* HCI/BMI = Human-Computer & Brain-Machine Interfaces

 

 

 Computational Neuroscience & Deep Machine Learning

 

 

‘Computational Neuroscience’ and ‘Deep Machine Learning’ (CN/DML) are very exciting interdisciplinary fields of science. These fields of study involve using physics, mathematics, engineering and computational science, to understand how biological nervous systems work, in terms of their structures and functions, at every level of structural organisation (from the molecular/cellular, to networks of neural cells, to whole organ systems). However, these fields of study can also refer to how we can use our understanding of neurobiological systems to give computers (made of silicon-semiconductor-based transistors) the ability to operate more like human brains, especially when it comes to ‘learning’. Mathematical statisticians, computer engineers and software programmers may refer to these fields of study more broadly as ‘data science’, while biological scientists and bioanalysts may refer to them as ‘neuro-cognitive physiology’. Most people may simply refer to these cognitive functions of machines, as ‘General-purpose Artificial Intelligence’ (GAI/AGI). The more science-savvy readers of this will of course debate over the definition of all these terms. However, this debate can be put aside for now, as there is certainly a lot of overlap between all these terms.

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     AGI programs are mainly based on Artificial Neural Networks (ANNs). ANNs are computational and mathematical models inspired by biological neural networks; and they also have a physical implementation. Connectivity and interactions of the neural nodes is important. Nodes (standard artificial neurons) can be organised and connected in such a way, as to encode & store & process data, in particular ways. Certain  ANNs represent algorithms that have the ability to learn and perform certain computations: 

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● The algorithms are not pre-programmed to solve a particular task, they learn how to solve a particular task, just like the human brain.

● The learning is based on forming associations between node A and node B. This is done by increasing (potentiating) or decreasing (depressing) the connection weights (synaptic strengths) between neural nodes.

● Furthermore, the algorithms are not narrowly-defined or domain-specific, they are general-purpose. The same algorithm will perform well across a range of different tasks.

● So in other words, all the rules don’t have to be programmed in manually by the programmer; the ANNs learn to abstract their own rules, and self-modify their own high-level code using learning rules.

● Given enough processing power (by using more layers and nodes; aka. ‘deeper’ neural networks), they do considerably better when it comes to optimization and processing ‘big-data’, compared to conventional methods. They are a massive extension of the algorithms we have had up until the mid-2000s, which were just a combination of human-made computer-based non-learning automata and expert human cognition, but not true AGI.

● ANNs can provide more optimised optimal solutions, and to a wide range of complex problems.

● There is supervised learning and unsupervised learning, and everything in between.

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●●● click: archived eLearning video resources by HM ●●●

 

()https://drive.google.com/drive/folders/0B0qSKFqszohLUkxpbVF3YmJOU0U?usp=sharing

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Here are some strongly recommended links to other sources:

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● http://www.gatsby.ucl.ac.uk/research.html (UCL Computational Neuroscience Unit)

 

ucl wellcome trust centre for neuroimaging

http://www.fil.ion.ucl.ac.uk/Training/

 

www.icn.ucl.ac.uk (Institute of Cognitive Neuroscience - UCL)

 

Sainsbury Wellcome Centre for Neural Circuits and Behaviour

http://www.ucl.ac.uk/swc/research/OKeefe

 

INTELLIGENT SYSTEMS GROUP

DEPARTMENT OF COMPUTER SCIENCE

http://is.cs.ucl.ac.uk/introduction/

 

http://www.engineering.ucl.ac.uk/departments/computer-science/

 

● https://www.ntnu.edu/web/kavli/publications

The Kavli Institute for Systems Neuroscience, Cantre for Neural Computation, is a Kavli Foundation Institute since 2007, a Centre of Excellence since 2002, and a department under the Faculty of Medicine and Health Sciences NTNU.

 

● www.ucl.ac.uk/swc (Sainsbury Wellcome Centre for Neural Circuits and Behaviour - UCL)

 

● http://cs229.stanford.edu/materials.html (Machine Learning info)

 

● https://ml.berkeley.edu/ (UC Berkeley Machine Learning)

 

● www.seas.harvard.edu/courses/cs281/

 

● https://www.coursera.org/learn/computational-neuroscience

 

● https://deepmind.com/research/publications/ (AI company, OA research papers)

 

 

● www.brainnet.net/about/brain-resource-international-database/ (The BRAINnet Database - Brain Research And Integrative Science)

 

● https://crcns.org/ (CRCNS database data-sharing website)

 

 

● https://www.ucl.ac.uk/complex (Centre for Mathematics and Physics in the Life Sciences and EXperimental Biology)

 

● https://www.ethz.ch/.../master-neural-systems-and-computation.html (Neural Systems and Computation | ETH Zurich)

