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ref: -2013 tags: synaptic learning rules calcium harris stdp date: 02-18-2021 19:48 gmt revision:3 [2] [1] [0] [head]

PMID-24204224 The Convallis rule for unsupervised learning in cortical networks 2013 - Pierre Yger  1 , Kenneth D Harris

This paper aims to unify and reconcile experimental evidence of in-vivo learning rules with  established STDP rules.  In particular, the STDP rule fails to accurately predict change in strength in response to spike triplets, e.g. pre-post-pre or post-pre-post.  Their model instead involves the competition between two time-constant threshold circuits / coincidence detectors, one which controls LTD and another LTP, and is such an extension of the classical BCM rule.  (BCM: inputs below a threshold will weaken a synapse; those above it will strengthen. )

They derive the model from optimization criteria that neurons should try to optimize the skewedness of the distribution of their membrane potential: much time spent either firing spikes or strongly inhibited.  This maps to a objective function F that looks like a valley - hence the 'convallis' in the name (latin for valley); the objective is differentiated to yield a weighting function for weight changes; they also add a shrinkage function (line + heaviside function) to gate weight changes 'off' at resting membrane potential. 

A network of firing neurons successfully groups correlated rate-encoded inputs, better than the STDP rule.  it can also cluster auditory inputs of spoken digits converted into cochleogram.  But this all seems relatively toy-like: of course algorithms can associate inputs that co-occur.  The same result was found for a recurrent balanced E-I network with the same cochleogram, and convalis performed better than STDP.   Meh.

Perhaps the biggest thing I got from the paper was how poorly STDP fares with spike triplets:

Pre following post does not 'necessarily' cause LTD; it's more complicated than that, and more consistent with the two different-timeconstant coincidence detectors.  This is satisfying as it allows for apical dendritic depolarization to serve as a contextual binding signal - without negatively impacting the associated synaptic weights. 

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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: Harris-1998.08 tags: noise wolpert harris motor planning Fitt velocity variance control theory date: 01-27-2013 22:33 gmt revision:1 [0] [head]

PMID-9723616[0] Signal-dependent noise determines motor planning.

  • We present a unifying theory of eye and arm movements based on the single physiological assumption that the neural control signals are corrupted by noise whose variance increases with the size of the control signal
    • Poisson noise? (I have not read the article -- storing here for future reference.)
  • This minimum-variance theory accurately predicts the trajectories of both saccades and arm movements and the speed-accuracy trade-off described by Fitt's law.


[0] Harris CM, Wolpert DM, Signal-dependent noise determines motor planning.Nature 394:6695, 780-4 (1998 Aug 20)

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ref: Harris-2011.12 tags: mechanically adaptive electrodes implants case western dissolving flexible histology Harris date: 01-25-2013 01:39 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-22049097[0] Mechanically adaptive intracortical implants improve the proximity of neuronal cell bodies.

  • See also [1]
  • Initial tensile modulus of 5GPa dropped to 12MPa. (almost 500-fold!)
    • Their polymer nanocomposite (NC) still swells 65-70% (with water?)
    • Implant size 100 x 200um.
  • Controlled with tungsten of identical size and coating.
  • Tethered to skull.
  • Interesting:
    • The neuronal nuclei density within 100 µm of the device at four weeks post-implantation was greater for the compliant nanocomposite compared to the stiff wire.
    • At eight weeks post-implantation, the neuronal nuclei density around the nanocomposite was maintained, but the density around the wire recovered to match that of the nanocomposite.
    • Hypothesis, in discussion: softer implants are affecting the time-course of the response rather that final results
  • The glial scar response to the compliant nanocomposite was less vigorous than it was to the stiffer wire
  • Cultured astrocytes have been shown to respond to mechanical stimuli via calcium signaling (Ostrow and Sachs, 2005).
  • Substrate stiffness is also known to shift cell differentiation in mesenchymal stem cells to be neurogenic, myogenic, or osteogenic (Engler et al., 2006).
  • In vivo studies which focus on the effects of electrode tethering have shown that untethered implants reduce the extent of the glial scar (Biran et al., 2007; Kim et al., 2004; Subbaroyan, 2007)
  • Parylene, polymide, and PDMS still each have moduli 6 orders of mangitude larger than that of the brain.
  • In some of their plots, immune response is higher around the nanocomposites!
    • Could be that their implant is still too large / stiff?
  • Note that recent research shows that vitemin may have neuroprotective effects --
    • Research has linked vimentin expression to rapid neurite extension in response to damage (Levin et al., 2009)
    • NG2+ cells that express vimentin have been proposed to support repair of central nervous system (CNS) damage, and stabilize axons in response to dieback from ED1+ cells (Alonso, 2005; Nishiyama, 2007; Busch et al., 2010)
  • Prior work (Frampton et al., 2010 PMID-20336824[2]) hypothesizes that a more compact GFAP response increases the impedance of an electrode which may decrease the quality of electrode recordings.


