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[0] Loewenstein Y, Seung HS, Operant matching is a generic outcome of synaptic plasticity based on the covariance between reward and neural activity.Proc Natl Acad Sci U S A 103:41, 15224-9 (2006 Oct 10)

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ref: -0 tags: ETPA entangled two photon absorption Goodson date: 09-24-2019 02:25 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

Can we image biological tissue with entangled photons?

How much fluorescence can we expect, based on reasonable concentrations & published ETPA cross sections?

Start with beer's law: A=σLN A = \sigma L N AA = absorbance; LL = sample length, 10 μm, 1e-3 cm; NN = concentration, 10 μmol; σ\sigma = cross-section, for ETPA assume 2.4e18cm 2/molec2.4e-18 cm^2 / molec (this is based on a FMN based fluorophore; actual cross-section may be higher). Including Avogadro's number and 1l=1000cm 31 l = 1000 cm^3 , A=1.45e5A = 1.45e-5

Now, add in quantum efficiency ϕ=0.8\phi = 0.8 (Rhodamine); collection efficiency η=0.2\eta = 0.2 ; and an incoming photon pair flux of I=1e12photons/sec/modeI = 1e12 photons / sec / mode (which roughly about the limit for quantum behavior; n = 0.1 photons / mode; will add this calculation).

F=ϕησLNI=2.3e6photons/secF = \phi \eta \sigma L N I = 2.3e6 photons/sec This is very low, but within practical imaging limits. As a comparison, incoherent 2p imaging creates ~ 100 photons per pulse, of which 10 make it to the detector; for 512 x 512 pixels at 15fps, the dwell time on each pixel is 20 pulses of a 80 MHz Ti:Sapphire laser, or ~ 200 photons.

Note the pair flux is per optical mode; for a typical application, we'll use a Nikon 16x objective with a 600 μm Ø FOV and 0.8 NA. At 800 nm imaging wavelength, the diffraction limit is 0.5 μm. This equates to about 7e57e5 addressable modes in the FOV. Then an illumination of 1e121e12 photons / sec / mode equates to 7e177e17 photons over the whole field; if each photon pair has an energy of 2.75eV,λ=450nm2.75 eV, \lambda = 450 nm , this is equivalent to 300 mW. 100mW is a reasonable limit, hence scale incoming flux to 2.3e172.3e17 pairs /sec.

Hence, the imaging mode is power limited, and not quantum limited (if you could get such a bright entangled source). And right now that's the limit -- for a BBO crystal, circa 1998 experimenters were getting 1e4 photons / sec / mW. So, 2.3e172.3e17 pairs / sec would require 23 GW. Yikes.

More efficient entangled sources have been developed, using periodically-poled potassium titanyl phosphate (PPPTP), which (again assuming linearity) puts the power requirement at 23 MW. This is within the reason of q-switched lasers, but still incredibly inefficient. The down-conversion process is not linear in intensity, which is why Goodson pumps with SHG from a Ti:sapphire to yield ~1e7 photons; but this of induces temporal correlations which increase the frequency of incoherent TPA.

Still, combining PPPTP with a Ti:sapphire laser could result in 1e13 photons / sec, which is sufficient for scanned microscopy. Since the laser is pulsed, it will still be subject to incoherent TPA; but that's OK, the point is to reduce the power going into the animal via larger ETPA cross-section. The answer to above is a tentative yes. Upon the development of brighter entangled sources (e.g. arrays of quantum structures), this can move to fully widefield imaging.

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ref: -0 tags: ETPA entangled two photon absorption Goodson date: 09-19-2019 15:49 gmt revision:13 [12] [11] [10] [9] [8] [7] [head]

Various papers put out by the Goodson group:

And from a separate group at Northwestern:

  • Entangled Photon Resonance Energy Transfer in Arbitrary Media
    • Suggests three orders of magnitude improvement in cross-section relative to incoherent TPA.
    • In SPDC, photon pairs are generated randomly and usually accompanied by undesirable multipair emissions.
      • For solid-state artificial atomic systems with radiative cascades (singled quantum emitters like quantum dots), the quantum efficiency is near unity.
    • Paper is highly mathematical, and deals with resonance energy transfer (which is still interesting)

Regarding high fluence sources, quantum dots / quantum structures seem promising.

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ref: -2013 tags: 2p two photon STED super resolution microscope date: 09-18-2019 02:22 gmt revision:0 [head]

PMID-23442956 Two-Photon Excitation STED Microscopy in Two Colors in Acute Brain Slices

  • Plenty of details on how they set up the microscope.

PMID-29932052 Chronic 2P-STED imaging reveals high turnover of spines in the hippocampus in vivo

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ref: -2017 tags: two photon holographic imaging Arch optogenetics GCaMP6 date: 09-12-2019 19:24 gmt revision:1 [0] [head]

PMID-28053310 Simultaneous high-speed imaging and optogenetic inhibition in the intact mouse brain.

  • Bovetti S1, Moretti C1, Zucca S1, Dal Maschio M1, Bonifazi P2,3, Fellin T1.
  • Image GCamp6 in either scanned mode (high resolution, slow) or holographically (SLM, redshirt 80x80 NeuroCCD, activate opsin Arch, simultaneously record juxtasomal action potentials.