 

 

 

● brain-map.org/ (OA database of gene expression patterns, by Allen Brain Institute)

 

● https://openai.com/about/ (OpenAI work, involvement by Elon Musk)

 

 

● https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/machine_learning.html

 

 

Invasive and non-invasive physical hardware technology and neurological biomedical

 

http://www.neurable.com/

 

http://mashable.com/2017/08/25/neuralink-elon-musk-raised-27-million/#OwYR2m5PXkqY

 

https://www.bloomberg.com/news/features/2017-09-07/brain-computer-interfaces-are-already-here

 

http://bcisociety.org/jobs/

 

https://www.neuralink.com/

 

http://dkengineering.com/neuralace-medical-receives-2-million-investment-led-fusionx-ventures-provide-alternative-opioids-chronic-pain-treatment/

Neuralace Medical Inc

https://www.bloomberg.com/research/stocks/private/snapshot.asp?privcapId=423479285

 

http://ieeexplore.ieee.org/document/6346870/?reload=true

“Brain-Machine Interface control of a robot arm using actor-critic reinforcement learning.”

(2012)

 

https://news.brown.edu/articles/2012/05/braingate2

 

https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-016-0134-9

“Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping” (2016)

 

 

 

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Application to Medicine

 

(TBC)

 

 

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If you want to find out more about companies using computational neuroscience / deep machine learning:

 

AI Companies listings (names, net worth, locations, etc) (Websites)

● http://www.ai-one.com (list of AI companies)

● http://www.datamation.com/applications/top-20-artificial-intelligence-companies.html

● https://angel.co/artificial-intelligence (list of AI companies)

● https://www.theguardian.com/technology/2014/jan/27/google-acquires-uk-artificial-intelligence-startup-deepmind

● https://www.cbinsights.com/blog/top-acquirers-ai-startups-ma-timeline/

● https://www.tractica.com/newsroom/press-releases/artificial-intelligence-technologies-are-quietly-penetrating-a-wide-range-of-enterprise-applications/

 

 

 

https://www.frontiersin.org/journals/computational-neuroscience#research-topics

 

https://www.theverge.com/2017/8/3/16007736/china-us-ai-artificial-intelligence

China and the US are battling to become the world’s first AI superpower

 

https://www.theguardian.com/technology/2017/jan/11/robots-jobs-employees-artificial-intelligence

“Robots will destroy our jobs – and we're not ready for it”

 

https://www.economist.com/news/special-report/21700758-will-smarter-machines-cause-mass-unemployment-automation-and-anxiety

 

https://www.tractica.com/artificial-intelligence/how-will-artificial-intelligence-impact-jobs/

 

https://www.forbes.com/sites/haroldstark/2017/04/28/as-robots-rise-how-artificial-intelligence-will-impact-jobs/#1aa891c97687

 

http://bruegel.org/2017/04/do-we-understand-the-impact-of-artificial-intelligence-on-employment/

 

https://deepmind.com/applied/deepmind-ethics-society/

 

https://techcrunch.com/2017/10/04/deepmind-now-has-an-ai-ethics-research-unit-we-have-a-few-questions-for-it/

 

 

 

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Here are some videos by other people; these videos have been selected as they are excellent when it comes to getting some understanding of basic concepts in this topic:

 

    

 

 

"What is Computational Neuroscience?" by BernsteinCenterFR  

[Aug 2011] [4min10sec]

 

http://youtube.com/watch?v=d5oqIxhTU8I

"What is Computational Neuroscience?" by BernsteinCenterFR

 

 

 

 

"Computational Neuroscience" by IBM Research 

[Jan 2016] [2min06sec]

 

http://youtube.com/watch?v=ic9yNyAu310

"Computational Neuroscience" by IBM Research

 

 

 

 

"Artificial Brain Simulation - Thalamocortical System, 8 Million Neurons - 1.4 Billion Synapses" by Ivan Dimkovic 

[May 2012] [1min42sec]

 

http://youtube.com/watch?v=u28ijlP6L6M

"Artificial Brain Simulation - Thalamocortical System, 8 Million Neurons - 1.4 Billion Synapses" by Ivan Dimkovic

 

 

 

 

"A brain in a supercomputer | Henry Markram" by TED 

[Oct 2009] [16min48sec]

 

http://youtube.com/watch?v=LS3wMC2BpxU

"A brain in a supercomputer | Henry Markram" by TED

 

 

 

 

"Demis Hassabis on Computational Neuroscience" by singularitysummit 

[Feb 2012] [33min19sec]

 

http://youtube.com/watch?v=F5PSyu7booU

"Demis Hassabis on Computational Neuroscience" by singularitysummit

 

 

 

 

