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Brain teaser: Physical Principles for Scalable Neural Recording

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Here are some excerpts text from Mark Buchanan's editorial in Nature Physics on the paper by bunch of friends.

(The origianl paper (arXiv) can be found here:

http://arxiv.org/abs/1306.5709)

 

".... Our ability to monitor and record these detailed dynamics remains limited. Although the technology for recording neural activity with wire electrodes has advanced consistently for decades — doubling in resolution every seven years since the 1950s — we can still only observe the dynamics of at most a few hundred neurons on timescales fast enough (milliseconds) to capture their core behaviour. Optical microscopy can get readings on something like 100,000 neurons, but only every second or so. Magnetic resonance imaging allows non-invasive whole-brain recordings on a one-second timescale, but doesn't come close to resolving the activity of single neurons....

.... physicist Adam Marblestone and colleagues have recently tried to look at how some of the current trends in technology might play out in the future (preprint at http://arxiv.org/abs/1306.5709; 2013). One conclusion: recording from every neuron isn't as out of reach as you might think.

Any suitable brain-imaging technology must avoid interference with normal function, both in terms of the power that can be dissipated within the brain, and the alteration of the physical tissue through direct influence. Both limits restrict the scope for traditional electrode technologies, even with the development of ever thinner wires and smaller electrode impedances. Hence, the biggest advances will probably come from radically different technologies....In their article, Marblestone and colleagues quote Freeman Dyson from his book Imagined Worlds, thinking about how a future technology might place enough tiny devices into the brain to record from each and every neuron. “There is”, he noted, “no law of physics that declares such an observational tool to be impossible.” This reality might be closer than we think...."

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Neural Turing Machines

Neural Turing Machines | Biobit: Computational Neuroscience & Biocomputation | Scoop.it

The capabilities of neural networks were extended by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.

  
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arXivblog coverage:

http://bit.ly/1DwqIRD ;

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[An Information-Theoretic Formalism for Multiscale Structure in Complex Systems

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Coverage by science news:

https://www.sciencenews.org/blog/context/there’s-new-way-quantify-structure-and-complexity

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Graph analysis of functional brain networks: practical issues in translational neuroscience

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A million spiking-neuron integrated circuit with a scalable communication network and interface

A million spiking-neuron integrated circuit with a scalable communication network and interface | Biobit: Computational Neuroscience & Biocomputation | Scoop.it

IBM Neuromorphic architecture

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Commentary:

http://www.sciencemag.org/content/345/6197/614.short

 

if behind paywall, you can read pop-sci articles about it here:

http://www.wired.com/2014/08/ibm-unveils-a-brain-like-chip-with-4000-processor-cores/

 

http://www.technologyreview.com/news/529691/ibm-chip-processes-data-similar-to-the-way-your-brain-does/?utm_campaign=socialsync&utm_medium=social-post&utm_source=facebook

 

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Algorithms, games, and evolution

Algorithms, games, and evolution | Biobit: Computational Neuroscience & Biocomputation | Scoop.it

the mathematical description of evolution in the presence of sexual recombination and weak selection is equivalent to a repeated game between genes played according to the multiplicative weight updates algorithm

Nima Dehghani's insight:

If you are behind PNAS paywall, you can read Quanta Magazine's commentary:

http://www.simonsfoundation.org/quanta/20140618-the-game-theory-of-life/

 

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First Movie Of An Entire Brain’s Neuronal Activity — The Physics arXiv Blog

First Movie Of An Entire Brain’s Neuronal Activity — The Physics arXiv Blog | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Neuroscientists have never been able to study the simultaneous activity of neurons in an entire brain. Until now…
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Algorithm Distinguishes Memes from Ordinary Information  — The Physics arXiv Blog

Algorithm Distinguishes Memes from Ordinary Information  — The Physics arXiv Blog | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Memes are the cultural equivalent of genes: units that transfer ideas or practices from one human to another by means of imitation.
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Consciousness as a State of Matter?

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Consciousness as a State of Matter? but why Tegmark based his calculations on an unproved hypothesis?

