Neural ensemble
Encyclopedia
A neural ensemble is a population of nervous system
cells
(or cultured
neurons) involved in a particular neural computation.
as the system of reflex
arcs, each composed of interconnected excitatory and inhibitory neuron
s. 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.
Donald Hebb
theoretically developed the concept of neural ensemble in his famous book "The Organization of Behavior" (1949). He defined "cell assembly" as "a diffuse structure comprising cells in the cortex and diencephalon, 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.
In the 1980s, Apostolos Georgopoulos and his colleagues Ron Kettner, Andrew Schwartz, and Kenneth Johnson 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.
operation - multiple edits by many participants. Neuroscientist
s 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 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 distributed processing scheme, individual neurons may exhibit neuronal noise
, but the population as a whole averages this noise out.
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 cell
s 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.
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).
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.
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.
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.
, and both are involved in towards human trials with their methods.
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).
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. This work made possible creation of brain-machine interfaces - electronic devices that read arm 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 robot
ic 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.
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 developed with Apostolos Georgopoulos.
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 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.
Interestingly, 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 grasping movements. Mikhail Lebedev, Steven Wise and their colleagues reported 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.
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.
Nervous system
The nervous system is an organ system containing a network of specialized cells called neurons that coordinate the actions of an animal and transmit signals between different parts of its body. In most animals the nervous system consists of two parts, central and peripheral. The central nervous...
cells
Cell (biology)
The cell is the basic structural and functional unit of all known living organisms. It is the smallest unit of life that is classified as a living thing, and is often called the building block of life. The Alberts text discusses how the "cellular building blocks" move to shape developing embryos....
(or cultured
Cell culture
Cell culture is the complex process by which cells are grown under controlled conditions. In practice, the term "cell culture" has come to refer to the culturing of cells derived from singlecellular eukaryotes, especially animal cells. However, there are also cultures of plants, fungi and microbes,...
neurons) involved in a particular neural computation.
Background
The concept of neural ensemble dates back to the work of Charles Sherrington who described the functioning of the CNSCentral nervous system
The central nervous system is the part of the nervous system that integrates the information that it receives from, and coordinates the activity of, all parts of the bodies of bilaterian animals—that is, all multicellular animals except sponges and radially symmetric animals such as jellyfish...
as the system of reflex
Reflex
A reflex action, also known as a reflex, is an involuntary and nearly instantaneous movement in response to a stimulus. A true reflex is a behavior which is mediated via the reflex arc; this does not apply to casual uses of the term 'reflex'.-See also:...
arcs, each composed of interconnected excitatory and inhibitory neuron
Neuron
A neuron is an electrically excitable cell that processes and transmits information by electrical and chemical signaling. Chemical signaling occurs via synapses, specialized connections with other cells. Neurons connect to each other to form networks. Neurons are the core components of the nervous...
s. 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.
Donald Hebb
Donald Olding Hebb
Donald Olding Hebb FRS was a Canadian psychologist who was influential in the area of neuropsychology, where he sought to understand how the function of neurons contributed to psychological processes such as learning...
theoretically developed the concept of neural ensemble in his famous book "The Organization of Behavior" (1949). He defined "cell assembly" as "a diffuse structure comprising cells in the cortex and diencephalon, 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.
In the 1980s, Apostolos Georgopoulos and his colleagues Ron Kettner, Andrew Schwartz, and Kenneth Johnson formulated a population vector hypothesis
Population coding
Population coding is a means by which information is coded in a group of neurons. In population coding, each neuron has a distribution of responses over some set of inputs, and the responses of many neurons may be combined to determine some value about the inputs...
to explain how populations of motor cortex
Motor cortex
Motor cortex is a term that describes regions of the cerebral cortex involved in the planning, control, and execution of voluntary motor functions.-Anatomy of the motor cortex :The motor cortex can be divided into four main parts:...
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.
Encoding
Neuronal ensembles encode information in a way somewhat similar to the principle of WikipediaWikipedia
Wikipedia is a free, web-based, collaborative, multilingual encyclopedia project supported by the non-profit Wikimedia Foundation. Its 20 million articles have been written collaboratively by volunteers around the world. Almost all of its articles can be edited by anyone with access to the site,...
operation - multiple edits by many participants. Neuroscientist
Neuroscientist
A neuroscientist is an individual who studies the scientific field of neuroscience or any of its related sub-fields...
s have discovered that individual neurons are very noisy. For example, by examining the activity of only a single neuron in the visual cortex
Visual cortex
The visual cortex of the brain is the part of the cerebral cortex responsible for processing visual information. It is located in the occipital lobe, in the back of the brain....
