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Neuroscience is the scientific study of the nervous system and its functions. It is a multidisciplinary science that combines physiology, anatomy, molecular biology, developmental biology, cytology, psychology, physics, computer science, chemistry, medicine, statistics, and mathematical modeling to understand the fundamental and emergent properties of neurons, glia and neural circuits. The understanding of the biological basis of learning, memory, behavior, perception, and consciousness has been described by Eric Kandel as the "epic challenge" of the biological sciences.
Cognitive neuroscience is the scientific field that is concerned with the study of the biological processes and aspects that underlie cognition, with a specific focus on the neural connections in the brain which are involved in mental processes. It addresses the questions of how cognitive activities are affected or controlled by neural circuits in the brain. Cognitive neuroscience is a branch of both neuroscience and psychology, overlapping with disciplines such as behavioral neuroscience, cognitive psychology, physiological psychology and affective neuroscience. Cognitive neuroscience relies upon theories in cognitive science coupled with evidence from neurobiology, and computational modeling.
Computational neuroscience is a branch of neuroscience which employs mathematical models, computer simulations, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.
Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learning. Bio-inspired computing is a major subset of natural computation.
Neuromorphic engineering, also known as neuromorphic computing, is the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, and transistors. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch, or using canonical learning rules from the biological learning literature, e.g., using BindsNet.
Terrence Joseph Sejnowski is the Francis Crick Professor at the Salk Institute for Biological Studies where he directs the Computational Neurobiology Laboratory and is the director of the Crick-Jacobs center for theoretical and computational biology. He has performed pioneering research in neural networks and computational neuroscience.
A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological neurons, or an artificial neural network, used for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled in artificial neural networks as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1.
An artificial brain is software and hardware with cognitive abilities similar to those of the animal or human brain.
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.
Neuroinformatics is the field that combines informatics and neuroscience. Neuroinformatics is related with neuroscience data and information processing by artificial neural networks. There are three main directions where neuroinformatics has to be applied:
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, high-impedance conductors; 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.
The Blue Brain Project is a Swiss brain research initiative that aims to create a digital reconstruction of the mouse brain. The project was founded in May 2005 by the Brain and Mind Institute of École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. Its mission is to use biologically-detailed digital reconstructions and simulations of the mammalian brain to identify the fundamental principles of brain structure and function.
Spiking neural networks (SNNs) are artificial neural networks 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.
Integrative neuroscience is the study of neuroscience that works to unify functional organization data to better understand complex structures and behaviors. The relationship between structure and function, and how the regions and functions connect to each other. Different parts of the brain carrying out different tasks, interconnecting to come together allowing complex behavior. Integrative neuroscience works to fill gaps in knowledge that can largely be accomplished with data sharing, to create understanding of systems, currently being applied to simulation neuroscience: Computer Modeling of the brain that integrates functional groups together.
The Human Brain Project (HBP) is a large ten-year scientific research project, based on exascale supercomputers, that aims to build a collaborative ICT-based scientific research infrastructure to allow researchers across Europe to advance knowledge in the fields of neuroscience, computing, and brain-related medicine.
Brain simulation is the concept of creating a functioning computer model of a brain or part of a brain. Brain simulation projects intend to contribute to a complete understanding of the brain, and eventually also assist the process of treating and diagnosing brain diseases.
The Dehaene–Changeux model (DCM), also known as the global neuronal workspace or the global cognitive workspace model is a part of Bernard Baars's "global workspace model" for consciousness.
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 following outline is provided as an overview of and topical guide to brain mapping:
Spaun is a cognitive architecture pioneered by Chris Eliasmith of the University of Waterloo Centre for Theoretical Neuroscience. It consists of 2.5 million simulated neurons organized into subsystems that resemble specific brain regions, such as the prefrontal cortex, basal ganglia, and thalamus. It can recognize numbers, remember them, figure out numeric sequences, and even write them down with a robotic arm. It is implemented using Nengo.
References
- ↑ Bekolay T, Bergstra J, Hunsberger E, Dewolf T, Stewart TC, Rasmussen D, Choo X, Voelker AR, Eliasmith C (2014). "Nengo: a Python tool for building large-scale functional brain models". Front Neuroinform. 7: 48. doi: 10.3389/fninf.2013.00048 . PMC 3880998 . PMID 24431999.
- ↑ Friedl, K. E.; Voelker, A. R.; Peer, A.; Eliasmith, C. (1 January 2016). "Human-Inspired Neurorobotic System for Classifying Surface Textures by Touch" (PDF). IEEE Robotics and Automation Letters. 1 (1): 516–523. doi:10.1109/LRA.2016.2517213. ISSN 2377-3766. S2CID 6401430.
- ↑ Nengo History; retrieved 2016-10-28.
- ↑ Nengo GUI source code; retrieved 2016-10-28.
- ↑ Nengo License; retrieved 2016-10-28.
- ↑ Eliasmith, Chris; Anderson, Charles H. (2003). Neural engineering : computation, representation, and dynamics in neurobiological systems. MIT Press. ISBN 9780262550604.
- ↑ Eliasmith 2013
- ↑ Stewart, Terrence C. (2012). A technical overview of the neural engineering framework (PDF) (Technical report). Vol. 110. Centre for Theoretical Neuroscience, University of Waterloo.
- ↑ Nengo FAQ; retrieved 2016-10-28.
- ↑ Kajić, Ivana; Gosmann, Jan; Stewart, Terrence C.; Wennekers, Thomas; Eliasmith, Chris (2016). "Towards a cognitively realistic representation of word associations" (PDF). Proceedings of the 38th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 2183–8. ISBN 978-0-9911967-3-9.
- ↑ Blouw, Peter; Eliasmith, Chris; Tripp, Brian (2016). "A scaleable spiking neural model of action planning". Proceedings of the 38th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 1583–8. ISBN 978-0-9911967-3-9.
- ↑ "Archived copy". Archived from the original on 2018-02-03. Retrieved 2018-02-02.
{{cite web}}: CS1 maint: archived copy as title (link) - ↑ Xuan Choo (2018). Spaun 2.0: Extending the World's Largest Functional Brain Model (PhD thesis). University of Waterloo. hdl:10012/13308.
- ↑ Eliasmith, C.; Stewart, T.C.; Choo, X.; Bekolay, T.; DeWolf, T.; Tang, Y.; Rasmussen, D. (2012). "A large-scale model of the functioning brain". Science. 338 (6111): 1202–5. Bibcode:2012Sci...338.1202E. doi:10.1126/science.1225266. PMID 23197532. S2CID 1673514.