 

here is the arXiv blog piece on the story..https://medium.com/the-physics-arxiv-blog/5e7ed624986d

 

and some pop-sci simplification: http://www.extremetech.com/extreme/181284-human-consciousness-is-simply-a-state-of-matter-like-a-solid-or-liquid-but-quantum

 

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Testing Turing’s theory of morphogenesis in chemical cells validated

Testing Turing’s theory of morphogenesis in chemical cells validated | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Alan Turing, in “The Chemical Basis of Morphogenesis”, described how, incircular arrays of identical biological cells, diffusion can interact with chemical reactions to generate up to six periodic spatiotemporal chemical structures. Turing proposed that one of these structures, a stationary pattern with a chemically determined wavelength, is responsible for differentiation.  Quantitative experimental results obtained using this artificial cellular system establish the strengths and weaknesses of the Turing model, applicable to biology and materials science alike, and pinpoint which directions are required for improvement.
Nima Dehghani's insight:

For those who do not have access to the full paper, they can check a summary here:

http://www.brandeis.edu/now/2014/march/turingpnas.html

http://machineslikeus.com/news/alan-turings-theory-morphogenesis-finally-validated?utm_source=feedburner&utm_medium=twitter&utm_campaign=Feed%3A+MachinesLikeUs+%28Machines+Like+Us+-+Top+Stories%29

 

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Power laws and self-organized criticality in theory and nature

Power laws and self-organized criticality in theory and nature | Biobit: Computational Neuroscience & Biocomputation | Scoop.it

a comparative overview of distinct modeling
approaches together with a discussion of their potential relevance as
generative models for real-world phenomena...A
special section is devoted to an assessment of power-law scaling in the
neural activity.

Nima Dehghani's insight:

 Dimitrije Marković and Claudius Gros argue that:

"Finally, in spite of the evidence that quite different physical systems exhibit dynamical properties akin to the one observed in various sandpile models, there is no convincing proof that the generative mechanism for power-law scaling, as proposed by SOC, constitutes the true causal explanation. A substantial controversy regarding the interpretation of empirical data still persists, and the resolve of this controversy will, together with novel approaches for experimental setups and data analysis, require measurements with higher resolution.

On a final note, what one actually considers a self-organized process is to a certain extent a question of semantics. It is possible, in many circumstances, to tune a system toward a critical point. There is general agreement that the underlying process can be considered self-organized whenever this tuning process occurs through internal drives on time scales shorter than (or comparable to) the experimental time scale. The tuning of internal parameters may however also result from processes acting on much longer time scales, like, for example, Kauffman’s notion of “life at the edge of criticality”, as a consequence of Darwinian selection. In both cases the dynamical state will never be, for real-world systems, exactly at the critical point, but fluctuating around it, albeit on very long time scales."

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"How the Brain Handles Big Data: Online Algorithms in Neurons". Dmitri Mitya Chklovskii-- HARVARD IACS

"How the Brain Handles Big Data: Online Algorithms in Neurons". Dmitri Mitya Chklovskii-- HARVARD IACS | Biobit: Computational Neuroscience & Biocomputation | Scoop.it

IACS Seminar Title: "How the Brain Handles Big Data: Online Algorithms in Neurons":

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Ulam Lectures 2013. Complexity and the biology of computation (Santa Fe Institute)

In a series of three lectures over three nights September 10-12, 2013 in Santa Fe, SFI’s Stephanie Forrest revealed surprising commonalities between computers and networks and organisms and ecosystems, then described new research that blurs the distinction further. 

 
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1/ Evolving Computer Programs. 

2/ The Complex Science of Cyberdefense: Computer Immunology.

3/ Modeling Computer Networks from Chips to the Internet. 

 

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The Man Who Would Teach Machines to Think (Douglas Hofstadter)

The Man Who Would Teach Machines to Think (Douglas Hofstadter) | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Douglas Hofstadter, the Pulitzer Prize–winning author of Gödel, Escher, Bach, thinks we've lost sight of what artificial intelligence really means. His stubborn quest to replicate the human mind.
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Multiscale Balance of Excitation and Inhibition in Single-Unit ensemble Recordings in Human and Monkey Neocortex

Multiscale Balance of Excitation and Inhibition in Single-Unit ensemble Recordings in Human and Monkey Neocortex | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Nima Dehghani's insight:

new paper out...arXiv link will be added shortly get the pdf here: http://scholar.harvard.edu/nima/publications/multiscale-balance-excitation-and-inhibition-single-unit-ensemble-recordings-human

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Nerve pulse or electromechanical wave? Penetration of Action Potentials During Collision

Nerve pulse or electromechanical wave? Penetration of Action Potentials During Collision | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Experiments on neuron fibers from earthworms and lobsters reveal that two nerve pulses that collide do not annihilate, contrary to common beliefs of nerve electrophysiology.
Nima Dehghani's insight:

