 | This article includes a list of general references, but it lacks sufficient corresponding inline citations .(April 2012) |
A neuronal ensemble is a population of nervous system cells (or cultured neurons) involved in a particular neural computation.
| This article includes a list of general references, but it lacks sufficient corresponding inline citations .(April 2012) |
A neuronal ensemble is a population of nervous system cells (or cultured neurons) involved in a particular neural computation.
The concept of neuronal 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.
Donald Hebb 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 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'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.
Neuronal ensembles encode information in a way somewhat 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. 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 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 [ citation needed ]. 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). [1] [2]
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 addition, sleep stages and waking are associated with distinct oscillatory patterns.
Relatively simple neuronal ensembles operate in the spinal cord where they control basic automatisms such as monosynaptic tendon reflex 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 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. Central pattern generators that reside in the spinal cord are more complex ensembles for coordination of limb movements during locomotion. Neuronal ensembles of the higher brain structures is not completely understood, despite the vast literature on the neuroanatomy of these regions.
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.
John Donoghue formed the company Cyberkinetics to facilitate commercialization of brain-machine interfaces. They bought the Utah array from Richard A. Normann. 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).
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 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 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. [3]
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.
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. [4]
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.
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. [5]
The development of the nervous system, or neural development (neurodevelopment), refers to the processes that generate, shape, and reshape the nervous system of animals, from the earliest stages of embryonic development to adulthood. The field of neural development draws on both neuroscience and developmental biology to describe and provide insight into the cellular and molecular mechanisms by which complex nervous systems develop, from nematodes and fruit flies to mammals.
A brain–computer interface (BCI), sometimes called a brain–machine interface (BMI) or smartbrain, is a direct communication pathway between the brain's electrical activity and an external device, most commonly a computer or robotic limb. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. They are often conceptualized as a human–machine interface that skips the intermediary component of the physical movement of body parts, although they also raise the possibility of the erasure of the discreteness of brain and machine. Implementations of BCIs range from non-invasive and partially invasive to invasive, based on how close electrodes get to brain tissue.
Miguel Ângelo Laporta Nicolelis, is a Brazilian scientist, physician and Duke School of Medicine Professor in Neuroscience at Duke University, best known for his pioneering work surrounding brain-computer interface technology.
A neural circuit is a population of neurons interconnected by synapses to carry out a specific function when activated. Multiple neural circuits interconnect with one another to form large scale brain networks.
Neuroprosthetics is a discipline related to neuroscience and biomedical engineering concerned with developing neural prostheses. They are sometimes contrasted with a brain–computer interface, which connects the brain to a computer rather than a device meant to replace missing biological functionality.
Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons. At the level of neural ensembles, synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in an electroencephalogram. Oscillatory activity in groups of neurons generally arises from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. A well-known example of macroscopic neural oscillations is alpha activity.
In neuroscience, single-unit recordings provide a method of measuring the electro-physiological responses of a single neuron using a microelectrode system. When a neuron generates an action potential, the signal propagates down the neuron as a current which flows in and out of the cell through excitable membrane regions in the soma and axon. A microelectrode is inserted into the brain, where it can record the rate of change in voltage with respect to time. These microelectrodes must be fine-tipped, impedance matching; they are primarily glass micro-pipettes, metal microelectrodes made of platinum, tungsten, iridium or even iridium oxide. Microelectrodes can be carefully placed close to the cell membrane, allowing the ability to record extracellularly.
Radial glial cells, or radial glial progenitor cells (RGPs), are bipolar-shaped progenitor cells that are responsible for producing all of the neurons in the cerebral cortex. RGPs also produce certain lineages of glia, including astrocytes and oligodendrocytes. Their cell bodies (somata) reside in the embryonic ventricular zone, which lies next to the developing ventricular system.
Neural coding is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among the electrical activity of the neurons in the ensemble. Based on the theory that sensory and other information is represented in the brain by networks of neurons, it is thought that neurons can encode both digital and analog information.
