Computational Neuroscience

Start Date: 07/05/2020

Course Type: Common Course

Course Link:

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About Course

This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.

Course Syllabus

This module introduces you to the captivating world of neural information coding. You will learn about the technologies that are used to record brain activity. We will then develop some mathematical formulations that allow us to characterize spikes from neurons as a code, at increasing levels of detail. Finally we investigate variability and noise in the brain, and how our models can accommodate them.

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Course Introduction

Computational Neuroscience Computational Neuroscience is the branch of Neuroscience that studies the mechanisms by which networks are designed to perform tasks at high levels of abstraction. The methods used by computational neurophysiologists focus on the problem of designing a model that maximizes the number of neurons that can be activated at once. The methods employed focus on the problem of ensuring that the model that maximizes the number of neurons that can be activated at the same time does not result in an over- or under-responsiveness of the network. The methods employed focus on the problem of designing a model that maximizes the number of neurons that can be activated at once. Computational neurophysiologists study the fundamental principles behind the behavior of neurons and their representations in abstract and physical ways. The course focuses on the problems that arise from designing a model that maximizes the number of neurons that can be activated at once. Computational neurophysiologists study the fundamental principles behind the behavior of neurons and their representations in abstract and physical ways. The course focuses on the problems that arise from designing a model that maximizes the number of neurons that can be activated at once. The course focuses on the problems that arise from designing a model that maximizes the number of neurons that can be activated at once. The course focuses on the problems that arise from designing a model that maximizes the number of neurons that can be activated at once. In order to maximize the number of participants that

Course Tag

Computational Neuroscience Artificial Neural Network Reinforcement Learning Biological Neuron Model