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ref: -0 tags: nanophotonics interferometry neural network mach zehnder interferometer optics date: 06-13-2019 21:55 gmt revision:3 [2] [1] [0] [head]

Deep Learning with Coherent Nanophotonic Circuits

  • Used a series of Mach-Zehnder interferometers with thermoelectric phase-shift elements to realize the unitary component of individual layer weight-matrix computation.
    • Weight matrix was decomposed via SVD into UV*, which formed the unitary matrix (4x4, Special unitary 4 group, SU(4)), as well as Σ\Sigma diagonal matrix via amplitude modulators. See figure above / original paper.
    • Note that interfereometric matrix multiplication can (theoretically) be zero energy with an optical system (modulo loss).
      • In practice, you need to run the phase-moduator heaters.
  • Nonlinearity was implemented electronically after the photodetector (e.g. they had only one photonic circuit; to get multiple layers, fed activations repeatedly through it. This was a demonstration!)
  • Fed network FFT'd / banded recordings of consonants through the network to get near-simulated vowel recognition.
    • Claim that noise was from imperfect phase setting in the MZI + lower resolution photodiode read-out.
  • They note that the network can more easily (??) be trained via the finite difference algorithm (e.g. test out an incremental change per weight / parameter) since running the network forward is so (relatively) low-energy and fast.
    • Well, that's not totally true -- you need to update multiple weights at once in a large / deep network to descend any high-dimensional valleys.

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ref: -2019 tags: optical neural networks spiking phase change material learning date: 06-01-2019 19:00 gmt revision:4 [3] [2] [1] [0] [head]

All-optical spiking neurosynaptic networks with self-learning capabilities

  • J. Feldmann, N. Youngblood, C. D. Wright, H. Bhaskaran & W. H. P. Pernice
  • Idea: use phase-change material to either block or pass the light in waveguides.
    • In this case, they used GST -- germanium-antimony-tellurium. This material is less reflective in the amorphous phase, which can be reached by heating to ~150C and rapidly quenching. It is more reflective in the crystalline phase, which occurs on annealing.
  • This is used for both plastic synapses (phase change driven by the intensity of the light) and the nonlinear output of optical neurons (via a ring resonator).
  • Uses optical resonators with very high Q factors to couple different wavelengths of light into the 'dendrite'.
  • Ring resonator on the output: to match the polarity of the phase-change material. Is this for reset? Storing light until trigger?
  • Were able to get correlative-like or hebbian learning (which I suppose is not dissimilar from really slow photographic film, just re-branded, and most importantly with nonlinear feedback.)
  • Issue: every weight needs a different source wavelength! Hence they have not demonstrated a multi-layer network.
  • Previous paper: All-optical nonlinear activation function for photonic neural networks
    • Only 3db and 7db extinction ratios for induced transparency and inverse saturation

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ref: -0 tags: Na Ji 2p two photon fluorescent imaging pulse splitting damage bleaching date: 05-31-2019 19:55 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-18204458 High-speed, low-photodamage nonlinear imaging using passive pulse splitters

  • Core idea: take a single pulse and spread it out to N=2 kN= 2^k pulses using reflections and delay lines.
  • Assume two optical processes, signal SI αS \propto I^{\alpha} and photobleaching/damage DI βD \propto I^{\beta} , β>α>1\beta \gt \alpha \gt 1
  • Then an NN pulse splitter requires N 11/αN^{1-1/\alpha} greater average power but reduces the damage by N 1β/α.N^{1-\beta/\alpha}.
  • At constant signal, the same NN pulse splitter requires N\sqrt{N} more power, consistent with two photon excitation (proportional to the square of the intensity: N pulses of N/N\sqrt{N}/N intensity, 1/N per pulse fluorescence, Σ1\Sigma \rightarrow 1 overall fluorescence.)
  • This allows for shorter dwell times, higher power at the sample, lower damage, slower photobleaching, and better SNR for fluorescently labeled slices.
  • Examine the list of references too, e.g. "Multiphoton multifocal microscopy exploiting a diffractive optical element" (2003)

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ref: -2018 tags: Michael Levin youtube talk NIPS 2018 regeneration bioelectricity organism patterning flatworm date: 04-09-2019 18:50 gmt revision:1 [0] [head]

What Bodies Think About: Bioelectric Computation Outside the Nervous System - NeurIPS 2018

  • Short notes from watching the video, mostly interesting factoids: (This is a somewhat more coordinated narrative in the video. Am resisting ending each of these statements with and exclamation point).
  • Human children up to 7-11 years old can regenerate their fingertips.
  • Human embryos, when split in half early, develop into two normal humans; mouse embryos, when squished together, make one normal mouse.
  • Butterflies retain memories from their caterpillar stage, despite their brains liquefying during metamorphosis.
  • Flatworms are immortal, and can both grow and contract, as the environment requires.
    • They can also regenerate a whole body from segments, and know to make one head, tail, gut etc.
  • Single cell organisms, e.g. Lacrymaria, can have complex (and fast!) foraging / hunting plans -- without a brain or anything like it.
  • Axolotl can regenerate many parts of their body (appendages etc), including parts of the nervous system.
  • Frog embryos can self-organize an experimenter jumbled body plan, despite the initial organization having never been experienced in evolution.
  • Salamanders, when their tail is grafted into a foot/leg position, remodel the transplant into a leg and foot.
  • Neurotransmitters are ancient; fungi, who diverged from other forms of life about 1.5 billion years ago, still use the same set of inter-cell transmitters e.g. serotonin, which is why modulatory substances from them have high affinity & a strong effect on humans.
  • Levin, collaborators and other developmental biologists have been using voltage indicators in embryos ... this is not just for neurons.
  • Can make different species head shapes in flatworms by exposing them to ion-channel modulating drugs. This despite the fact that the respective head shapes are from species that have been evolving separately for 150 million years.
  • Indeed, you can reprogram (with light gated ion channels, drugs, etc) to body shapes not seen in nature or not explored by evolution.
    • That said, this was experimental, not by design; Levin himself remarks that the biology that generates these body plans is not known.
  • Flatworms can sore memory in bioelectric networks.
  • Frogs don't normally regenerate their limbs. But, with a drug cocktail targeting bioelectric signaling, they can regenerate semi-functional legs, complete with nerves, muscle, bones, and cartilage. The legs are functional (enough).
  • Manipulations of bioelectric signaling can reverse very serious genetic problems, e.g. deletion of Notch, to the point that tadpoles regain some ability for memory creation & recall.

