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[0] Won DS, Wolf PD, A simulation study of information transmission by multi-unit microelectrode recordings.Network 15:1, 29-44 (2004 Feb)

[0] Soetedjo R, Fuchs AF, Complex spike activity of purkinje cells in the oculomotor vermis during behavioral adaptation of monkey saccades.J Neurosci 26:29, 7741-55 (2006 Jul 19)

[0] Bair W, Koch C, Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey.Neural Comput 8:6, 1185-202 (1996 Aug 15)[1] Shmiel T, Drori R, Shmiel O, Ben-Shaul Y, Nadasdy Z, Shemesh M, Teicher M, Abeles M, Neurons of the cerebral cortex exhibit precise interspike timing in correspondence to behavior.Proc Natl Acad Sci U S A 102:51, 18655-7 (2005 Dec 20)[2] Mainen ZF, Sejnowski TJ, Reliability of spike timing in neocortical neurons.Science 268:5216, 1503-6 (1995 Jun 9)

[0] Wood F, Fellows M, Donoghue J, Black M, Automatic spike sorting for neural decoding.Conf Proc IEEE Eng Med Biol Soc 6no Issue 4009-12 (2004)

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ref: -0 tags: NMDA spike hebbian learning states pyramidal cell dendrites date: 10-03-2018 01:15 gmt revision:0 [head]

PMID-20544831 The decade of the dendritic NMDA spike.

  • NMDA spikes occur in the finer basal, oblique, and tuft dendrites.
  • Typically 40-50 mV, up to 100's of ms in duration.
  • Look similar to cortical up-down states.
  • Permit / form the substrate for spatially and temporally local computation on the dendrites that can enhance the representational or computational repertoire of individual neurons.

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ref: Chestek-2009.09 tags: BMI problems address critique spike sorting Shenoy date: 01-23-2013 02:23 gmt revision:3 [2] [1] [0] [head]

IEEE-5332822 (pdf) Neural prosthetic systems: Current problems and future directions

  • Where there is unlikely to be improvements: spike sorting and spiking models.
  • Where there are likely to be dramatic improvements: non-stationarity of recorded waveforms, limitations of a linear mappings between neural activity and movement kinematics, and the low signal to noise ratio of the neural data.
  • Compare different sorting methods: threshold, single unit, multiunit, relative to decoding.
  • Plot waveform changes over an hour -- this contrasts with earlier work (?) {1032}
  • Figure 5: there is no obvious linear transform between neural activity and the kinematic parameters.
  • Suggest that linear models need to be replaced by the literature of how primates actually make reaches.
  • Discuss that offline performance is not at all the same as online; in the latter the user can learn and adapt on the fly!

____References____

Chestek, C.A. and Cunningham, J.P. and Gilja, V. and Nuyujukian, P. and Ryu, S.I. and Shenoy, K.V. Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE 3369 -3375 (2009)

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ref: -0 tags: filtering spike sorting AUC ROC r date: 08-08-2012 23:35 gmt revision:12 [11] [10] [9] [8] [7] [6] [head]

A frequent task in the lab is to sort spikes (extracellular neural action potentials) from background noise. In the lab we are working on doing this wirelessly; to minimize power consumption, spike sorting is done before the radio. In this way only times of spikes need be transmitted, saving bandwidth and power. (This necessitates a bidirectional radio protocol, but this is a worthy sacrifice).

In most sorting programs (e.g. Plexon), the raw signal is first thresholded, then waveform snippets (typically 32 samples long) are compared to a template to accept/reject them, or to sort them into different units. The comparison metric is usually the mean-squared error, MSE, aka the L2 norm. This makes sense, as the spike shapes are assumed to be stereotyped (they may very well not be), and the noise white / uncorrelated (another debatable assumption).

On the headstage we are working with for wireless neural recording, jumps and memory moves are expensive operations, hence we've elected to do no waveform extraction, and instead match continuously match. By using the built-in MPEG compression opcodes, we can compute the L1 norm at a rate of 4 samples / clock -- very efficient. However, this was more motivated by hardware considerations an not actual spike sorting practice. Literature suggests that for isolating a fixed-pattern signal embedded in noise, the best solution is instead a matched filter.

