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[0] Gage GJ, Ludwig KA, Otto KJ, Ionides EL, Kipke DR, Naive coadaptive cortical control.J Neural Eng 2:2, 52-63 (2005 Jun)

[0] Jackson A, Mavoori J, Fetz EE, Correlations between the same motor cortex cells and arm muscles during a trained task, free behavior, and natural sleep in the macaque monkey.J Neurophysiol 97:1, 360-74 (2007 Jan)

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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: -0 tags: betzig lattice light sheet date: 09-18-2019 18:32 gmt revision:0 [head]

PMID-25342811 Lattice Light Sheet Microscopy: Imaging Molecules to Embryos at High Spatiotemporal Resolution

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ref: -0 tags: ETPA entangled two photon absorption Goodson date: 09-18-2019 04:16 gmt revision:2 [1] [0] [head]

Rough fluorescence yield calculation

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 . 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; 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). About that source -- for a BBO crystal, circa 1998 experimenters were getting 1e4 photons / sec / mW. So, 2.3e172.3e17 pairs / sec would require 23 Gw. Right.

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.

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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: -2012 tags: cortex striatum learning carmena costa basal ganglia date: 09-13-2019 18:30 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-22388818 Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills.

  • Trained a mouse to control an auditory cursor, as in Kipke's task {99}. Did not cite that paper, claimed it was 'novel'. oops.
  • Summed neuronal firing rate of groups of 2 or 4 M1 neurons.
  • Auditory feedback was essential for the operant learning.
    • One group increased the frequency with increased firing rate; the other decreased tone with increasing FR.
  • Specific deletion of striatal NMDA receptors impairs the ability to learn neuroprosthetic skills.
    • Hence, they argue, cortico-striatal plastciity is required to learn abstract skills, such as this tone to firing rate target acquisition task.
  • Controlled by recording EMG of the vibrissae + injection of lidocane into the whisker pad.
  • One reward was sucrose solution; the other was a food pellet. When the rat was satiated on one modality, they showed increased preference for the opposite reward during BMI control -- thereby demonstrating intentionality. Clever!.
  • Noticed pronounced oscillatory spike coupling, the coherence of which was increased in low-frequency bands in late learning relative to early learning (figure 3).
  • Genetic manipulations: knockin line that expresses Cre recombinase in both striatonigral and striatopallidal medium spiny neurons, crossed with mice carrying a floxed allele of the NMDAR1 gene.
    • These animals are relatively normal, and can learn to perform rapid sequential movements, but are unable to learn precise motor sequences.
    • Acute pharmacological blockade of NMDAR did not affect performance of the neuroprosthetic skill.
    • Hence the deficits in the transgenic mice are due to an inability to perform the skill.

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ref: Gage-2005.06 tags: naive coadaptive control Kalman filter Kipke audio BMI date: 09-13-2019 02:33 gmt revision:2 [1] [0] [head]

PMID-15928412[0] Naive coadaptive Control May 2005. see notes

____References____

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ref: Jackson-2007.01 tags: Fetz neurochip sleep motor control BMI free behavior EMG date: 09-13-2019 02:21 gmt revision:4 [3] [2] [1] [0] [head]

PMID-17021028[0] Correlations Between the Same Motor Cortex Cells and Arm Muscles During a Trained Task, Free Behavior, and Natural Sleep in the Macaque Monkey

  • used their implanted "neurochip" recorder that recorded both EMG and neural activity. The neurochip buffers data and transmits via IR offline. It doesn't have all that much flash onboard - 16Mb.
    • used teflon-insulated 50um tungsten wires.
  • confirmed that there is a strong causal relationship, constant over the course of weeks, between motor cortex units and EMG activity.
    • some causal relationships between neural firing and EMG varied dependent on the task. Additive / multiplicative encoding?
  • this relationship was different at night, during REM sleep, though (?)
  • point out, as Todorov did, that Stereotyped motion imposes correlation between movement parameters, which could lead to spurrious relationships being mistaken for neural coding.
    • Experiments with naturalistic movement are essential for understanding innate, untrained neural control.
  • references {597} Suner et al 2005 as a previous study of long term cortical recordings. (utah probe)
  • during sleep, M1 cells exhibited a cyclical patter on quiescence followed by periods of elevated activity;
    • the cycle lasted 40-60 minutes;
    • EMG activity was seen at entrance and exit to the elevated activity period.
    • during periods of highest cortical activity, muscle activity was completely suppressed.
    • peak firing rates were above 100hz! (mean: 12-16hz).