  • I wonder how so much information can go through a the apparently scalar channel of membrane voltage. It seems you'd get symbol interference, and that many more signals would be required to pattern organs.
  • That said, calcium is used a great many places in the cell for all sorts of signaling tasks, over many different timescales as well, and it doesn't seem to be plagued by interference.
    • First question from the audience was how cells differentiate organismal patterning signals and behavioral signals, e.g. muscle contraction.

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ref: -2017 tags: vicarious dileep george captcha message passing inference heuristic network date: 03-06-2019 04:31 gmt revision:2 [1] [0] [head]

PMID-29074582 A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs

  • Vicarious supplementary materials on their RCN (recursive cortical network).
  • Factor scene into shape and appearance, which CNN or DCNN do not do -- they conflate (ish? what about the style networks?)
    • They call this the coloring book approach -- extract shape then attach appearance.
  • Hierarchy of feature layers F frcF_{f r c} (binary) and pooling layer H frcH_{f r c} (multinomial), where f is feature, r is row, c is column (e.g. over image space).
  • Each layer is exclusively conditional on the layer above it, and all features in a layer are conditionally independent given the layer above.
  • Pool variables H frcH_{f r c} is multinomial, and each value associated with a feature, plus one off feature.
    • These features form a ‘pool’, which can/does have translation invariance.
  • If any of the pool variables are set to enable FF , then that feature is set (or-operation). Many pools can contain a given feature.
  • One can think of members of a pool as different alternatives of similar features.
  • Pools can be connected laterally, so each is dependent on the activity of its neighbors. This can be used to enforce edge continuity.
  • Each bottom-level feature corresponds to an edge, which defines ‘in’ and ‘out’ to define shape, YY .
  • These variables YY are also interconnected, and form a conditional random field, a ‘Potts model’. YY is generated by gibbs sampling given the F-H hierarchy above it.
  • Below Y, the per-pixel model X specifies texture with some conditional radial dependence.
  • The model amounts to a probabalistic model for which exact inference is impossible -- hence you must do approximate, where a bottom up pass estimates the category (with lateral connections turned off), and a top down estimates the object mask. Multiple passes can be done for multiple objects.
  • Model has a hard time moving from rgb pixels to edge ‘in’ and ‘out’; they use edge detection pre-processing stage, e.g. Gabor filter.
  • Training follows a very intuitive, hierarchical feature building heuristic, where if some object or collection of lower level features is not present, it’s added to the feature-pool tree.
    • This includes some winner-take-all heuristic for sparsification.
    • Also greedily learn some sort of feature ‘’dictionary’’ from individual unlabeled images.
  • Lateral connections are learned similarly, with a quasi-hebbian heuristic.
  • Neuroscience inspiration: see refs 9, 98 for message-passing based Bayesian inference.

  • Overall, a very heuristic, detail-centric, iteratively generated model and set of algorithms. You get the sense that this was really the work of Dileep George or only a few people; that it was generated by successively patching and improving the model/algo to make up for observed failures and problems.
    • As such, it offers little long-term vision for what is possible, or how perception and cognition occurs.
    • Instead, proof is shown that, well, engineering works, and the space of possible solutions -- including relatively simple elements like dictionaries and WTA -- is large and fecund.
      • Unclear how this will scale to even more complex real-world problems, where one would desire a solution that does not have to have each level carefully engineered.
      • Modern DCNN, at least, do not seem to have this property -- the structure is learned from the (alas, labeled) data.
  • This extends to the fact that yes, their purpose-built system achieves state of the art performance on the designated CAPATCHA tasks.
  • Check: B. M. Lake, R. Salakhutdinov, J. B. Tenenbaum, Human-level concept learning through probabilistic program induction. Science 350, 1332–1338 (2015). doi:10.1126/science.aab3050 Medline

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ref: -0 tags: convolutional neural networks audio feature extraction vocals keras tensor flow fourier date: 02-18-2019 21:40 gmt revision:3 [2] [1] [0] [head]

Audio AI: isolating vocals from stereo music using Convolutional Neural Networks

  • Ale Koretzky
  • Fairly standard CNN, but use a binary STFT mask to isolate vocals from instruments.
    • Get Fourier-type time-domain artifacts as a results; but it sounds reasonable.
    • Didn't realize it until this paper / blog post: stacked conv layers combine channels.
    • E.g. Input size 513*25*16 513 * 25 * 16 (512 freq channels + DC, 25 time slices, 16 filter channels) into a 3x3 Conv2D -> 3*3*16+16=1603 * 3 * 16 + 16 = 160 total parameters (filter weights and bias).
    • If this is followed by a second Conv2D layer of the same parameters, the layer acts as a 'normal' fully connected network in the channel dimension.
    • This means there are (3*3*16)*16+16=2320(3 * 3 * 16) * 16 + 16 = 2320 parameters.
      • Each input channel from the previous conv layer has independent weights -- they are not shared -- whereas the spatial weights are shared.
      • Hence, same number of input channels and output channels (in this case; doesn't have to be).
      • This, naturally, falls out of spatial weight sharing, which might be obvious in retrospect; of course it doesn't make sense to share non-spatial weights.
      • See also: https://datascience.stackexchange.com/questions/17064/number-of-parameters-for-convolution-layers
  • Synthesized a large training set via acapella youtube videos plus instrument tabs .. that looked like a lot of work!
    • Need a karaoke database here.
  • Authors wrapped this into a realtime extraction toolkit.