, it is very difficult to reconstruct the visual scene that the owner of the brain is looking at. 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 distributed processing scheme, individual neurons may exhibit neuronal noise
Neuronal noise
Neuronal noise is the term that describes random activity of neurons that presumably is not associated with encoding of behaviorally relevant variables. Many neuroscientists consider neuronal noise a factor that limits the capacity of information processing by the brain...
, but the population as a whole averages this noise out.
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 cell
Grandmother cell
The grandmother cell is a hypothetical neuron that represents a complex but specific concept or object. It activates when a person's brain "sees, hears, or otherwise sensibly discriminates" a specific entity, such as his or her grandmother. The term was coined around 1969 by Jerry Lettvin...
s 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.
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).
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.
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
Neural binding
According to the neural binding hypothesis, neurons within neuronal assemblies fire in synchrony to link different features of neuronal representations together. These features can include, shape, motion, color, depth, and other aspects of perception. Neural oscillations have been suggested as the...
(Gray, Singer and others). In additions, sleep stages and waking are associated with distinct oscillatory patterns.
Location and function
Relatively simple neuronal ensembles operate in the spinal cordSpinal cord
The spinal cord is a long, thin, tubular bundle of nervous tissue and support cells that extends from the brain . The brain and spinal cord together make up the central nervous system...
where they control basic automatisms such as monosynaptic tendon reflex
Stretch reflex
The stretch reflex is a muscle contraction in response to stretching within the muscle. It is a monosynaptic reflex which provides automatic regulation of skeletal muscle length....
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
Brain
The brain is the center of the nervous system in all vertebrate and most invertebrate animals—only a few primitive invertebrates such as sponges, jellyfish, sea squirts and starfishes do not have one. It is located in the head, usually close to primary sensory apparatus such as vision, hearing,...
structures such as the cerebral cortex
Cerebral cortex
The cerebral cortex is a sheet of neural tissue that is outermost to the cerebrum of the mammalian brain. It plays a key role in memory, attention, perceptual awareness, thought, language, and consciousness. It is constituted of up to six horizontal layers, each of which has a different...
, basal ganglia
Basal ganglia
The basal ganglia are a group of nuclei of varied origin in the brains of vertebrates that act as a cohesive functional unit. They are situated at the base of the forebrain and are strongly connected with the cerebral cortex, thalamus and other brain areas...
and cerebellum
Cerebellum
The cerebellum is a region of the brain that plays an important role in motor control. It may also be involved in some cognitive functions such as attention and language, and in regulating fear and pleasure responses, but its movement-related functions are the most solidly established...
are not completely understood, despite the vast literature on the neuroanatomy of these regions.
Real-time decoding
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 NicolelisMiguel Nicolelis
Miguel Angelo Laporta Nicolelis, MD, PhD, is a Brazilian physician and scientist, best known for his pioneering work in "reading monkey thought". He and his colleagues implanted electrode arrays into a monkey's brain that were able to detect the monkey's motor intent and thus able to control...
, and both are involved in towards human trials with their methods.
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).
Miguel Nicolelis worked with John Chapin, Johan Wessberg, Mark Laubach, Jose Carmena, Mikhail Lebedev
Mikhail Lebedev
Mikhail Ivanovich Lebedev was a Russian painter.Lebedev was born in Tartu into the family of an impoverished serf. In the 1820s, serfdom was abolished in his region and the young Lebedev got an opportunity to study at a nearby school...
, Antonio Pereira, Jr., Sidarta Ribeiro and other colleagues showed that activity of large neural ensembles can predict arm position. This work made possible creation of brain-machine interfaces - electronic devices that read arm 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 robot
Robot
A robot is a mechanical or virtual intelligent agent that can perform tasks automatically or with guidance, typically by remote control. In practice a robot is usually an electro-mechanical machine that is guided by computer and electronic programming. Robots can be autonomous, semi-autonomous or...
ic 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.
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 developed with Apostolos Georgopoulos.
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 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.
Interestingly, 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 grasping movements. Mikhail Lebedev, Steven Wise and their colleagues reported prefrontal cortex
Prefrontal cortex
The prefrontal cortex is the anterior part of the frontal lobes of the brain, lying in front of the motor and premotor areas.This brain region has been implicated in planning complex cognitive behaviors, personality expression, decision making and moderating correct social behavior...
neurons that simultaneously encoded spatial locations that the monkeys attended to and those that they stored in short-term memory
Short-term memory
Short-term memory is the capacity for holding a small amount of information in mind in an active, readily available state for a short period of time. The duration of short-term memory is believed to be in the order of seconds. A commonly cited capacity is 7 ± 2 elements...
. Both attended and remembered locations could be decoded when these neurons were considered as population.
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.