See commentary:

http://physics.aps.org/synopsis-for/10.1103/PhysRevX.4.031047

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Characterizing Autopoiesis in the Game of Lif - Artificial Life

Characterizing Autopoiesis in the Game of Lif  - Artificial Life | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
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Neurosci data deluge: Mapping brain activity at scale with cluster computing

Neurosci data deluge: Mapping brain activity at scale with cluster computing | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
An open-source library of analytical tools for mapping large-scale patterns of brain activity using cluster computing finds structure in two-photon imaging data from mouse and whole-brain light-sheet functional imaging data from behaving larval zebrafish. Vladimirov et al., also online, describes the light-sheet functional imaging system used here.
Nima Dehghani's insight:

With such surge of data, the lack of theory is the Achilles' heel of neuroscience.

 

Can computing keep up with the neuroscience data deluge?

http://spectrum.ieee.org/tech-talk/biomedical/imaging/can-computing-keep-up-with-the-neuroscience-data-deluge

 

Media coverage:

http://www.theguardian.com/science/neurophilosophy/2013/mar/18/an-activity-map-of-the-whole-zebrafish-brain

 

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Self-referencing cellular automata: A model of the evolution of information control in biological systems

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commentary:

https://www.sciencenews.org/blog/context/if-world-computer-life-algorithm

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NSF Distinguished Lecture in Mathematical and Physical Sciences: "Theoretical Physics and the Phenomena of Life: How Much Can We Calculate?

NSF Distinguished Lecture in Mathematical and Physical Sciences: "Theoretical Physics and the Phenomena of Life: How Much Can We Calculate? | Biobit: Computational Neuroscience & Biocomputation | Scoop.it

NSF's mission is to advance the progress of science, a mission accomplished by funding proposals for research and education made by scientists, engineers, and educators from across the country.

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David Deutsch – On Artificial Intelligence

David Deutsch – On Artificial Intelligence | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
The very laws of physics imply that artificial intelligence must be possible. What's holding us up?
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Universal computing by DNA origami robots in a living animal : Nature Nanotechnology : Nature Publishing Group

Universal computing by DNA origami robots in a living animal : Nature Nanotechnology : Nature Publishing Group | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Nanoscale robots made from DNA origami can dynamically interact with each other and perform logic computations in a living animal.
Nima Dehghani's insight:

if you are behind paywall, see the coverage here:

http://www.newscientist.com/article/dn25376-dna-nanobots-deliver-drugs-in-living-cockroaches.html?utm_source=NSNS&utm_medium=SOC&utm_campaign=twitter&cmpid=SOC%7CNSNS%7C2012-GLOBAL-twitter#.U0bOo9x7Ecv

 

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"Can Neuroscience Help Us Build Better Computer Vision and Machine Learning Systems?"

"Can Neuroscience Help Us Build Better Computer Vision and Machine Learning Systems?" | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
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[1309.7804] On statistics, computation and scalability

How should statistical procedures be designed so as to be scalable computationally to the massive datasets that are increasingly the norm? When coupled with the requirement that an answer to an inferential question be delivered within a certain time budget, this question has significant repercussions for the field of statistics. With the goal of identifying "time-data tradeoffs," we investigate some of the statistical consequences of computational perspectives on scability, in particular divide-and-conquer methodology and hierarchies of convex relaxations.

  

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Boltzmann machine: stochastic recurrent neural network

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A good review by (late) Sam Roweis

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MEMCOMPUTING. Intermittency, quasiperiodicity and chaos in probe-induced ferroelectric domain switching : Nature Physics

MEMCOMPUTING. Intermittency, quasiperiodicity and chaos in probe-induced ferroelectric domain switching : Nature Physics | Biobit: Computational Neuroscience & Biocomputation | Scoop.it

Memristive materials and devices, which enable information storage and processing on one and the same physical platform, offer an alternative to conventional von Neumann computation architectures. 

Nima Dehghani's insight:

A very interesting article. The ability of this type of computing engine lies in its simultaneous ability to store and process information, similar to neurons and unlike the conventional Von Neumann architecture. Also, the scalability of such architecture is more attuned to low-energy systems like brain. Perhaps this is a good step forward toward a biologically-inspired computing architecture.

 

If you are behind a paywall, check the arXiv paper on memecomputing:

http://arxiv.org/abs/1211.4487



A commentary:

http://blog.physicsworld.com/2013/11/19/how-to-build-a-memcomputer/

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