[0] Harris JP, Capadona JR, Miller RH, Healy BC, Shanmuganathan K, Rowan SJ, Weder C, Tyler DJ, Mechanically adaptive intracortical implants improve the proximity of neuronal cell bodies.J Neural Eng 8:6, 066011 (2011 Dec)
[1] Harris JP, Hess AE, Rowan SJ, Weder C, Zorman CA, Tyler DJ, Capadona JR, In vivo deployment of mechanically adaptive nanocomposites for intracortical microelectrodes.J Neural Eng 8:4, 046010 (2011 Aug)
[2] Frampton JP, Hynd MR, Shuler ML, Shain W, Effects of glial cells on electrode impedance recorded from neuralprosthetic devices in vitro.Ann Biomed Eng 38:3, 1031-47 (2010 Mar)

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ref: Du-2011.01 tags: Harrison recording electrode MEA Blanche date: 01-04-2013 02:43 gmt revision:3 [2] [1] [0] [head]

PMID-22022568[0] Multiplexed, High Density Electrophysiology with Nanofabricated Neural Probes

  • The number of single-units possible to record doubles every 7 years [5].
  • Electrodes must be within 100um of soma to relaibly detect extracellular action potentials.
  • Existing Michigan arrays have trace features around >=1 um; here they use E-beam lithography to decrease the probe width dramatically.
    • Their wire widths are 290 nm. Still bigger than 40nm process (?)
  • Seem to use Reid Harrison's ASIC RHA22132 design.
  • noise of electrodes progressively decreased with consecutive gold electroplating cycles. Plating makes the electrodes rough, and decreases their impedance to around 1 M.
    • Electrode contacts are around 10 x 10 um square, 108 um^2 area.
  • Intrinsic noise of the amplifier 1.7 uV RMS.
  • 290 nm wire had an impedance of 9.2 k -- corresponding to 1.0 uV rms noise.
  • able to record from the same neuron from several adjacent electrodes. Spacing ~ 28 um.
  • Detail their process extensively -- 40% of probes survived the process with <= 5 defective channels. THey propose further optimization to the e-beam lithography. Probes took 7 hours to pattern on the lithography machine (!).


[0] Du J, Blanche TJ, Harrison RR, Lester HA, Masmanidis SC, Multiplexed, high density electrophysiology with nanofabricated neural probes.PLoS One 6:10, e26204 (2011)

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ref: Harrison-2003.06 tags: CMOS amplifier headstage electrophysiology neural_recording low_power chopper Reid Harrison date: 01-16-2012 04:43 gmt revision:12 [11] [10] [9] [8] [7] [6] [head]

IEEE-1201998 (pdf) A low-power low-noise CMOS amplifier for neural recording applications