Hence, a careful study of spike-sorting was attempted in matlab, given the following assumptions: fixed spike shape (this was extracted from real data), and uncorrelated band-limited noise. The later was just white noise passed through a bandpass filter, e.g.

cheby1(3, 2, [500/15e3 7.5/15])

Where the passband edges are 500 Hz and 15kHz, at a sampling rate of 30kHz. (Actual rate is 31.25kHz). Since the spike times are known, we can rigorously compare the Receiver Operating Characteristic (ROC) and the area under curve (AUC) for different sorting algorithms. Four were tried: L1 (as mentioned above, motivated by the MPEG opcodes), L2 (Plexon), FIR matched filter, and IIR matched filter.

The latter was very much an experiment -- IIR filters are efficiently implemented on the blackfin processor, and they generally require fewer taps than their equivalent FIR implementation. To find an IIR equivalent to a given FIR matched filter (whose impulse response closely looks like the actual waveshape, just time-reversed), the filter parameters were simply optimized to match the two impulse responses. To facilitate the search, the denominator was specified in terms of complex conjugate pole locations (thereby constraining the form of the filter), while the numerator coefficients were individually optimized. Note that this is not optimizing given the objective to maximize sorting quality -- rather, it is to make the IIR filter impulse response as close as possible to the FIR matched filter, hence computationally light.

And yet: the IIR filter outperforms the FIR matched filter, even though the IIR filter has 1/3 the coefficients (10 vs 32)! Below is the AUC quality metric for the four methods.

And here are representative ROC curves at varying spike SNR ratios.

The remarkable thing is that even at very low SNR, the matched IIR filter can reliably sort cells from noise. (Note that the acceptable false positive here should be weighted more highly; in the present analysis true positive and false positive are weighted equally, which is decidedly non-Bayesian given most of the time there is no spike.) The matched IIR filter is far superior to the normal MSE to template / L2 norm method -- seems we've been doing it wrong all along?

As for reliably finding spikes / templates / filters when the SNR < 0, the tests above - which assume an equal number of spike samples and non-spike samples -- are highly biased; spikes are not normally sortable when the SNR < 0.


Upon looking at the code again, I realized three important things:

  1. The false positive rate need to be integrated over all time where there is no spike, just the same as the true positive is over all time where there is a spike.
  2. All methods need to be tested with 'distractors', or other spikes with a different shape.
  3. The FIR matched filter was backwards!

Including #1 above, as expected, dramatically increased the false positive rate, which is to be expected and how the filters will be used in the real world. #2 did not dramatically impact any of the discriminators, which is good. #3 alleviated the gap between the IIR and FIR filters, and indeed the FIR matched filter performance now slightly exceeds the IIR matched filer.

Below, AUC metric for 4 methods.

And corresponding ROC for 6 different SNR ratios (note the SNRs sampled are slightly different, due to the higher false positive rate).

One thing to note: as implemented, the IIR filter requires careful matching of poles and zeros, and is may not work with 1.15 fixed-point math on the Blackfin. The method really deserves to be tested in vivo, which I shall do shortly.


More updates:

See www.aicit.org/jcit/ppl/JCIT0509_05.pdf -- they add an 'adjustment' function to the matched filter due to variance in the amplitude of spikes, which adds a little performance at low SNRs.

F(t)=[x(t)kσe˙ 1x(t)kσ] n

Sigma is the standard deviation of x(t), n and k determine 'zoom intensity and zoom center'. The paper is not particularly well written - there are some typos, and their idea seems unjustified. Still the references are interesting:

  • IEEE-238472 (pdf) Optimal detection, classification, and superposition resolution in neural waveform recordings.
    • Their innovation: whitening filter before template matching, still use L2 norm.
  • IEEE-568916 (pdf) Detection, classification, and superposition resolution of action potentials in multiunit single-channel recordings by an on-line real-time neural network
    • Still uses thresholding / spike extraction and L2 norm. Inferior!
  • IEEE-991160 (pdf) Parameter estimation of human nerve C-fibers using matched filtering and multiple hypothesis tracking
    • They use a real matched filter to detect extracellular action potentials.


Update: It is not to difficult to convert FIR filters to IIR filters using simple numerical optimization. Within my client program, this is done using simulated annealing; have tested this using fminsearch in matlab. To investigate the IIR-filter fitting problem more fully, I sliced the 10-dimensional optimization space along pairs of dimensions about the optimum point as found using fminsearch.

The parameters are as follows:

  1. Two poles, stored as four values (a real and imaginary part for each pole pair). These are expanded to denominator coefficients before evaluating the IIR filter.
  2. Five numerator coeficients.
  3. One delay coefficient (to match the left/right shift).