" The Cognitive and Computational Neuroscience of Categorization, Novelty-Detection, ..." by GoogleTechTalks  

[Dec 2007] [1hr02min14sec]

 

http://youtube.com/watch?v=2Ei6wFJ9kCc

" The Cognitive and Computational Neuroscience of Categorization, Novelty-Detection, ..." by GoogleTechTalks

 

 

 

 

"Computational Neuroscience with Python & Matlab" by Bradley Monk 

[Apr 2015] [1min56sec]

 

http://youtube.com/watch?v=aGdkaufQekQ

"Computational Neuroscience with Python & Matlab" by Bradley Monk

 

 

 

 

"Classifying Handwritten Digits with TF.Learn - Machine Learning Recipes #7" by Google Developers 

[Aug 2016] [7min00sec]

 

http://youtube.com/watch?v=Gj0iyo265bc

"Classifying Handwritten Digits with TF.Learn - Machine Learning Recipes #7" by Google Developers

 

 

 

 

"Lecture 1 | Machine Learning (Stanford)" by Stanford 

[Jul 2008] [1hr08min39sec]

 

http://youtube.com/watch?v=UzxYlbK2c7E

"Lecture 1 | Machine Learning (Stanford)" by Stanford

 

 

 

 

"Deep Learning - Computerphile" by Computerphile

[Apr 2016] [11min05sec]

 

http://youtube.com/watch?v=l42lr8AlrHk 

"Deep Learning - Computerphile" by Computerphile

 

 

 

 

YouTube Video"A Gentle Introduction To Machine Learning; SciPy 2013 Presentation" by Enthought  

[Jul 2013] [17min32sec]

 

http://youtube.com/watch?v=NOm1zA_Cats

YouTube Video"A Gentle Introduction To Machine Learning; SciPy 2013 Presentation" by Enthought

 

 

 

 

"Neil Burgess: How your brain tells you where you are" by TED 

[Feb 2012] [9min04sec]

 

http://youtube.com/watch?v=Zd71719_G8Y 

"Neil Burgess: How your brain tells you where you are" by TED

 

 

 

 

"Nobel Laureate John O’Keefe, Ph.D. - 'Reverse Engineering the Brain’s Cognitive Map'" by UCI Media 

 [Jul 2015] [1hr01min04sec]

 

http://youtube.com/watch?v=6vTIX7rQh6k

"Nobel Laureate John O’Keefe, Ph.D. - 'Reverse Engineering the Brain’s Cognitive Map'" by UCI Media

 

 

 

 

"Google DeepMind: Ground-breaking AlphaGo masters the game of Go" by DeepMind 

[Jan 2016] [2min47sec] 

 

http://youtube.com/watch?v=SUbqykXVx0A

"Google DeepMind: Ground-breaking AlphaGo masters the game of Go" by DeepMind

 

 

 

 

"The computer that mastered Go" by nature video 

 [Jan 2016] [7min51sec]

 

http://youtube.com/watch?v=g-dKXOlsf98

"The computer that mastered Go" by nature video

 

 

                    

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Excellent interactive ANN simulation

(http://playground.tensorflow.org/)

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Topic

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--- TBC

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Curriculum Sub-Topics

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--- TBC

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References

--- TBC

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Links to External Sources

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--- TBC

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Files

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---TBC

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Visual Media: Images & Diagrams, Graphics, Videos,

Embeds, Interactive Interfaces

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Descriptions here - TBC

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--- https://en.wikipedia.org/wiki/Tractography#/media/File:Tractography_animated_lateral_view.gif
--- https://commons.wikimedia.org/wiki/File:Typical_cnn.png
--- https://en.wikipedia.org/wiki/Long_short-term_memory
--- https://commons.wikimedia.org/wiki/File:Connectome_extraction_procedure.jpg
--- https://commons.wikimedia.org/wiki/File:Double_Connectogram.png
--- https://commons.wikimedia.org/wiki/File:Visualizing_data_mining_results_with_the_Brede_tools_-_Figure_3.jpg

--- "Future Computing: Brain-Based Chips | Henry Markram" by World Economic Forum (Mar 2015)
https://www.youtube.com/watch?v=PCql2DgW5sE

--- "Angus Silver - Workshop on open collaboration in computational neuroscience (2014)" by INCF (Sep 2014)
https://www.youtube.com/watch?v=EAPHtzu2s9I

--- "Lecture 10 - Neural Networks" by caltech (May 2012)
https://www.youtube.com/watch?v=Ih5Mr93E-2c

--- "Engineering brain-computer interfaces to regain control of movement | Jaimie Henderson" by World Economic Forum (Feb 2017)
https://www.youtube.com/watch?v=ZpTgdQEJc6I

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