Related Wiki Topic

Article Example
Computational neuroscience Computational neuroscience (also theoretical neuroscience) studies brain function in terms of the information processing properties of the structures that make up the nervous system. It is an interdisciplinary computational science that links the diverse fields of neuroscience, cognitive science, and psychology with electrical engineering, computer science, mathematics, and physics.
Computational neuroscience The term "computational neuroscience" was introduced by Eric L. Schwartz, who organized a conference, held in 1985 in Carmel, California, at the request of the Systems Development Foundation to provide a summary of the current status of a field which until that point was referred to by a variety of names, such as neural modeling, brain theory and neural networks. The proceedings of this definitional meeting were published in 1990 as the book "Computational Neuroscience". The first open international meeting focused on Computational Neuroscience was organized by James M. Bower and John Miller in San Francisco, California in 1989 and has continued each year since as the annual CNS meeting. The first graduate educational program in computational neuroscience was organized as the Computational and Neural Systems Ph.D. program at the California Institute of Technology in 1985.
Computational neuroscience Research in computational neuroscience can be roughly categorized into several lines of inquiry. Most computational neuroscientists collaborate closely with experimentalists in analyzing novel data and synthesizing new models of biological phenomena.
Cognitive neuroscience Theoretical approaches include computational neuroscience and cognitive psychology.
Integrative neuroscience Integrative neuroscience sculptures a theoretical neuroscience with a mathematical neuroscience that is different from computational neuroscience. In computational neuroscience, reductionist approaches span multiple levels of neural organization. However in integrative neuroscience, each level is seamlessly sculptured as part of a continuum of levels.
Computational neuroscience Computational neuroscience is distinct from psychological connectionism and from learning theories of disciplines such as machine learning, neural networks, and computational learning theory in that it emphasizes descriptions of functional and biologically realistic neurons (and neural systems) and their physiology and dynamics. These models capture the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, proteins, and chemical coupling to network oscillations, columnar and topographic architecture, and learning and memory.
Computational neuroscience It is a field that brings together experts in neuroscience, neurology, psychiatry, decision sciences and computational modeling to quantitatively define and investigate problems in neurological and psychiatric diseases, and to train scientists and clinicians that wish to apply these models to diagnosis and treatment.
Computational neuroscience The brain seems to be able to discriminate and adapt particularly well in certain contexts. For instance, human beings seem to have an enormous capacity for memorizing and recognizing faces. One of the key goals of computational neuroscience is to dissect how biological systems carry out these complex computations efficiently and potentially replicate these processes in building intelligent machines.
Computational neuroscience The early historical roots of the field can be traced to the work of people such as Louis Lapicque, Hodgkin & Huxley, Hubel & Wiesel, and David Marr, to name a few. Lapicque introduced the integrate and fire model of the neuron in a seminal article published in 1907; this model is still one of the most popular models in computational neuroscience for both cellular and neural networks studies, as well as in mathematical neuroscience because of its simplicity (see the recent review article for the centenary of Lapicque's original paper). About 40 years later, Hodgkin & Huxley developed the voltage clamp and created the first biophysical model of the action potential. Hubel & Wiesel discovered that neurons in the primary visual cortex, the first cortical area to process information coming from the retina, have oriented receptive fields and are organized in columns. David Marr's work focused on the interactions between neurons, suggesting computational approaches to the study of how functional groups of neurons within the hippocampus and neocortex interact, store, process, and transmit information. Computational modeling of biophysically realistic neurons and dendrites began with the work of Wilfrid Rall, with the first multicompartmental model using cable theory.
Computational X Computational X is a term used to describe the various fields of study that have emerged from the applications of informatics and big data to specific disciplines. Examples include computational biology, computational neuroscience, computational physics, and computational linguistics.
Computational neuroscience These computational models are used to frame hypotheses that can be directly tested by biological or psychological experiments.
Computational neuroscience The computational functions of complex dendrites are also under intense investigation. There is a large body of literature regarding how different currents interact with geometric properties of neurons.
Liquid state machine Criticisms of LSMs as used in computational neuroscience are that
Bernstein Network In 2009, members of the Bernstein Network have funded a non-profit association, the Bernstein Association Computational Neuroscience, which aims at promoting science, research, and teaching in Computational Neuroscience and the communication of research contents and results to the public. Since March 2014, anyone who is scientifically active in the field of Computational Neuroscience or related subjects may become a member.
Computational neuroscience Even single neurons have complex biophysical characteristics and can perform computations (e.g.). Hodgkin and Huxley's original model only employed two voltage-sensitive currents (Voltage sensitive ion channels are glycoprotein molecules which extend through the lipid bilayer, allowing ions to traverse under certain conditions through the axolemma), the fast-acting sodium and the inward-rectifying potassium. Though successful in predicting the timing and qualitative features of the action potential, it nevertheless failed to predict a number of important features such as adaptation and shunting. Scientists now believe that there are a wide variety of voltage-sensitive currents, and the implications of the differing dynamics, modulations, and sensitivity of these currents is an important topic of computational neuroscience.
Computational neuroscience The Computational Representational Understanding of Mind (CRUM) is another attempt at modeling human cognition through simulated processes like acquired rule-based systems in decision making and the manipulation of visual representations in decision making.
Computational neuroscience One of the major problems in neurophysiological memory is how it is maintained and changed through multiple time scales. Unstable synapses are easy to train but also prone to stochastic disruption. Stable synapses forget less easily, but they are also harder to consolidate. One recent computational hypothesis involves cascades of plasticity that allow synapses to function at multiple time scales. Stereochemically detailed models of the acetylcholine receptor-based synapse with the Monte Carlo method, working at the time scale of microseconds, have been built. It is likely that computational tools will contribute greatly to our understanding of how synapses function and change in relation to external stimulus in the coming decades.
Computational neuroscience The brain's large-scale organizational principles are illuminated by many fields, including biology, psychology, and clinical practice. Integrative neuroscience attempts to consolidate these observations through unified descriptive models and databases of behavioral measures and recordings. These are the bases for some quantitative modeling of large-scale brain activity.
Computational neuroscience One of the ultimate goals of psychology/neuroscience is to be able to explain the everyday experience of conscious life. Francis Crick and Christof Koch made some attempts to formulate a consistent framework for future work in neural correlates of consciousness (NCC), though much of the work in this field remains speculative.
Computational neuroscience Computational modeling of higher cognitive functions has only recently begun. Experimental data comes primarily from single-unit recording in primates. The frontal lobe and parietal lobe function as integrators of information from multiple sensory modalities. There are some tentative ideas regarding how simple mutually inhibitory functional circuits in these areas may carry out biologically relevant computation.