The figure below plots the +-1 beyond the optimum for each axis pair. Click for full resolution image. Note that the last parameter is discrete, hence steps in the objective function. Also note that the problem is perfectly quadratic for the numerator, as expected, which is why LMS works so well.

Note that for the denominator pole locations, the volume of the optimum is small, and there are interesting features beyond this. Some spaces have multiple optima.

The next figure plots +-0.1 beyond the optimum for each axis vs. every other one. It shows that, at least on a small scale, the problem becomes very quadratic in all axes hence amenable to line or conjugate gradient search.

Moving away from planes that pass through a found optima, what does the space look like? E.g. From a naive start, how hard is it to find at least one workable solution? To test this, I perturbed the found optimum with white noise in the parameters std 0.2, and plotted the objective function as before, albeit at higher resolution (600 x 600 points for each slice).

These figures show that there can be several optima in the denominator, but again it appears that a very rough exploration followed by gradient descent should arrive at an optima.

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ref: -0 tags: spike sorting variational bayes PCA Japan date: 04-04-2012 20:16 gmt revision:1 [0] [head]

PMID-22448159 Spike sorting of heterogeneous neuron types by multimodality-weighted PCA and explicit robust variational Bayes.

  • Cutting edge windowing-then-sorting method.
  • projection multimodality-weighted principal component analysis (mPCA, novel).
    • Multimodality of a feature is by checking the informativeness using the KS test of a given feature.
  • Also investigate graph laplacian features (GLF), which projects high-dimensional data onto a low-dimensional space while preserving topological structure.
  • Clustering based on variational Bayes for Student's T mixture model (SVB).
    • Does not rely on MAP inference and works reliably over difficult-to sort data, e.g. bursting neurons and sparsely firing neurons.
  • Wavelet preprocessing improves spike separation.
  • open-source, available at http://etos.sourceforge.net/

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ref: Sodagar-2009.09 tags: ASIC recording Najafi spike sorting date: 01-15-2012 22:07 gmt revision:4 [3] [2] [1] [0] [head]

IEEE-5226763 (pdf) An Implantable 64-Channel Wireless Microsystem for Single-Unit Neural Recording

  • Spike sorting (thresholding) on 64 channels, 8 bit digitization, 62.5 ks/sec, 60dB gain, 14.4 mW at 1.8V.
  • 1.4 by 1.55 cm.

____References____

Sodagar, A.M. and Perlin, G.E. and Ying Yao and Najafi, K. and Wise, K.D. An Implantable 64-Channel Wireless Microsystem for Single-Unit Neural Recording Solid-State Circuits, IEEE Journal of 44 9 2591 -2604 (2009)

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ref: Vibert-1979.08 tags: spike sorting recording depth extracellular glass electrodes active feedback original date: 01-15-2012 06:46 gmt revision:3 [2] [1] [0] [head]

PMID-95711[0] Spike separation in multiunit records: A multivariate analysis of spike descriptive parameters

  • Glass coated tungsten microeletrodes have high capacitance; they compensate for this by spraying colloidal silver over the outside sheath of the glass, insulating that with varnish, and driving the shield in a positive-feedback way (stabillized in some way?) This negates the capacitance. 'low impedance capacitance compensated'.
    • Capacitance compensation really matters!!
  • Were able to record from single units for 40-100um range (average: 50um) with SNRs 2:1 to 7:1.
    • Some units had SNRs that could reach 15:1 (!!!), these could be recorded for 600 um of descent.
    • more than 3 units could usually be recognized at each recording point by visual inspection of the oscilloscope, and in some cases up to 6 units could be distinguished
    • Is there some clever RF way of neutralizing the capacitance of everything but the electrode tip? Hmm. Might as well try to minimize it.
  • Bandpass 300 Hz - 10 kHz.
  • When the signal crossed the threshold level, it was retained and assumed to be a spike if the duration of the first component was between 70 and 1000 us.
    • This 70 us lower limit was determined on a preliminary study as a fairly good rise time threshold for separation of fiber spikes from somatic or dendritic spikes.
    • I really need to do some single electrode recordings. Platt?
  • Would it be possible to implement this algorithm in realtime on the DSP?
  • Describe clustering based on PCA.
  • Programming this computer (PDP-12) must have been crazy!
  • They analyzed 20k spikes. Mango gives billions.
  • First principal component (F1) represented 60-65% of total information was based mostly on amplitude
  • Second principal component, 15-20% of total information represented mainly time parameters.
  • Suggested 3 parameters: Vmax, Vmin, and T3 (time from max to min).
  • Maybe they don't know what they are talking about:

____References____

[0] Vibert JF, Costa J, Spike separation in multiunit records: a multivariate analysis of spike descriptive parameters.Electroencephalogr Clin Neurophysiol 47:2, 172-82 (1979 Aug)

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ref: Schmidt-1984.11 tags: spike sorting Schmidt date: 01-15-2012 05:45 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-6392757[0] Instruments for sorting neuroelectric data: a review

  • Seems like it would be useful for me :-)
  • Amplifier bandwidth should be between 5 and 7.5kHz
  • High-pass between 100 and 600Hz, to reduce the 'baseline hash produced by the firing of distant neurons.
  • Electrodes generate Johnson noise (same as thermal noise): E=0.1219R×BμVrms , where B = bandwidth in Hz and R = resistance in MOhm.
  • Modern low-noise FET amplifiers produce noise equivalent to a source resistance of 15K
  • Describe a number of nonlinear spike detection filters using switched amplifiers; these do not seem to have survived.
  • Analog window comparators described have been largely replaced with digital filtering techniques.
    • That said, the use of photo-detectors taped to an oscilloscope is an ingenious method for spike discrimination!
  • Note that audio output is useful, too. Ear is a good discriminator.

____References____

[0] Schmidt EM, Instruments for sorting neuroelectric data: a review.J Neurosci Methods 12:1, 1-24 (1984 Nov)

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ref: Guillory-1999.09 tags: recording spike sorting Utah date: 01-15-2012 05:32 gmt revision:2 [1] [0] [head]

PMID-10522821[0] A 100-channel system for real time detection and storage of extracellular spike waveforms.

  • Large, non-wireless, 100 channel recording system.
  • Spike snippet extraction
  • Base 5 multiplexing (??)
  • 1uv input-referred noise.
  • also 88 instructions per sample with their 66Mhz DSP.
  • Windows GUI. all of this much like my work, actually. except not wireless.
  • 1999. hard to remember that a 200 Mhz PC was state of the art back then (!!)

____References____

[0] Guillory KS, Normann RA, A 100-channel system for real time detection and storage of extracellular spike waveforms.J Neurosci Methods 91:1-2, 21-9 (1999 Sep 15)

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ref: Wyler-1980.05 tags: operant control motor learning interspike intervals ISI Wyler Lange Neafsey Robbins date: 01-07-2012 21:46 gmt revision:1 [0] [head]

PMID-6769536[0] Operant control of precentral neurons: Control of modal interspike intervals

  • Question: can monkeys control the ISI of operantly controlled neurons?
    • Answer: Seems they cannot. Operant and overt movement cells have about the same ISI, and this cannot be changed by conditioning.
  • Task requires a change from tonic to phasic firing, hence they call it "Differential reinforcement of Tonic Patterns".
    • That is, the monkey is trained to produce spikes within a certain ISI window.
    • PDP8 control, applesauce feedback.
    • modal ISI, in this case, means mode (vs. mean and median) of the ISI.
  • Interesting: "It was not uncommon for a neuron to display bi- or trimodal ISI distributions when the monkey was engaged in a movement unrelated to a unit's firing"
  • For 80% of the units, the more tightly a neuron's firing was related to a specific movement, the more gaussian its ISI became.
  • As the monkey gained control over reinforced units, the ISI became more gaussian.
  • Figure 2: monkey was not able to significantly change the modal ISI.
    • Monkeys instead seem to succeed at the task by decreasing the dispersion of the ISI distribution and increasing the occurrence of the modal ISI.
  • Monkeys mediate response through proprioceptive feedback:
    • Cervical spinal cord sectioning decreases the fidelity of control.
    • When contralateral C5-7 ventral roots were sectioned, PTN responsive to passive arm movements could not be statistically controlled.
    • Thus, monkeys operantly control precentral neurons through peripheral movements, perhaps even small and isometric contractions.
  • Excellent paper. Insightful conclusions.