  • detail novel MOS-bipolar pseudoresistor element to permit amplification of low-frequency signals down to milihertz range.
  • 80 microwatt spike amplifier in 0.16mm^2 silicon with 1.5 um CMOS, 1 microwatt EEG amplifier
  • input-referred noise of 2.2uV RMS.
  • has a nice graph comparing the power vs. noise for a number of other published designs
  • i doubt the low-frequency amplification really matters for neural recording, though certainly it matters for EEG.
    • they give an equation for the noise efficiency factor (NEF), as well as much detailed background.
    • NEF better than any prev. reported. Theoretical limit is 2.9 for this topology; they measure 4.8
  • does not compare well to Medtronic amp: http://www.eetimes.com/news/design/showArticle.jhtml?articleID=197005915
    • 2 microwatt! @ 1.8V
    • chopper-stabilized
    • not sure what they are going to use it for - the battery will be killed it it has to telemeter anything!
    • need to find the report for this.
  • tutorial on chopper-stabilized amplifiers -- they have nearly constant noise v.s. frequency, and very low input/output offset.
  • References: {1056} Single unit recording capabilities of a 100 microelectrode array. Nordhausen CT, Maynard EM, Normann RA.
  • [5] see {1041}
  • [9] {1042}
  • [12] {1043}

Harrison, R.R. and Charles, C. A low-power low-noise CMOS amplifier for neural recording applications Solid-State Circuits, IEEE Journal of 38 6 958 - 965 (2003)

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ref: Harrison-2009.08 tags: low power ASIC wireless neural recording Reid Harrison Shenoy date: 01-03-2012 00:55 gmt revision:2 [1] [0] [head]

IEEE-5061585 (pdf) Wireless Neural Recording With Single Low-Power Integrated Circuit

  • 100 channels, with threshold spike extraction.
  • 900Mhz FSK transmit coil.
  • Inductive power and data link.


Harrison, R.R. and Kier, R.J. and Chestek, C.A. and Gilja, V. and Nuyujukian, P. and Ryu, S. and Greger, B. and Solzbacher, F. and Shenoy, K.V. Wireless Neural Recording With Single Low-Power Integrated Circuit Neural Systems and Rehabilitation Engineering, IEEE Transactions on 17 4 322 -329 (2009)

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ref: Harris-2008.03 tags: retroaxonal retrosynaptic Harris learning cortex backprop date: 12-07-2011 02:34 gmt revision:2 [1] [0] [head]

PMID-18255165[0] Stability of the fittest: organizing learning through retroaxonal signals

  • the central hypothesis: strengthening of a neuron's output synapses stabilizes recent changes in the same neuron's inputs.
    • this causes representations (as are arrived at with backprop) that are tuned to task features.
  • Retroaxonal signaling in the brain is too slow for an instructive (says at least the sign of the error wrt a current neuron's output) backprop algorithm
  • hence, retroaxonal signals are not instructive but selective.
  • At SFN Harris was looking for people to test this in a model; as (yet) unmodeled and untested, I'm suspicious of it.
  • Seems plausible, yet it also just seems to be a way of moving the responsibility for learning computation to the postsynaptic neuron (which is then propagated back to the present neuron). The theory does not immediately suggest what neurons are doing to learn their stuff; rather how they may be learning.
    • If this stabilization is based on some sort of feedback (attention? reward?), which may guide learning (except for the cortex, which does not have many (any?) DA receptors...), then I may be more willing to accept it.
    • It seems likely that the cortex is doing a lot of unsupervised learning: predicting what sensory info will come next based on present sensory info (ICA, PCA).


[0] Harris KD, Stability of the fittest: organizing learning through retroaxonal signals.Trends Neurosci 31:3, 130-6 (2008 Mar)

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ref: notes-0 tags: neuroscience ion channels information coding John Harris date: 01-07-2008 16:46 gmt revision:4 [3] [2] [1] [0] [head]

  • crazy idea: that neurons have a number of ion channel lines which can be selectively activated. That is, information is transmitted by longitudial transmission channels which are selectively activated based on the message that is transmitted
  • has any evidence for such a fine structure been found?? I think not, due to binding studies, but who knows..
  • dude uses historical references (Neumann) to back up his ideas. I find these sorts of justifications interesting, but not logically substantiative. Do not talk about the opinions of old philosophers (exclusively, at least), talk about their data.
  • interesting story about holography & the holograph of Dennis Gabor.
    • he does make interesting analogies to neuroscience & the importance of preserving spatial phase.
  • fourier images -- neato.
conclusion: interesting, but a bit cooky.