Semanlink - Computational Neuroscience

> an overview of the material covered in a course taught at Stanford in the spring quarter of 2018. The course draws upon **insight from cognitive and systems neuroscience to implement hybrid connectionist and symbolic reasoning systems** that leverage and extend the state of the art in machine learning **by integrating human and machine intelligence**. As a concrete example we focus on digital assistants that learn from continuous dialog with an expert software engineer while providing initial value as powerful analytical, computational and mathematical savants.

> [#Dehaene](/tag/stanislas_dehaene)'s work extends the [#Global Workspace Theory](/tag/global_workspace_theory) of Bernard Baars. Dehaene’s version of the theory combined with Yoshua Bengio’s concept of a [#consciousness prior](/tag/consciousness_prior.html) and deep reinforcement learning suggest a model for constructing and maintaining the cognitive states that arise and persist during complex problem solving.

2019-11-12 A propos

Information theory and neural coding (1999) (Alexander Borst and Frédéric E. Theunissen)

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2019-10-11 A propos

The intertwined quest for understanding biological intelligence and creating artificial intelligence

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2018-12-29 A propos

Emergence of Binocular Disparity Selectivity through Hebbian Learning | Journal of Neuroscience

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2018-12-06 A propos

Des modèles informatiques pour expliquer le développement de la vision (Biologie au CNRS)

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2018-12-06 A propos

Solving the Neural Code Conundrum: Digital or Analog? - MIT Technology Review

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2017-12-04 A propos

Facial Recognition: Cracking the Brain’s Code | CNRS News

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2017-09-07 A propos

Computational Neuroscience: Why does the brain adopt mechanisms like sparse coding to represent the environment? - Quora

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2017-06-19 A propos

The Code for Facial Identity in the Primate Brain

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Computational Neuroscience

2017-06-05 A propos

Neuroscientists rethink how the brain recognizes faces : Nature News & Comment

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2017-06-05 A propos

How Mimicking Brain Function is Revolutionising NLP - Dataconomy

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2017-04-28 A propos

The Clever Machine | Topics in Computational Neuroscience & Machine Learning

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2016-09-17 A propos

23 problems in systems neuroscience

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2016-09-04 A propos

IBM's 'Rodent Brain' Chip Could Make Our Phones Hyper-Smart | WIRED

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2015-08-18 A propos

IEEE Xplore Abstract - Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations

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n this paper, we describe the design of Neurogrid, a neuromorphic system for simulating large-scale neural models in real time. Neuromorphic systems realize the function of biological neural systems by emulating their structure. Designers of such systems face three major design choices: 1) whether to emulate the four neural elements—axonal arbor, synapse, dendritic tree, and soma—with dedicated or shared electronic circuits; 2) whether to implement these electronic circuits in an analog or digital manner; and 3) whether to interconnect arrays of these silicon neurons with a mesh or a tree network. The choices we made were: 1) we emulated all neural elements except the soma with shared electronic circuits; this choice maximized the number of synaptic connections; 2) we realized all electronic circuits except those for axonal arbors in an analog manner; this choice maximized energy efficiency; and 3) we interconnected neural arrays in a tree network; this choice maximized throughput. These three choices made it possible to simulate a million neurons with billions of synaptic connections in real time—for the first time—using 16 Neurocores integrated on a board that consumes three watts.

2014-04-30 A propos

Stanford bioengineers create circuit board modeled on the human brain | Stanford News Release

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2014-04-30 A propos

YaleNews | Yale researchers reconstruct facial images locked in a viewer’s mind

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