Biobit: Computational Neuroscience & Biocomputation
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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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Imprinting and recalling cortical ensembles

Imprinting and recalling cortical ensembles | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Donald Hebb's hypothesis that coactivation of neurons leads to the formation of ensembles of neurons has inspired neuroscientists for decades. The experimental creation of such ensembles has been technically challenging. Using two-photon optogenetic stimulation with single-cell resolution, Carrillo-Reid et al. discovered that recurrent activation of a group of neurons creates an ensemble that is imprinted in the brain circuitry. Activation of a single neuron can lead to recall of the entire ensemble in a phenomenon called pattern completion. The artificial ensemble persists over days and can be reactivated at later time points without interfering with endogenous circuitry.

Science , this issue p. [691][1]

[1]: /lookup/doi/10.1126/science.aaf7560
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Operation of a homeostatic sleep switch : Nature : Nature Research

Operation of a homeostatic sleep switch : Nature : Nature Research | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Sleep disconnects animals from the external world, at considerable risks and costs that must be offset by a vital benefit. Insight into this mysterious benefit will come from understanding sleep homeostasis: to monitor sleep need, an internal bookkeeper must track physiological changes that are linked to the core function of sleep. In Drosophila, a crucial component of the machinery for sleep homeostasis is a cluster of neurons innervating the dorsal fan-shaped body (dFB) of the central complex. Artificial activation of these cells induces sleep, whereas reductions in excitability cause insomnia. dFB neurons in sleep-deprived flies tend to be electrically active, with high input resistances and long membrane time constants, while neurons in rested flies tend to be electrically silent. Correlative evidence thus supports the simple view that homeostatic sleep control works by switching sleep-promoting neurons between active and quiescent states. Here we demonstrate state switching by dFB neurons, identify dopamine as a neuromodulator that operates the switch, and delineate the switching mechanism. Arousing dopamine caused transient hyperpolarization of dFB neurons within tens of milliseconds and lasting excitability suppression within minutes. Both effects were transduced by Dop1R2 receptors and mediated by potassium conductances. The switch to electrical silence involved the downregulation of voltage-gated A-type currents carried by Shaker and Shab, and the upregulation of voltage-independent leak currents through a two-pore-domain potassium channel that we term Sandman. Sandman is encoded by the CG8713 gene and translocates to the plasma membrane in response to dopamine. dFB-restricted interference with the expression of Shaker or Sandman decreased or increased sleep, respectively, by slowing the repetitive discharge of dFB neurons in the ON state or blocking their entry into the OFF state. Biophysical changes in a small population of neurons are thus linked to the control of sleep–wake state.
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Energy and time determine scaling in biological and computer designs

Energy and time determine scaling in biological and computer designs | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Metabolic rate in animals and power consumption in computers are analogous quantities that scale similarly with size. We analyse vascular systems of mammals and on-chip networks of microprocessors, where natural selection and human engineering, respectively, have produced systems that minimize both energy dissipation and delivery times. Using a simple network model that simultaneously minimizes energy and time, our analysis explains empirically observed trends in the scaling of metabolic rate in mammals and power consumption and performance in microprocessors across several orders of magnitude in size. Just as the evolutionary transitions from unicellular to multicellular animals in biology are associated with shifts in metabolic scaling, our model suggests that the scaling of power and performance will change as computer designs transition to decentralized multi-core and distributed cyber-physical systems. More generally, a single energy–time minimization principle may govern the design of many complex systems that process energy, materials and information.

This article is part of the themed issue ‘The major synthetic evolutionary transitions’.
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Towards an integration of deep learning and neuroscience

Towards an integration of deep learning and neuroscience | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution
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Dynamic Balance of Excitation and Inhibition in Human and Monkey Neocortex

Dynamic Balance of Excitation and Inhibition in Human and Monkey Neocortex | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Balance of excitation and inhibition is a fundamental feature of in vivo network activity and is important for its computations.
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Demon Dynamics: Deterministic Chaos, the Szilard Map, and the Intelligence of Thermodynamic Systems

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Adaptation to sensory input tunes visual cortex to criticality

Adaptation to sensory input tunes visual cortex to criticality | Biobit: Computational Neuroscience & Biocomputation | Scoop.it

 In visual cortex and in a computational model, strong sensory input initially elicits cortical network dynamics that are not critical, but adaptive changes in the network rapidly tune the system to criticality.