____References____

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ref: -2019 tags: Kleinfeld Harris record every neuron date: 09-13-2019 01:51 gmt revision:0 [head]

PMID-31495645 Can One Concurrently Record Electrical Spikes from Every Neuron in a Mammalian Brain?

  • Argues for a concrete arrangement of 6um diamond (1.2TPa modulus) shanks, 2mm long, on 40um hexagonal grid. Each would be patterned with 5 layers of metal, 30nm x 30nm Au traces (what about surface roughness?), high dielectric insulation, 9um x 14um TiN contacts.
  • This will be mated to state of the art adaptive amplifiers, which would be biased to only burn necessary power needed to sort spikes.
  • The sharpened spikes should penetrate the brain; 4um diameter diamond shanks should also work...
  • Overall volume displacement ~ 2% (which still seems high).
  • Suggest that the shanks can push capillaries out of the way, or puncture them while making a seal. Clearly, that's possible ...
  • ... but realistically, unless these are inserted glacially slowly, it will cause possibly catastrophic / cascading inflammation. (Which can spread on the order of 100-150um).
  • Does not cite Marblestone 2013.

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ref: -0 tags: swept field confocal date: 09-12-2019 20:01 gmt revision:1 [0] [head]

PMID-22831554 Swept field laser confocal microscopy for enhanced spatial and temporal resolution in live-cell imaging.

  • Invented by Marvin Minsky back in 1955 memoir!
  • Idea is not unlike light-sheet imaging -- sweep a confocal slit and laser line across a sample, rather than a pinhole and point, respectively.
  • This results in lower phototoxicity, but still reasonable rejection of out-of-focus light compared to widefield imaging.

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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: -2007 tags: photobleaching GFP date: 09-10-2019 01:42 gmt revision:1 [0] [head]

PMID-17179937 Major signal increase in fluorescence microscopy through dark-state relaxation (2007)

  • 5-25x increase in fluorescence yields.
  • Idea: allow the (dark) triplet states to decay naturally by keeping inter-pulse intervals of illumination greater than 1us.
  • Works for both 1p and 2p.
  • For volume imaging via 2p, I don’t think that 1um decay time is much of an issue; revisit given fluorophores after >1ms!
  • Suggests again that transition from triplet dark state to excited higher state is a prominent or significant cause of photobleaching; also suggests that triple quenching will have limited utility in scanned or pulsed 2p systems (will have more utility in 1p systems, perhaps..)
  • Atto532 dye has low intersystem crossing to the triplet state (1%) [3,5,14] .. humm.
  • 2p total photon emission seems to flatten above 100GW/cm^2 intensity.
  • 2p absorption is easily saturated independent of pulse width: for short pulses, high intensity leads to absorption to T1 state, which has high cross-section to the Tn>1 state; longer pulses give more time for single-photon absorption.
  • τp by m = 200 and hence the pulse energy by 14-fold does not have a considerable effect on G2p. This obviously indicates that the saturation of the S0 → S1 or of the T1 → Tn > 1 excitation eliminates any dependence on pulse peak intensity or energy.