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ref: -2019 tags: Arild Nokland local error signals backprop neural networks mnist cifar VGG date: 02-15-2019 03:15 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

Training neural networks with local error signals

  • Arild Nokland and Lars H Eidnes
  • Idea is to use one+ supplementary neural networks to measure within-batch matching loss between transformed hidden-layer output and one-hot label data to produce layer-local learning signals (gradients) for improving local representation.
  • Hence, no backprop. Error signals are all local, and inter-layer dependencies are not explicitly accounted for (! I think).
  • L simL_{sim} : given a mini-batch of hidden layer activations H=(h 1,...,h n)H = (h_1, ..., h_n) and a one-hot encoded label matrix Y=(y 1,...,y nY = (y_1, ..., y_n ,
    • L sim=||S(NeuralNet(H))S(Y)|| F 2 L_{sim} = || S(NeuralNet(H)) - S(Y)||^2_F (don't know what F is..)
    • NeuralNet()NeuralNet() is a convolutional neural net (trained how?) 3*3, stride 1, reduces output to 2.
    • S()S() is the cosine similarity matrix, or correlation matrix, of a mini-batch.
  • L pred=CrossEntropy(Y,W TH)L_{pred} = CrossEntropy(Y, W^T H) where W is a weight matrix, dim hidden_size * n_classes.
    • Cross-entropy is H(Y,W TH)=Σ i,jY i,jlog((W TH) i,j)+(1Y i,j)log(1(W TH) i,j) H(Y, W^T H) = \Sigma_{i,j} Y_{i,j} log((W^T H)_{i,j}) + (1-Y_{i,j}) log(1-(W^T H)_{i,j})
  • Sim-bio loss: replace NeuralNet()NeuralNet() with average-pooling and standard-deviation op. Plus one-hot target is replaced with a random transformation of the same target vector.
  • Overall loss 99% L simL_sim , 1% L predL_pred
    • Despite the unequal weighting, both seem to improve test prediction on all examples.
  • VGG like network, with dropout and cutout (blacking out square regions of input space), batch size 128.
  • Tested on all the relevant datasets: MNIST, Fashion-MNIST, Kuzushiji-MNIST, CIFAR-10, CIFAR-100, STL-10, SVHN.
  • Pretty decent review of similarity matching measures at the beginning of the paper; not extensive but puts everything in context.
    • See for example non-negative matrix factorization using Hebbian and anti-Hebbian learning in and Chklovskii 2014.
  • Emphasis put on biologically realistic learning, including the use of feedback alignment {1423}
    • Yet: this was entirely supervised learning, as the labels were propagated back to each layer.
    • More likely that biology is setup to maximize available labels (not a new concept).

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ref: -0 tags: diffraction terahertz 3d print ucla deep learning optical neural networks date: 02-13-2019 23:16 gmt revision:1 [0] [head]

All-optical machine learning using diffractive deep neural networks

  • Pretty clever: use 3D printed plastic as diffractive media in a 0.4 THz all-optical all-interference (some attenuation) linear convolutional multi-layer 'neural network'.
  • In the arxive publication there are few details on how they calculated or optimized given diffractive layers.
  • Absence of nonlinearity will limit things greatly.
  • Actual observed performance (where thy had to print out the handwritten digits) rather poor, ~ 60%.

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ref: -0 tags: Hinton google tech talk dropout deep neural networks Boltzmann date: 02-12-2019 08:03 gmt revision:2 [1] [0] [head]

Brains, sex, and machine learning -- Hinton google tech talk.

  • Hinton believes in the the power of crowds -- he thinks that the brain fits many, many different models to the data, then selects afterward.
    • Random forests, as used in predator, is an example of this: they average many simple to fit and simple to run decision trees. (is apparently what Kinect does)
  • Talk focuses on dropout, a clever new form of model averaging where only half of the units in the hidden layers are trained for a given example.
    • He is inspired by biological evolution, where sexual reproduction often spontaneously adds or removes genes, hence individual genes or small linked genes must be self-sufficient. This equates to a 'rugged individualism' of units.
    • Likewise, dropout forces neurons to be robust to the loss of co-workers.
    • This is also great for parallelization: each unit or sub-network can be trained independently, on it's own core, with little need for communication! Later, the units can be combined via genetic algorithms then re-trained.
  • Hinton then observes that sending a real value p (output of logistic function) with probability 0.5 is the same as sending 0.5 with probability p. Hence, it makes sense to try pure binary neurons, like biological neurons in the brain.
    • Indeed, if you replace the backpropagation with single bit propagation, the resulting neural network is trained more slowly and needs to be bigger, but it generalizes better.
    • Neurons (allegedly) do something very similar to this by poisson spiking. Hinton claims this is the right thing to do (rather than sending real numbers via precise spike timing) if you want to robustly fit models to data.
      • Sending stochastic spikes is a very good way to average over the large number of models fit to incoming data.
      • Yes but this really explains little in neuroscience...
  • Paper referred to in intro: Livnat, Papadimitriou and Feldman, PMID-19073912 and later by the same authors PMID-20080594
    • A mixability theory for the role of sex in evolution. -- "We define a measure that represents the ability of alleles to perform well across different combinations and, using numerical iterations within a classical population-genetic framework, show that selection in the presence of sex favors this ability in a highly robust manner"
    • Plus David MacKay's concise illustration of why you need sex, pg 269, __Information theory, inference, and learning algorithms__
      • With rather simple assumptions, asexual reproduction yields 1 bit per generation,
      • Whereas sexual reproduction yields G\sqrt G , where G is the genome size.