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Neuromimetic Circuits with Synaptic Devices Based on Strongly Correlated Electron Systems

Neuromimetic Circuits with Synaptic Devices Based on Strongly Correlated Electron Systems | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
A crucial feature of biological neural architectures is their ability to learn, and unlearn, in response to external stimulation. In this work the authors reproduce this feature in an electronic network composed of strongly correlated electron materials implemented as synaptic devices. This network responds to both excitatory and inhibitory excitations, exhibits associative as well as nonassociative learning, and even displays habituation-like behavior and other aspects of authentic neuronal systems. This opens avenues for both investigating biological behaviors and designing computers with the capacity to learn and remember based on hardware alone.
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http://physics.aps.org/synopsis-for/10.1103/PhysRevApplied.2.064003

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Computational capacity of human neurons: High Bandwidth Synaptic Communication and Frequency Tracking

Computational capacity of human neurons: High Bandwidth Synaptic Communication and Frequency Tracking | Biobit: Computational Neuroscience & Biocomputation | Scoop.it

Data show that, in contrast to the widely held views of limited information transfer in rodent depressing synapses, fast recovering synapses of human neurons can actually transfer substantial amounts of information during spike trains. In addition, human pyramidal neurons are equipped to encode high synaptic information content. Thus, adult human cortical microcircuits relay information at a wider bandwidth than rodent microcircuits..

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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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High-frequency oscillations in human and monkey neocortex during the wake–sleep cycle

High-frequency oscillations in human and monkey neocortex during the wake–sleep cycle | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
National Academy of Sciences
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Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice

Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
National Academy of Sciences
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The Free Energy Requirements of Biological Organisms; Implications for Evolution

The Free Energy Requirements of Biological Organisms; Implications for Evolution | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Recent advances in nonequilibrium statistical physics have provided unprecedented insight into the thermodynamics of dynamic processes. The author recently used these advances to extend Landauer’s semi-formal reasoning concerning the thermodynamics of bit erasure, to derive the minimal free energy required to implement an arbitrary computation. Here, I extend this analysis, deriving the minimal free energy required by an organism to run a given (stochastic) map π from its sensor inputs to its actuator outputs. I use this result to calculate the input-output map π of an organism that optimally trades off the free energy needed to run π with the phenotypic fitness that results from implementing π. I end with a general discussion of the limits imposed on the rate of the terrestrial biosphere’s information processing by the flux of sunlight on the Earth.
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Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex

Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution
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Dynamic Flux Tubes Form Reservoirs of Stability in Neuronal Circuits

Dynamic Flux Tubes Form Reservoirs of Stability in Neuronal Circuits | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Can a single neuronal spike in 100 billion spikes affect information processing in the human brain? Monteforte and Wolf from Max-Planck Institute for Dynamics and Self-organization show that the answer is ``yes'' and also reveal how that comes about with a novel concept of nonlinear dynamics.
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Reconstruction and Simulation of Neocortical Microcircuitry: Cell

Reconstruction and Simulation of Neocortical Microcircuitry: Cell | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
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coverage:

http://www.theguardian.com/science/2015/oct/08/complex-living-brain-simulation-replicates-sensory-rat-behaviour

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Building an organic computing device with multiple interconnected brains : Scientific Reports : Nature Publishing Group

Building an organic computing device with multiple interconnected brains : Scientific Reports : Nature Publishing Group | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
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Human-level control through deep reinforcement learning

Human-level control through deep reinforcement learning | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
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full paper:
http://www.nature.com/articles/nature14236.epdf?shared_access_token=GIOrFXxA5wzljOYzWRImAtRgN0jAjWel9jnR3ZoTv0P5kedCCNjz3FJ2FhQCgXkAePdzyjambXhp5LvdzdyyCNg34zh4473TwBocbqMfDjtLMFljQyylqXySHKcB585VHZDGSzYyXk0oiP2Rt2vUAlNIuBitkPUO4IlY9pFoWlfmlzHtdTOkTTI8P5gqa7k56Yxz--Pb0DYy2QuvFpIgyLADMejyqoWJknb-dMcsXFE%3D

 

commentary (behind pay wall): 

http://www.nature.com/nature/journal/v518/n7540/full/518486a.html

 

news:

http://www.bbc.com/news/science-environment-31623427

 

http://www.nature.com/news/game-playing-software-holds-lessons-for-neuroscience-1.16979?WT.mc_id=FBK_NatureNews

 

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"What is life?" Second take, a post-Darwinian hypothesis...Statistical Physics of Adaptation.

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paper:

http://arxiv-web3.library.cornell.edu/abs/1412.1875v1

 

Few months ago, Quanta Magazine released an interesting piece on England's work:

http://www.quantamagazine.org/20140122-a-new-physics-theory-of-life/

 

His talk on the matter in Stockholm:

https://www.youtube.com/watch?v=e91D5UAz-f4

 

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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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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.
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See commentary:

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

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