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ref: -0 tags: computational neuroscience opinion tony zador konrad kording lillicrap date: 07-30-2019 21:04 gmt revision:0 [head]

Two papers out recently in Arxive and Biorxiv:

  • A critique of pure learning: what artificial neural networks can learn from animal brains
    • Animals learn rapidly and robustly, without the need for labeled sensory data, largely through innate mechanisms as arrived at and encoded genetically through evolution.
    • Still, this cannot account for the connectivity of the human brain, which is much to large for the genome; with us, there are cannonical circuits and patterns of intra-area connectivity which act as the 'innate' learning biases.
    • Mice and men are not so far apart evolutionary. (I've heard this also from people FIB-SEM imaging cortex) Hence, understanding one should appreciably lead us to understand the other. (I agree with this sentiment, but for the fact that lab mice are dumb, and have pretty stereotyped behaviors).
    • References Long short term memory and learning to learn in networks of spiking neurons -- which claims that a hybrid algorithm (BPTT with neuronal rewiring) with realistic neuronal dynamics markedly increases the computational power of spiking neural networks.
  • What does it mean to understand a neural network?
    • As has been the intuition with a lot of neuroscientists probably for a long time, posits that we have to investigate the developmental rules (wiring and connectivity, same as above) plus the local-ish learning rules (synaptic, dendritic, other .. astrocytic).
      • The weights themselves, in either biological neural networks, or in ANN's, are not at all informative! (Duh).
    • Emphasizes the concept of compressability: how much information can be discarded without impacting performance? With some modern ANN's, 30-50x compression is possible. Authors here argue that little compression is possible in the human brain -- the wealth of all those details about the world are needed! In other words, no compact description is possible.
    • Hence, you need to learn how the network learns those details, and how it's structured so that important things are learned rapidly and robustly, as seen in animals (very similar to above).

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ref: -0 tags: python timelapse script date: 07-30-2019 20:45 gmt revision:3 [2] [1] [0] [head]

Edited Terrence Eden's script to average multiple frames when producing a time-lapse video from a continuous video. Frames are averaged together before decimation, rather than pure decimation, as with ffmpeg. Produces appealing results on subjects like water. Also, outputs a video directly, without having to write individual images.

python
#!/usr/bin/python
import cv2
import sys

#   Video to read
print str(sys.argv[1])
vidcap = cv2.VideoCapture(sys.argv[1])

#   Which frame to start from, how many frames to go through
start_frame = 0
frames = 61000

#   Counters
count = 0
save_seq = 0
decimate = 10
rolling = 16 # average over N output frames
transpose = False

if(transpose):
	h = vidcap.get(3)
	w = vidcap.get(4)
else:
	w = vidcap.get(3)
	h = vidcap.get(4)

fourcc = cv2.VideoWriter_fourcc(*'mp4v')
writer = cv2.VideoWriter("timelapse.mp4", fourcc, 30, (int(w), int(h)), True)

avglist = []

while True:
	#   Read a frame
	success,image = vidcap.read()
	if not success:
		break
	if count > start_frame+frames:
		break
	if count >= start_frame:
		if (count % decimate == 0):
			#   Extract the frame and convert to float
			avg = image.astype('uint16') # max 255 frames averaged. 
		if (count % decimate > 0 and count % decimate <= (decimate-1)):
			avg = avg + image.astype('uint16')
		if (count % decimate == (decimate-1)):
			#   Every 100 frames (3 seconds @ 30fps)
			avg = avg / decimate
			if(transpose):
				avg = cv2.transpose(avg)
				avg = cv2.flip(avg, 1)
			avg2 = avg; 
			for a in avglist:
				avg2 = avg2 + a
			avg2 = avg2 / rolling; 
			avglist.append(avg); 
			if len(avglist) >= rolling:
				avglist.pop(0) # remove the first item. 
			
			avg2 = avg2.astype('uint8')
			print("saving "+str(save_seq))
			#   Save Image
			# cv2.imwrite(filename+str('{0:03d}'.format(save_seq))+".png", avg)
			save_seq += 1
			writer.write(avg2)
			if count == frames + start_frame:
				break
	count += 1
writer.release()

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ref: -2019 tags: neuromorphic optical computing date: 06-19-2019 14:47 gmt revision:1 [0] [head]

Large-Scale Optical Neural Networks based on Photoelectric Multiplication

  • Critical idea: use coherent homodyne detection, and quantum photoelectric multiplication for the MACs.
    • That is, E-fields from coherent light multiplies rather than adds within a (logarithmic) photodiode detector.
    • Other lit suggests rather limited SNR for this effect -- 11db.
  • Hence need EO modulators and OE detectors followed by nonlinearity etc.
  • Pure theory, suggests that you can compute with as few as 10's of photons per MAC -- or less! Near Landauer's limit.