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ref: -0 tags: computational biology evolution metabolic networks andreas wagner genotype phenotype network date: 06-12-2017 19:35 gmt revision:1 [0] [head]

Evolutionary Plasticity and Innovations in Complex Metabolic Reaction Networks

  • ‘’João F. Matias Rodrigues, Andreas Wagner ‘’
  • Our observations suggest that the robustness of the Escherichia coli metabolic network to mutations is typical of networks with the same phenotype.
  • We demonstrate that networks with the same phenotype form large sets that can be traversed through single mutations, and that single mutations of different genotypes with the same phenotype can yield very different novel phenotypes
  • Entirely computational study.
    • Examines what is possible given known metabolic building-blocks.
  • Methodology: collated a list of all metabolic reactions in E. Coli (726 reactions, excluding 205 transport reactions) out of 5870 possible reactions.
    • Then ran random-walk mutation experiments to see where the genotype + phenotype could move. Each point in the genotype had to be viable on either a rich (many carbon source) or minimal (glucose) growth medium.
    • Viability was determined by Flux-balance analysis (FBA).
      • In our work we use a set of biochemical precursors from E. coli 47-49 as the set of required compounds a network needs to synthesize, ‘’’by using linear programming to optimize the flux through a specific objective function’’’, in this case the reaction representing the production of biomass precursors we are able to know if a specific metabolic network is able to synthesize the precursors or not.
      • Used Coin-OR and Ilog to optimize the metabolic concentrations (I think?) per given network.
    • This included the ability to synthesize all required precursor biomolecules; see supplementary information.
    • ‘’’“Viable” is highly permissive -- non-zero biomolecule concentration using FBA and linear programming. ‘’’
    • Genomic distances = hamming distance between binary vectors, where 1 = enzyme / reaction possible; 0 = mutated off; 0 = identical genotype, 1 = completely different genotype.
  • Between pairs of viable genetic-metabolic networks, only a minority (30 - 40%) of reactions are essential,
    • Which naturally increases with increasing carbon source diversity:
    • When they go back an examine networks that can sustain life on any of (up to) 60 carbon sources, and again measure the distance from the original E. Coli genome, they find this added robustness does not significantly constrain network architecture.

Summary thoughts: This is a highly interesting study, insofar that the authors show substantial support for their hypotheses that phenotypes can be explored through random-walk non-lethal mutations of the genotype, and this is somewhat invariant to the source of carbon for known biochemical reactions. What gives me pause is the use of linear programming / optimization when setting the relative concentrations of biomolecules, and the permissive criteria for accepting these networks; real life (I would imagine) is far more constrained. Relative and absolute concentrations matter.

Still, the study does reflect some robustness. I suggest that a good control would be to ‘fuzz’ the list of available reactions based on statistical criteria, and see if the results still hold. Then, go back and make the reactions un-biological or less networked, and see if this destroys the measured degrees of robustness.

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ref: -0 tags: china trustwothiness social engineering communism date: 10-31-2016 05:42 gmt revision:1 [0] [head]

China 'social credit': Beijing sets up huge system

So long as it purports to measure just one social variable -- 'trustworthiness' -- it might be a good idea. Many commerce websites (.. ebay ..) have these sort of rating systems already, and they are useful. When humans live in smaller communities something like this is in the shared consciousness.

Peering into everyone's purchasing habits and hobbies, however, seems like it will be grossly myopic and, as the article says, Orwellian. Likely they will train a deep-belief network on past data of weakly and communist party defined success, with all purchasing and social media as the input data, and use that in the proprietary algorithm for giving people their scalars to optimize. This would be the ultimate party control tool -- a great new handle for controlling people's minds, even 'better' than capitalism.

Surprising that the article only hints at this, and that the Chinese themselves seem rather clueless that it's a power play. In this sense, it's a very clever play to link it to reproduction.


Other comments:

These sorts of systems may be necessary in highly populated countries, where freedom and individuality are less valued and social cohesion is requisite.

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ref: -0 tags: David Kleinfeld cortical vasculature laser surgery network occlusion flow date: 09-23-2016 06:35 gmt revision:1 [0] [head]

Heller Lecture - Prof. David Kleinfeld

  • Also mentions the use of LIBS + q-switched laser for precisely drilling holes in the scull. Seems to work!
    • Use 20ns delay .. seems like there is still spectral broadening.
    • "Turn neuroscience into an industrial process, not an art form" After doing many surgeries, agreed!
  • Vasodiliation & vasoconstriction is very highly regulated; there is not enough blood to go around.
    • Vessels distant from a energetic / stimulated site will (net) constrict.
  • Vascular network is most entirely closed-loop, and not tree-like at all -- you can occlude one artery, or one capillary, and the network will route around the occlusion.
    • The density of the angio-architecture in the brain is unique in this.
  • Tested micro-occlusions by injecting rose bengal, which releases free radicals on light exposure (532nm, 0.5mw), causing coagulation.
  • "Blood flow on the surface arteriole network is insensitive to single occlusions"
  • Penetrating arterioles and venules are largely stubs -- single unbranching vessels, which again renders some immunity to blockage.
  • However! Occlusion of a penetrating arteriole retards flow within a 400 - 600um cylinder (larger than a cortical column!)
  • Occulsion of many penetrating vessels, unsurprisingly, leads to large swaths of dead cortex, "UBOS" in MRI parlance (unidentified bright objects).
  • Death and depolarizing depression can be effectively prevented by excitotoxicity inhibitors -- MK801 in the slides (NMDA blocker, systemically)