____References____

[0] Wyler AR, Lange SC, Neafsey EJ, Robbins CA, Operant control of precentral neurons: control of modal interspike intervals.Brain Res 190:1, 29-38 (1980 May 19)

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ref: BMI notes-0 tags: spike filtering rate_estimation BME 265 Henriquez date: 01-06-2012 03:06 gmt revision:1 [0] [head]

http://hardm.ath.cx:88/pdf/BME265_final.pdf

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ref: Brown-2007 tags: Kalman filter BMI Black spike_sorting Donoghue date: 01-06-2012 00:07 gmt revision:1 [0] [head]

From Uncertain Spikes to Prosthetic Control a powerpoint presentation w/ good overview of all that the Brown group has done

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ref: Schmidt-1984.12 tags: Schmidt spike sorting PCA date: 12-20-2011 23:34 gmt revision:1 [0] [head]

PMID-6396456[0] Computer separation of multi-unit neuroelectric data: a review

  • goes through the standard, by then well-established ideas: template matching, PCA, spike amplitude, peak-to-peak amplitude, Fourier analysis, curve fitting, spike area, rms value.
  • These are all useful features, though template matching seems the standard now..
  • Gerstein and Clark 1964 -- stored spikes on tape, then sampled the tape until a threshold was exceeded. 32 samples of the waveform around threshold crossing were stored for analysis on the computer; up to 7000 points could be saved.
  • also looked at cross-correlation of a spike with a template -- back in 1968 on a LINC-8!
  • Reviews a good number of other very clever spike sorting techniques for using the lmiited hardware available.
  • Talk about template realignment and resampling Mambrito and De Luca 1983

____References____

[0] Schmidt EM, Computer separation of multi-unit neuroelectric data: a review.J Neurosci Methods 12:2, 95-111 (1984 Dec)

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ref: Won-2004.02 tags: Debbie Won Wolf spike sorting mutual information tuning BMI date: 12-07-2011 02:58 gmt revision:3 [2] [1] [0] [head]

PMID-15022843[0] A simulation study of information transmission by multi-unit microelectrode recordings key idea:

  • when the units on a single channel are similarly tuned, you don't loose much information by grouping all spikes as coming from one source. And the opposite effect is true when you have very differently tuned neurons on the same channel - the information becomes more ambiguous.

____References____

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ref: Soetedjo-2006.07 tags: cerebellum purkinje cells complex spike saccade date: 12-09-2008 18:46 gmt revision:2 [1] [0] [head]

PMID-16855102[0] Complex spike activity of purkinje cells in the oculomotor vermis during behavioral adaptation of monkey saccades.

  • central conclusion: that change in complex spike rate correlates with the sign of scaccade error, but not the magnitude.
  • analysis is far more complicated than what this conclusion seems to require, though ... or maybe it is just too late for me.

____References____

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ref: Bair-1996.08 tags: precise spike timing cortex behavior Sejnowski date: 04-09-2007 00:57 gmt revision:0 [head]

PMID-8768391[0] Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey

  • This temporal modulation is stimulus dependent, being present for highly dynamic random motion but absent when the stimulus translates rigidly -- that is, the response is markedly reproducable and precise to a few milliseconds.

PMID-16339894[1] Neurons of the cerebral cortex exhibit precise interspike timing in correspondence to behavior.

  • in the cortex, spikes can be very precise.
  • this was a slice investigation.

PMID-7770778[2] Reliability of spike timing in neocortical neurons.

  • neocortex of rats
  • suggest low intrinsic noise level in spike generation, allowing accurate transformation of synaptic input into spike generation

____References____

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ref: Wood-2004.01 tags: spikes sorting BMI Black Donoghue prediction kalman date: 04-06-2007 21:57 gmt revision:2 [1] [0] [head]

PMID-17271178[0] automatic spike sorting for neural decoding

  • idea: select the number of units (and, indeed, clustering) based on the ability to predict a given variable. makes sense!
  • results:
    • human sorting: 13,5 cm^2 MSE
    • automatic spike sorting: 11.4 cm^2 MSE
      • yes, I know, the increase is totally dramatic.
  • they do not say if this could be implemented in realtime or not. hence, probably not.