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ref: -2016 tags: fluorescent proteins photobleaching quantum yield piston GFP date: 06-19-2019 14:33 gmt revision:0 [head]

PMID-27240257 Quantitative assessment of fluorescent proteins.

  • Cranfill PJ1,2, Sell BR1, Baird MA1, Allen JR1, Lavagnino Z2,3, de Gruiter HM4, Kremers GJ4, Davidson MW1, Ustione A2,3, Piston DW
  • Model bleaching as log(F)=αlog(P)+clog(F) = -\alpha log(P) + c or k bleach=bI αk_{bleach} = b I^{\alpha} where F is the fluorescence intensity, P is the illumination power, and b and c are constants.
    • Most fluorescent proteins have α\alpha > 1, which means superlinear photobleaching -- more power, bleaches faster.
  • Catalog the degree to which each protein tends to form aggregates by tagging to the ER and measuring ER morphology. Fairly thorough -- 10k cells each FP.

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ref: -2017 tags: neuromorphic optical computing nanophotonics date: 06-17-2019 14:46 gmt revision:5 [4] [3] [2] [1] [0] [head]

Progress in neuromorphic photonics

  • Similar idea as what I had -- use lasers as the optical nonlinearity.
    • They add to this the idea of WDM and 'MRR' (micro-ring resonator) weight bank -- they don't talk about the ability to change the weihts, just specify them with some precision.
  • Definitely makes the case that III-V semiconductor integrated photonic systems have the capability, in MMACs/mm^2/pj, to exceed silicon.

See also :

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ref: -2013 tags: microscopy space bandwidth product imaging resolution UCSF date: 06-17-2019 14:45 gmt revision:0 [head]

How much information does your microscope transmit?

  • Typical objectives 1x - 5x, about 200 Mpix!

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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: -2012 tags: phase change materials neuromorphic computing synapses STDP date: 06-13-2019 21:19 gmt revision:3 [2] [1] [0] [head]

Nanoelectronic Programmable Synapses Based on Phase Change Materials for Brain-Inspired Computing

  • Here, we report a new nanoscale electronic synapse based on technologically mature phase change materials employed in optical data storage and nonvolatile memory applications.
  • We utilize continuous resistance transitions in phase change materials to mimic the analog nature of biological synapses, enabling the implementation of a synaptic learning rule.
  • We demonstrate different forms of spike-timing-dependent plasticity using the same nanoscale synapse with picojoule level energy consumption.
  • Again uses GST germanium-antimony-tellurium alloy.
  • 50pJ to reset (depress) the synapse, 0.675pJ to potentiate.
    • Reducing the size will linearly decrease this current.
  • Synapse resistance changes from 200k to 2M approx.

See also: Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element

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ref: -0 tags: optical gain media lasers cross section dye date: 06-13-2019 15:13 gmt revision:2 [1] [0] [head]

Eminently useful. Source: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-974-fundamentals-of-photonics-quantum-electronics-spring-2006/lecture-notes/chapter7.pdf

Laser Dye technology by Peter Hammond

  • This paper is another great resource!
  • Lists the stimulated emission cross-section for Rhodamine-6G as 4e-16 @ 550nm, consistent with the table above.
  • At a (high) concentration of 2mMol (1 g/l), 1/e penetration depth is 20um.
    • Depending on the solvent, there may be aggregation and stacking / quenching.
  • Tumbling time of Rhodamine 6G in ethanol is 20 to 300ps; fluorescence lifetime in oscillators is 10's of ps, so there is definitely polarization sensitive amplification.
  • Generally in dye lasers, the emission cross-section must be higher than the excited state absorption, σ eσ \sigma_e - \sigma^\star most important.
  • Bacteria can actually subsist on rhodamine-similar sulfonated dyes in aqueous solutions! Wow.