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ref: -0 tags: hinton convolutional deep networks image recognition 2012 date: 01-11-2014 20:14 gmt revision:0 [head]

ImageNet Classification with Deep Convolutional Networks

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ref: Ganguly-2011.05 tags: Carmena 2011 reversible cortical networks learning indirect BMI date: 01-23-2013 18:54 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-21499255[0] Reversible large-scale modification of cortical networks during neuroprosthetic control.

  • Split the group of recorded motor neurons into direct (decoded and controls the BMI) and indirect (passive) neurons.
  • Both groups showed changes in neuronal tuning / PD.
    • More PD. Is there no better metric?
  • Monkeys performed manual control before (MC1) and after (MC2) BMI training.
    • The majority of neurons reverted back to original tuning after BC; c.f. [1]
  • Monkeys were trained to rapidly switch between manual and brain control; still showed substantial changes in PD.
  • 'Near' (on same electrode as direct neurons) and 'far' neurons (different electrode) showed similar changes in PD.
    • Modulation Depth in indirect neurons was less in BC than manual control.
  • Prove (pretty well) that motor cortex neuronal spiking can be dissociated from movement.
  • Indirect neurons showed decreased modulation depth (MD) -> perhaps this is to decrease interference with direct neurons.
  • Quote "Studies of operant conditioning of single neurons found that conconditioned adjacent neurons were largely correlated with the conditioned neurons".
    • Well, also: Fetz and Baker showed that you can condition neurons recorded on the same electrode to covary or inversely vary.
  • Contrast with studies of motor learning in different force fields, where there is a dramatic memory trace.
    • Possibly this is from proprioception activating the cerebellum?

Other notes:

  • Scale bars on the waveforms are incorrect for figure 1.
  • Same monkeys as [2]

____References____

[0] Ganguly K, Dimitrov DF, Wallis JD, Carmena JM, Reversible large-scale modification of cortical networks during neuroprosthetic control.Nat Neurosci 14:5, 662-7 (2011 May)
[1] Gandolfo F, Li C, Benda BJ, Schioppa CP, Bizzi E, Cortical correlates of learning in monkeys adapting to a new dynamical environment.Proc Natl Acad Sci U S A 97:5, 2259-63 (2000 Feb 29)
[2] Ganguly K, Carmena JM, Emergence of a stable cortical map for neuroprosthetic control.PLoS Biol 7:7, e1000153 (2009 Jul)

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ref: Dethier-2011.28 tags: BMI decoder spiking neural network Kalman date: 01-06-2012 00:20 gmt revision:1 [0] [head]

IEEE-5910570 (pdf) Spiking neural network decoder for brain-machine interfaces

  • Golden standard: kalman filter.
  • Spiking neural network got within 1% of this standard.
  • THe 'neuromorphic' approach.
  • Used Nengo, freely available neural simulator.

____References____

Dethier, J. and Gilja, V. and Nuyujukian, P. and Elassaad, S.A. and Shenoy, K.V. and Boahen, K. Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on 396 -399 (2011)

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ref: Grutzendler-2011.09 tags: two-photon imaging in-vivo neurons recording dendrites spines date: 01-03-2012 01:02 gmt revision:3 [2] [1] [0] [head]

PMID-21880826[0] http://cshprotocols.cshlp.org/content/2011/9/pdb.prot065474.full?rss=1

  • Excellent source of information and references. Go CSH!
  • Possible to image up to 400um deep. PMID-12490949[1]
  • People have used TPLSM imaging for years in mice. PMID-19946265[2]

____References____

[0] Grutzendler J, Yang G, Pan F, Parkhurst CN, Gan WB, Transcranial two-photon imaging of the living mouse brain.Cold Spring Harb Protoc 2011:9, no Pages (2011 Sep 1)
[1] Grutzendler J, Kasthuri N, Gan WB, Long-term dendritic spine stability in the adult cortex.Nature 420:6917, 812-6 (2002 Dec 19-26)
[2] Yang G, Pan F, Gan WB, Stably maintained dendritic spines are associated with lifelong memories.Nature 462:7275, 920-4 (2009 Dec 17)

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ref: Sanchez-2005.06 tags: BMI Sanchez Nicolelis Wessberg recurrent neural network date: 01-01-2012 18:28 gmt revision:2 [1] [0] [head]

IEEE-1439548 (pdf) Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface

  • Putting it here for the record.
  • Note they did a sensitivity analysis (via chain rule) of the recurrent neural network used for BMI predictions.
  • Used data (X,Y,Z) from 2 monkeys feeding.
  • Figure 6 is strange, data could be represented better.
  • Also see: IEEE-1300786 (pdf) Ascertaining the importance of neurons to develop better brain-machine interfaces Also by Justin Sanchez.

____References____

Sanchez, J.C. and Erdogmus, D. and Nicolelis, M.A.L. and Wessberg, J. and Principe, J.C. Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface Neural Systems and Rehabilitation Engineering, IEEE Transactions on 13 2 213 -219 (2005)

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ref: Bassett-2009.07 tags: Weinberger congnitive efficiency beta band neuroimagaing EEG task performance optimization network size effort date: 12-28-2011 20:39 gmt revision:1 [0] [head]

PMID-19564605[0] Cognitive fitness of cost-efficient brain functional networks.