Neural cliques are network-level memory coding units in the hippocampus. They are functionally organized in a categorical and hierarchical manner. Researchers investigating the role of neural cliques have gained insight into the process of storing memories in the brain. Research evidence suggests that memory of events is achieved not through memorization of exact event details but through recreation of select images based on cognitive significance. This process enables the brain to exhibit large storage capacity and facilitates the capacity for abstract reasoning and generalization. Although several studies converges in the demonstration that real-time patterns of memory traces and sensory inputs are retained in the form of neural cliques, the topic is currently in active research in order to fully understand this biological code.
The primary gustatory cortex (GC) is a brain structure responsible for the perception of taste. It consists of two substructures: the anterior insula on the insular lobe and the frontal operculum on the inferior frontal gyrus of the frontal lobe. Because of its composition the primary gustatory cortex is sometimes referred to in literature as the AI/FO(Anterior Insula/Frontal Operculum). By using extracellular unit recording techniques, scientists have elucidated that neurons in the AI/FO respond to sweetness, saltiness, bitterness, and sourness, and they code the intensity of the taste stimulus.
Ralph Mitchell Siegel, a researcher who studied the neurological underpinnings of vision, was a professor of neuroscience at Rutgers University, Newark, in the Center for Molecular and Behavioral Neuroscience. He died September 2, 2011, at his home following a long illness.
Neural decoding is a neuroscience field concerned with the hypothetical reconstruction of sensory and other stimuli from information that has already been encoded and represented in the brain by networks of neurons. Reconstruction refers to the ability of the researcher to predict what sensory stimuli the subject is receiving based purely on neuron action potentials. Therefore, the main goal of neural decoding is to characterize how the electrical activity of neurons elicit activity and responses in the brain.
Hippocampal replay is a phenomenon observed in rats, mice, cats, rabbits, songbirds and monkeys. During sleep or awake rest, replay refers to the re-occurrence of a sequence of cell activations that also occurred during activity, but the replay has a much faster time scale. It may be in the same order, or in reverse. Cases were also found where a sequence of activations occurs before the actual activity, but it is still the same sequence. This is called preplay.
A chronic electrode implant is an electronic device implanted chronically into the brain or other electrically excitable tissue. It may record electrical impulses in the brain or may stimulate neurons with electrical impulses from an external source.
The Radial Unit Hypothesis (RUH) is a conceptual theory of cerebral cortex development, first described by Pasko Rakic. The RUH states that the cerebral cortex develops during embryogenesis as an array of interacting cortical columns, or 'radial units', each of which originates from a transient stem cell layer called the ventricular zone, which contains neural stem cells known as radial glial cells.
In neuroscience, a neurometric function is a mathematical formula relating the activity of brain cells to aspects of an animal's sensory experience or motor behavior. Neurometric functions provide a quantitative summary of the neural code of a particular brain region.
The term posterior cortical hot zone was coined by Christof Koch and colleagues to describe the part of the neocortex closely associated with the minimal neural substrate essential for conscious perception. The posterior cortical hot zone includes sensory cortical areas in the parietal, temporal, and occipital lobes. It is the “sensory” cortex, much as the frontal cortex is the “action” cortex.
Jessica Cardin is an American neuroscientist who is an associate professor of neuroscience at Yale University School of Medicine. Cardin's lab studies local circuits within the primary visual cortex to understand how cellular and synaptic interactions flexibly adapt to different behavioral states and contexts to give rise to visual perceptions and drive motivated behaviors. Cardin's lab applies their knowledge of adaptive cortical circuit regulation to probe how circuit dysfunction manifests in disease models.
Brain cells make up the functional tissue of the brain. The rest of the brain tissue is structural or connective called the stroma which includes blood vessels. The two main types of cells in the brain are neurons, also known as nerve cells, and glial cells, also known as neuroglia.
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