____References____

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ref: neuro-0 tags: spike sorting dendogram distance matrix Stapleton date: 04-02-2007 15:07 gmt revision:4 [3] [2] [1] [0] [head]

Friday March 30 Jen shared an interesting algorithm for spike sorting:

dist=pdist(psi); %This finds the Euclidean distances for all of the points (waveforms) in psi;
                  %dist is of the form of a row vector of length m(m-1)/2. Could convert into a 
                  %distance matrix via squareform function, but is computationally inefficient.
                  %m is the number of waveforms in psit.

link=linkage(dist); %This performs a nearest neighbor linkage on the distance matrix and returns
                    %a matrix of size (m-1)x3. Cols 1 and 2 contain the indices of the objects
                    %were linked in pairs to form a new cluster. This new cluster is assigned the 
                    %index value m+i. There are m-1 higher clusters that correspond to the interior
                    %nodes of the hierarchical cluster tree. Col 3 contains the corresponding linkage 
                    %distances between the objects paired in the clusters at each row i.

[H,T]=dendrogram(link,0); %This creates a dendrogram; 0 instructs the function to plot all nodes in 
                          %the tree. H is vector of line handles, and T a vector of the cluster 
                          %number assignment for each waveform in psit.

It looks real nice in theory, and computes very quickly on 2000 x 32 waveform data (provided you don't want to plot) -- however, I'm not sure if it works properly on synthetic data. Here are the commands that i tried:

v = [randn(1000, 32); (randn(1000, 32) + rvecrep(ones(1,32),1000))];
[coef, vec] = pca(v);
vv = v * vec(:, 1:2);
dist = pdist(vv);
link = linkage(dist);
[H,T]=dendrogram(link,0);
figure
DensityPlotOpenGL(vv(:,1), vv(:,2))

-- the fitted dendogram, without PCA

-- the fitted dendogram, with PCA

-- the asociated PCA plot of the data, clearly showing two clusters.

need to figure out how jen made the colorized plots

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ref: Kerr-2004.01 tags: UP_DOWN states striatum cortex spike timing date: 0-0-2007 0:0 revision:0 [head]

PMID-14749432 Action Potential Timing Determines Dendritic Calcium during Striatal Up-States

  • striatum has up/down states too!
  • only read the abstract.

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ref: bookmark-0 tags: spiking neuron models learning SRM spike response model date: 0-0-2006 0:0 revision:0 [head]

http://diwww.epfl.ch/~gerstner/SPNM/SPNM.html

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ref: notes-0 tags: spike patterns neural response LGN spike_timing Sejnowski vision date: 0-0-2006 0:0 revision:0 [head]

http://www.jneurosci.org/cgi/reprint/24/12/2989.pdf

  • quote: " when a cortical neuron is repeatedly injected with the same fluctuating current stimulus, the timing of the spikes is highly precise from trial to trial and the spike pattern appears to be unique"
    • though: I'd imagine that somebody has characterized the actual transfer function of this.
  • mais: we conclude that the prestimulus history of a neuron may influence the precise timing of the spikes in repsonse to a stimulus over a wide range of time scales.
  • in vivo, it is hard to find patterns because neurons may jump between paterns & there is a large ammount of neuronal noise in there too. or there may be neural "attractors".
  • they observed long-term (seconds) firing patterns in cat LGN (interesting)

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ref: notes bookmarks-0 tags: spike sorting bayes spectral_analysis date: 0-0-2006 0:0 revision:0 [head]

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ref: notes, bookmark-0 tags: spikes action_potentials neurons subthreshold depolarization c.elegans date: 0-0-2006 0:0 revision:0 [head]

"Millisecond-timescale, genetically targeted optical control of neural activity" http://www.nature.com/neuro/journal/v8/n9/full/nn1525.html

what they did:

  • expressed ChR2 receptor in cultured hippocampal neurons.
  • ChR2 is a rapidly-gated light-sensitive cation channel recently isolated from unicellular green alga
  • cells were transfected via lentivirus
  • caused spiking in cells by exposing them to 5-15ms flashes of blue light.
  • stimulation was reliable to 30hz
  • stimulated spikes had low jitter - 2ms or so.
  • light stimulation protocol was robust across different neurons.
  • expression of the light-gated channel did not alter the properties of the neurons or their health etc.
  • they think it might be applicable to in-vivo mamalian studies!
  • Subthreshold!
    • for many cellular and systems neuroscience processes subthreshold depolarizations convey physiologically significant information.
    • the neurons in c.elegans do not spike!
    • subthreshold depolarizations are potent for activating synapes-to-nucleus signaling
    • the relative timing of subthreshold and suprathreshold depolarizations can determine the direction of synaptic plasticity.
    • subthreshold depolarizations operate in the more linear regime of membrane voltage