  • Idea: smaller, tighter networks are correlated with better task performance
    • working memory task in normal subjects and schizophrenics.
  • Larger networks operate with higher beta frequencies (more effort?) and show less efficient task performance.
  • Not sure about the noisy data, but v. interesting theory!

____References____

[0] Bassett DS, Bullmore ET, Meyer-Lindenberg A, Apud JA, Weinberger DR, Coppola R, Cognitive fitness of cost-efficient brain functional networks.Proc Natl Acad Sci U S A 106:28, 11747-52 (2009 Jul 14)

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ref: Loewenstein-2006.1 tags: reinforcement learning operant conditioning neural networks theory date: 12-07-2011 03:36 gmt revision:4 [3] [2] [1] [0] [head]

PMID-17008410[0] Operant matching is a generic outcome of synaptic plasticity based on the covariance between reward and neural activity

  • The probability of choosing an alternative in a long sequence of repeated choices is proportional to the total reward derived from that alternative, a phenomenon known as Herrnstein's matching law.
  • We hypothesize that there are forms of synaptic plasticity driven by the covariance between reward and neural activity and prove mathematically that matching (alternative to reward) is a generic outcome of such plasticity
    • models for learning that are based on the covariance between reward and choice are common in economics and are used phenomologically to explain human behavior.
  • this model can be tested experimentally by making reward contingent not on the choices, but rather on the activity of neural activity.
  • Maximization is shown to be a generic outcome of synaptic plasticity driven by the sum of the covariances between reward and all past neural activities.

____References____

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ref: -0 tags: backpropagation cascade correlation neural networks date: 12-20-2010 06:28 gmt revision:1 [0] [head]

The Cascade-Correlation Learning Architecture

  • Much better - much more sensible, computationally cheaper, than backprop.
  • Units are added one by one; each is trained to be maximally correlated to the error of the existing, frozen neural network.
  • Uses quickprop to speed up gradient ascent learning.

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ref: work-0 tags: emergent leabra QT neural networks GUI interface date: 10-21-2009 19:02 gmt revision:4 [3] [2] [1] [0] [head]

I've been reading Computational Explorations in Cognitive Neuroscience, and decided to try the code that comes with / is associated with the book. This used to be called "PDP+", but was re-written, and is now called Emergent. It's a rather large program - links to Qt, GSL, Coin3D, Quarter, Open Dynamics Library, and others. The GUI itself seems obtuse and too heavy; it's not clear why they need to make this so customized / panneled / tabbed. Also, it depends on relatively recent versions of each of these libraries - which made the install on my Debian Lenny system a bit of a chore (kinda like windows).

A really strange thing is that programs are stored in tree lists - woah - a natural folding editor built in! I've never seen a programming language that doesn't rely on simple text files. Not a bad idea, but still foreign to me. (But I guess programs are inherently hierarchal anyway.)

Below, a screenshot of the whole program - note they use a Coin3D window to graph things / interact with the model. The colored boxes in each network layer indicate local activations, and they update as the network is trained. I don't mind this interface, but again it seems a bit too 'heavy' for things that are inherently 2D (like 2D network activations and the output plot). It's good for seeing hierarchies, though, like the network model.

All in all looks like something that could be more easily accomplished with some python (or ocaml), where the language itself is used for customization, and not a GUI. With this approach, you spend more time learning about how networks work, and less time programming GUIs. On the other hand, if you use this program for teaching, the gui is essential for debugging your neural networks, or other people use it a lot, maybe then it is worth it ...

In any case, the book is very good. I've learned about GeneRec, which uses different activation phases to compute local errors for the purposes of error-minimization, as well as the virtues of using both Hebbian and error-based learning (like GeneRec). Specifically, the authors show that error-based learning can be rather 'lazy', purely moving down the error gradient, whereas Hebbian learning can internalize some of the correlational structure of the input space. You can look at this internalization as 'weight constraint' which limits the space that error-based learning has to search. Cool idea! Inhibition also is a constraint - one which constrains the network to be sparse.

To use his/their own words:

... given the explanation above about the network's poor generalization, it should be clear why both Hebbian learning and kWTA (k winner take all) inhibitory competition can improve generalization performance. At the most general level, they constitute additional biases that place important constraints on the learning and the development of representations. Mroe specifically, Hebbian learning constrains the weights to represent the correlational structure of the inputs to a given unit, producing systematic weight patterns (e.g. cleanly separated clusters of strong correlations).

Inhibitory competition helps in two ways. First, it encourages individual units to specialize in representing a subset of items, thus parcelling up the task in a much cleaner and more systematic way than would occur in an otherwise unconstrained network. Second, inhibition greatly restricts the settling dynamics of the network, greatly constraining the number of states the network can settle into, and thus eliminating a large proportion of the attractors that can hijack generalization.."

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ref: work-0 tags: neural networks course date: 09-01-2009 04:24 gmt revision:0 [head]

http://www.willamette.edu/~gorr/classes/cs449/intro.html -- descent resource, good explanation of the equations associated with artificial neural networks.

