis worked with John Chapin, Johan Wessberg, Mark Laubach, Jose Carmena, Mikhail Lebedev and other colleagues showed that activity of large neuronal ensembles can predict arm position. This work made possible creation of brain–machine interfaces – electronic devices that read arm
307:
In addition to the studies by
Nicolelis and Donoghue, the groups of Andrew Schwartz and Richard Andersen are developing decoding algorithms that reconstruct behavioral parameters from neuronal ensemble activity. For example, Andrew Schwartz uses population vector algorithms that he previously
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for individual neurons. In the population vector model, individual neurons 'vote' for their preferred directions using their firing rate. The final vote is calculated by vectorial summation of individual preferred directions weighted by neuronal rates. This model proved to be successful in
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Wessberg, Johan; Nicolelis, Miguel A. L.; Stambaugh, Christopher R.; Kralik, Jerald D.; Beck, Pamela D.; Laubach, Mark; Chapin, John K.; Kim, Jung; Biggs, S. James; Srinivasan, Mandayam A. (2000-11-16). "Real-time prediction of hand trajectory by ensembles of cortical neurons in primates".
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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
152:, capable of acting briefly as a closed system, delivering facilitation to other such systems". Hebb suggested that, depending on functional requirements, individual brain cells could participate in different cell assemblies and be involved in multiple computations.
331:
Luis
Carrillo-Reid and colleagues has demonstrated that external activation of as few as two neurons in an ensemble could trigger resonant activation of a whole ensemble and cause the ensemble-related behavioral response in the absence of a sensory stimulus.
296:. Along with colleagues Hatsopoulos, Paninski, Fellows and Serruya, they first showed that neuronal ensembles could be used to control external interfaces by having a monkey control a cursor on a computer screen with its mind (2002).
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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).
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movement intentions and translate them into movements of artificial actuators. Carmena et al. (2003) programmed the neural coding in a brain–machine interface allowed a monkey to control reaching and grasping movements by a
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neuronal ensembles driven by dorsal horn spinal neurones with spontaneous activity in the cat. Neurosci Lett. 318(3):145-8. doi: 10.1016/s0304-3940(01)02497-1). These include both excitatory and inhibitory neurons.
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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
144:
theoretically developed the concept of neuronal ensemble in his famous book "The
Organization of Behavior" (1949). He defined "cell assembly" as "a diffuse structure comprising cells in the
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arm, and
Lebedev et al. (2005) argued that brain networks reorganize to create a new representation of the robotic appendage in addition to the representation of the animal's own limbs.
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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
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neurons encode movement direction. This hypothesis was based on the observation that individual neurons tended to discharge more for movements in particular directions, the so-called
232:
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
218:
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
779:"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"
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quality increased with the number of neurons—initially very notably, but then substantially larger neuronal quantities were needed to improve the read-out.
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Laubach, M; Wessberg, J; Nicolelis, MA (2000). "Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task".
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that reside in the spinal cord are more complex ensembles for coordination of limb movements during locomotion. Neuronal ensembles of the higher
740:
Georgopoulos, AP; Lurito, JT; Petrides, M; Schwartz, AB; Massey, JT (1989). "Mental rotation of the neuronal population vector".
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Carmena, JM; Lebedev, MA; Crist, RE; O'Doherty, JE; Santucci, DM; Dimitrov, DF; Patil, PG; Henriquez, CS; Nicolelis, MA (2003).
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have discovered that individual neurons are very noisy. For example, by examining the activity of only a single neuron in the
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In the 1980s, Apostolos
Georgopoulos and his colleagues Ron Kettner, Andrew Schwartz, and Kenneth Johnson formulated a
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496:"Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain–Machine Interface"
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211:. However, the basic principle of ensemble encoding holds: large neuronal populations do better than single neurons.
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236:(Gray, Singer and others). In addition, sleep stages and waking are associated with distinct oscillatory patterns.
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neurons that simultaneously encoded spatial locations that the monkeys attended to and those that they stored in
324:. Both attended and remembered locations could be decoded when these neurons were considered as population.
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structures is not completely understood, despite the vast literature on the neuroanatomy of these regions.
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226:, on the contrary, states that precise timing of neuronal spikes is an important encoding mechanism.
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Carrillo-Reid, Luis; Han, Shuting; Yang, Weijian; Akrouh, Alejandro; Yuste, Rafael (June 2019).
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After the techniques of multielectrode recordings were introduced, the task of real-time
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130:. In Sherrington's scheme, α-motoneurons are the final common path of a number of
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Lebedev, Mikhail A.; Messinger, Adam; Kralik, Jerald D.; Wise, Steven P. (2004).
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grasping movements. Mikhail Lebedev, Steven Wise and their colleagues reported
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Population of nervous system cells involved in a particular neural computation
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Nicolelis MA, Ribeiro S (2002). "Multielectrode recordings: the next steps".
553:"Representation of Attended Versus Remembered Locations in Prefrontal Cortex"
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to facilitate commercialization of brain-machine interfaces. They bought the
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822:"Making complexity simpler: Multivariability and metastability in the brain"
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integrate a large number of inputs and send their final output to muscles.
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16:"Neural population" redirects here. For the anatomical cell group, see
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880:"Functional connectivity in the brain – is it an elusive concept?"
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432:"Functional connectivity in the brain – is it an elusive concept?"
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Fingelkurts, An.A.; Fingelkurts, Al.A.; Kähkönen, S.A. (2005).
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Fingelkurts An.A., Fingelkurts Al.A., Kähkönen S.A. (2005).
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The concept of neuronal ensemble dates back to the work of
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where they control basic automatisms such as monosynaptic
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273:, several lab groups used millions of dollars to make
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Georgopoulos, AP; Kettner, RE; Schwartz, AB (1988).
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introduction of an enormous Neuroscience boost from
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Relatively simple neuronal ensembles operate in the
186:operation – multiple edits by many participants.
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106:) involved in a particular neural computation.
659:The Integrative Action of the Nervous System.
366:Fingelkurts An.A., Fingelkurts Al.A. (2004).
479:: CS1 maint: multiple names: authors list (
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887:Neuroscience & Biobehavioral Reviews
439:Neuroscience & Biobehavioral Reviews
39:This article includes a list of general
682:Methods for Neural Ensemble Recordings.
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308:developed with Apostolos Georgopoulos.
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118:who described the functioning of the
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45:it lacks sufficient corresponding
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494:Lebedev, M. A. (2005-05-11).
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250:Central pattern generators
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134:of different complexity:
275:brain–machine interfaces
224:temporal encoding theory
762:10.1126/science.2911737
500:Journal of Neuroscience
60:more precise citations.
18:Nucleus (neuroanatomy)
230:Neuronal oscillations
209:signal-to-noise ratio
220:rate encoding theory
165:preferred directions
1019:2000Natur.408..361W
969:Curr Opin Neurobiol
936:2000Natur.405..567L
754:1989Sci...243..234G
116:Charles Sherrington
92:is a population of
294:Richard A. Normann
260:Real-time decoding
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322:short-term memory
318:prefrontal cortex
204:grandmother cells
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870:2005-11-13
783:J Neurosci
707:(2): E42.
684:CRC Press.
416:2005-11-13
353:References
290:Utah array
110:Background
66:April 2012
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336:See also
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172:Encoding
101:cultured
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960:4341812
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