Neural ensemble
From Freepedia
Neural ensemble is a population of brain cells involved in a particular neural computation.
Neuronal ensembles encode information in the way similar to the principle of Wikipedia 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. (However, the existence of neurons specialized to detect specific objects - the so-called grandmother neurons - has been postulated.) Like a single Wikipedia 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 1980s, Apostolos Georgopoulos and his colleagues Richard Kettner and Andrew Schwartz formulated a population vector hypothesis to explain how populations of motor cortex 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 preferred directions 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 description of motor-cortex encoding of reach direction, and it was also capable to predict new effects. For example, Georgopoulos' 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.
After Miguel Nicolelis introduced into Neuroscience the techniques of multielectrode recordings, the task of real-time decoding of information from large neuronal ensembles became feasible. A series of studies that Nicolelis conducted with John Chapin, Johan Wessberg, Mark Laubach, Jose Carmena, Mikhail Lebedev, Sidarta Ribeiro and other colleagues showed that activity of large [[neural ensemble]s is predictive of behavioral state, intended direction of movement and movement parameters. This work made possible creation of brain-machine interfaces - electronic devices that read movement intentions from the brain and translate them into movements of artificial actuators. Carmena et al. (2003) built a brain-machine interface allowed a monkey to control reaching and grasping movements by a robotic 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.
Interestingly, 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. Mikhail Lebedev, Steven Wise and their colleagues reported prefrontal cortex neurons that simultaneously recorded 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.
How many neurons are needed to obtain an accurate read-out from the population activity? To address this question, 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.
What about the role of experts? Are a few experts better than mass action of many contributors? Neuroscientists found that, indeed, some neurons provide better information than the others, and selection of such expert neurons improves signal to noise ratio in neuronal signals. However, the basic principle holds: large neuronal populations do better than single neurons.
See also
References
- 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.
- 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.