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ref: Oskoei-2008.08 tags: EMG pattern analysis classification neural network date: 04-07-2009 21:10 gmt revision:2 [1] [0] [head]

  • EMG pattern analysis and classification by Neural Network
    • 1989!
    • short, simple paper. showed that 20 patterns can accurately be decoded with a backprop-trained neural network.
  • PMID-18632358 Support vector machine-based classification scheme for myoelectric control applied to upper limb.
    • myoelectric discrimination with SVM running on features in both the time and frequency domain.
    • a survace MES (myoelectric sensor) is formed via the superposition of individual action potentials generated by irregular discharges of active motor units in a muscle fiber. It's amplitude, variance, energy, and frequency vary depending on contration level.
    • Time domain features:
      • Mean absolute value (MAV)
      • root mean square (RMS)
      • waveform length (WL)
      • variance
      • zero crossings (ZC)
      • slope sign changes (SSC)
      • William amplitude.
    • Frequency domain features:
      • power spectrum
      • autoregressive coefficients order 2 and 6
      • mean signal frequency
      • median signal frequency
      • good performance with just RMS + AR2 for 50 or 100ms segments. Used a SVM with a RBF kernel.
      • looks like you can just get away with time-domain metrics!!

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ref: -0 tags: alopex machine learning artificial neural networks date: 03-09-2009 22:12 gmt revision:0 [head]

Alopex: A Correlation-Based Learning Algorithm for Feed-Forward and Recurrent Neural Networks (1994)

  • read the abstract! rather than using the gradient error estimate as in backpropagation, it uses the correlation between changes in network weights and changes in the error + gaussian noise.
    • backpropagation requires calculation of the derivatives of the transfer function from one neuron to the output. This is very non-local information.
    • one alternative is somewhat empirical: compute the derivatives wrt the weights through perturbations.
    • all these algorithms are solutions to the optimization problem: minimize an error measure, E, wrt the network weights.
  • all network weights are updated synchronously.
  • can be used to train both feedforward and recurrent networks.
  • algorithm apparently has a long history, especially in visual research.
  • the algorithm is quite simple! easy to understand.
    • use stochastic weight changes with a annealing schedule.
  • this is pre-pub: tables and figures at the end.
  • looks like it has comparable or faster convergence then backpropagation.
  • not sure how it will scale to problems with hundreds of neurons; though, they looked at an encoding task with 32 outputs.

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ref: Pearlmutter-2009.06 tags: sleep network stability learning memory date: 02-05-2009 19:21 gmt revision:1 [0] [head]

PMID-19191602 A New Hypothesis for Sleep: Tuning for Criticality.

  • Their hypothesis: in the course of learning, the brain's networks move closer to instability, as the process of learning and information storage requires that the network move closer to instability.
    • That is, a perfectly stable network stores no information: output is the same independent of input; a highly unstable network can potentially store a lot of information, or be a very selective or critical system: output is highly sensitive to input.
  • Sleep serves to restore the stability of the network by exposing it to a variety of inputs, checking for runaway activity, and adjusting accordingly. (inhibition / glia? how?)
  • Say that when sleep is not possible, an emergency mechanism must com into play, namely tiredness, to prevent runaway behavior.
  • (From wikipedia:) a potentially serious side-effect of many antipsychotics is that they tend to lower a individual's seizure threshold. Recall that removal of all dopamine can inhibit REM sleep; it's all somehow consistent, but unclear how maintaining network stability and being able to move are related.

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ref: notes-0 tags: two-photon laser imaging fluorescence lifetime imaging FRET GFP RFP date: 01-21-2008 17:23 gmt revision:0 [head]

images/538_1.pdf

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ref: bookmark-0 tags: open source cellphone public network date: 11-13-2007 21:28 gmt revision:2 [1] [0] [head]

http://dotpublic.istumbler.net/

  • kinda high-level, rather amorphous, but generally in the right direction. The drive is there, the time is coming, but we are not quite there yet..
  • have some designs for wireless repeaters, based on 802.11g mini-pci cards in a SBC, 3 repeaters. total cost about $1000
  • also interesting: http://www.opencellphone.org/index.php?title=Main_Page

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ref: bookmark-0 tags: book information_theory machine_learning bayes probability neural_networks mackay date: 0-0-2007 0:0 revision:0 [head]

http://www.inference.phy.cam.ac.uk/mackay/itila/book.html -- free! (but i liked the book, so I bought it :)

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ref: bookmark-0 tags: neural_networks machine_learning matlab toolbox supervised_learning PCA perceptron SOM EM date: 0-0-2006 0:0 revision:0 [head]

http://www.ncrg.aston.ac.uk/netlab/index.php n.b. kinda old. (or does that just mean well established?)

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ref: bookmark-0 tags: Numenta Bayesian_networks date: 0-0-2006 0:0 revision:0 [head]

http://www.numenta.com/Numenta_HTM_Concepts.pdf

  • shared, hierarchal representation reduces memory requirements, training time, and mirrors the structure of the world.
  • belief propagation techniques force the network into a set of mutually consistent beliefs.
  • a belief is a form of spatio-temporal quantization: ignore the unusual.
  • a cause is a persistent or recurring structure in the world - the root of a spatiotemporal pattern. This is a simple but important concept.
    • HTM marginalize along space and time - they assume time patterns and space patterns, not both at the same time. Temporal parameterization follows spatial parameterization.

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ref: bookmark-0 tags: Bayes Baysian_networks probability probabalistic_networks Kalman ICA PCA HMM Dynamic_programming inference learning date: 0-0-2006 0:0 revision:0 [head]

http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html very, very good! many references, well explained too.

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ref: bookmark-0 tags: training neural_networks with kalman filters date: 0-0-2006 0:0 revision:0 [head]

with the extended kalman filter, from '92: http://ftp.ccs.neu.edu/pub/people/rjw/kalman-ijcnn-92.ps

with the unscented kalman filter : http://hardm.ath.cx/pdf/NNTrainingwithUnscentedKalmanFilter.pdf