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Synchronization is the coordination of events to operate a system in unison. For example, the conductor of an orchestra keeps the orchestra synchronized or in time. Systems that operate with all parts in synchrony are said to be synchronous or in sync—and those that are not are asynchronous.
Computational neuroscience is a branch of neuroscience which employs mathematics, computer science, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.
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.
Purkinje cells or Purkinje neurons, named for Czech physiologist Jan Evangelista Purkyně who identified them in 1837, are a unique type of prominent large neurons located in the cerebellar cortex of the brain. With their flask-shaped cell bodies, many branching dendrites, and a single long axon, these cells are essential for controlling motor activity. Purkinje cells mainly release GABA neurotransmitter, which inhibits some neurons to reduce nerve impulse transmission. Purkinje cells efficiently control and coordinate the body's motor motions through these inhibitory actions.
Brainwave entrainment, also referred to as brainwave synchronization or neural entrainment, refers to the observation that brainwaves will naturally synchronize to the rhythm of periodic external stimuli, such as flickering lights, speech, music, or tactile stimuli.
In the field of computational neuroscience, the theory of metastability refers to the human brain's ability to integrate several functional parts and to produce neural oscillations in a cooperative and coordinated manner, providing the basis for conscious activity.
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.
Bursting, or burst firing, is an extremely diverse general phenomenon of the activation patterns of neurons in the central nervous system and spinal cord where periods of rapid action potential spiking are followed by quiescent periods much longer than typical inter-spike intervals. Bursting is thought to be important in the operation of robust central pattern generators, the transmission of neural codes, and some neuropathologies such as epilepsy. The study of bursting both directly and in how it takes part in other neural phenomena has been very popular since the beginnings of cellular neuroscience and is closely tied to the fields of neural synchronization, neural coding, plasticity, and attention.
Neural coding is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the neuronal responses, and the relationship among the electrical activities 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 believed that neurons can encode both digital and analog information.
Synaptic gating is the ability of neural circuits to gate inputs by either suppressing or facilitating specific synaptic activity. Selective inhibition of certain synapses has been studied thoroughly, and recent studies have supported the existence of permissively gated synaptic transmission. In general, synaptic gating involves a mechanism of central control over neuronal output. It includes a sort of gatekeeper neuron, which has the ability to influence transmission of information to selected targets independently of the parts of the synapse upon which it exerts its action.
Spiking neural networks (SNNs) are artificial neural networks (ANN) that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle, but rather transmit information only when a membrane potential—an intrinsic quality of the neuron related to its membrane electrical charge—reaches a specific value, called the threshold. When the membrane potential reaches the threshold, the neuron fires, and generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in response to this signal. A neuron model that fires at the moment of threshold crossing is also called a spiking neuron model.
Recurrent thalamo-cortical resonance is an observed phenomenon of oscillatory neural activity between the thalamus and various cortical regions of the brain. It is proposed by Rodolfo Llinas and others as a theory for the integration of sensory information into the whole of perception in the brain. Thalamocortical oscillation is proposed to be a mechanism of synchronization between different cortical regions of the brain, a process known as temporal binding. This is possible through the existence of thalamocortical networks, groupings of thalamic and cortical cells that exhibit oscillatory properties.
Optogenetics is a biological technique to control the activity of neurons or other cell types with light. This is achieved by expression of light-sensitive ion channels, pumps or enzymes specifically in the target cells. On the level of individual cells, light-activated enzymes and transcription factors allow precise control of biochemical signaling pathways. In systems neuroscience, the ability to control the activity of a genetically defined set of neurons has been used to understand their contribution to decision making, learning, fear memory, mating, addiction, feeding, and locomotion. In a first medical application of optogenetic technology, vision was partially restored in a blind patient with Retinitis pigmentosa.
A Bayesian Confidence Propagation Neural Network (BCPNN) is an artificial neural network inspired by Bayes' theorem, which regards neural computation and processing as probabilistic inference. Neural unit activations represent probability ("confidence") in the presence of input features or categories, synaptic weights are based on estimated correlations and the spread of activation corresponds to calculating posterior probabilities. It was originally proposed by Anders Lansner and Örjan Ekeberg at KTH Royal Institute of Technology. This probabilistic neural network model can also be run in generative mode to produce spontaneous activations and temporal sequences.
Calcium encoding (also referred to as Ca2+ encoding or calcium information processing) is an intracellular signaling pathway used by many cells to transfer, process and encode external information detected by the cell. In cell physiology, external information is often converted into intracellular calcium dynamics. The concept of calcium encoding explains how Ca2+ ions act as intracellular messengers, relaying information within cells to regulate their activity. Given the ubiquity of Ca2+ ions in cell physiology, Ca2+ encoding has also been suggested as a potential tool to characterize cell physiology in health and disease. The mathematical bases of Ca2+ encoding have been pioneered by work of Joel Keizer and Hans G. Othmer on calcium modeling in the 1990s and more recently they have been revisited by Eshel Ben-Jacob, Herbert Levine and co-workers.
Network of human nervous system comprises nodes that are connected by links. The connectivity may be viewed anatomically, functionally, or electrophysiologically. These are presented in several Wikipedia articles that include Connectionism, Biological neural network, Artificial neural network, Computational neuroscience, as well as in several books by Ascoli, G. A. (2002), Sterratt, D., Graham, B., Gillies, A., & Willshaw, D. (2011), Gerstner, W., & Kistler, W. (2002), and Rumelhart, J. L., McClelland, J. L., and PDP Research Group (1986) among others. The focus of this article is a comprehensive view of modeling a neural network. Once an approach based on the perspective and connectivity is chosen, the models are developed at microscopic, mesoscopic, or macroscopic (system) levels. Computational modeling refers to models that are developed using computing tools.
The neuroscience of rhythm refers to the various forms of rhythm generated by the central nervous system (CNS). Nerve cells, also known as neurons in the human brain are capable of firing in specific patterns which cause oscillations. The brain possesses many different types of oscillators with different periods. Oscillators are simultaneously outputting frequencies from .02 Hz to 600 Hz. It is now well known that a computer is capable of running thousands of processes with just one high-frequency clock. Humans have many different clocks as a result of evolution. Prior organisms had no need for a fast-responding oscillator. This multi-clock system permits quick response to constantly changing sensory input while still maintaining the autonomic processes that sustain life. This method modulates and controls a great deal of bodily functions.
Percolation is a theoretical model used to understand the way activation and diffusion of neural activity occurs within neural networks. Percolation is a model used to explain how neural activity is transmitted across the various connections within the brain. Percolation theory can be easily understood by explaining its use in epidemiology. Individuals who are infected with a disease can spread the disease through contact with others in their social network. Those who are more social and come into contact with more people will help to propagate the disease quicker than those who are less social. Factors such as occupation and sociability influence the rate of infection. Now, if one were to think of neurons as individuals and synaptic connections as the social bonds between people, then one can determine how easily messages between neurons will spread. When a neuron fires, the message is transmitted along all synaptic connections to other neurons until it can no longer continue. Synaptic connections are considered either open or closed and messages will flow along any and all open connections until they can go no further. Just like occupation and sociability play a key role in the spread of disease, so too do the number of neurons, synaptic plasticity and long-term potentiation when talking about neural percolation.
Phase resetting in neurons is a behavior observed in different biological oscillators and plays a role in creating neural synchronization as well as different processes within the body. Phase resetting in neurons is when the dynamical behavior of an oscillation is shifted. This occurs when a stimulus perturbs the phase within an oscillatory cycle and a change in period occurs. The periods of these oscillations can vary depending on the biological system, with examples such as: (1) neural responses can change within a millisecond to quickly relay information; (2) In cardiac and respiratory changes that occur throughout the day, could be within seconds; (3) circadian rhythms may vary throughout a series of days; (4) rhythms such as hibernation may have periods that are measured in years. This activity pattern of neurons is a phenomenon seen in various neural circuits throughout the body and is seen in single neuron models and within clusters of neurons. Many of these models utilize phase response (resetting) curves where the oscillation of a neuron is perturbed and the effect the perturbation has on the phase cycle of a neuron is measured.
Andreas Karl Engel is a German neuroscientist. He is the director of the Department of Neurophysiology and Pathophysiology at the University Medical Center Hamburg-Eppendorf (UKE).
References
- ↑ "Physicists train the oscillatory neural network to recognize images".
- ↑ Biswas, Dipayan; Pallikkulath, Sooryakiran; Chakravarthy, V. Srinivasa (2021). "A Complex-Valued Oscillatory Neural Network for Storage and Retrieval of Multidimensional Aperiodic Signals". Frontiers in Computational Neuroscience. 15. doi: 10.3389/fncom.2021.551111 . PMC 8181409 . PMID 34108869.
- ↑ Soman, Karthik; Muralidharan, Vignesh; Chakravarthy, V. Srinivasa (2018). "An Oscillatory Neural Autoencoder Based on Frequency Modulation and Multiplexing". Frontiers in Computational Neuroscience. 12: 52. doi: 10.3389/fncom.2018.00052 . PMC 6048285 . PMID 30042669.
- ↑ "A Neural Network Based on Synchronized Pairs of Nano-Oscillators".