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ref: -2013 tags: larkum calcium spikes dendrites association cortex binding date: 02-23-2021 19:52 gmt revision:3 [2] [1] [0] [head]

PMID-23273272 A cellular mechanism for cortical associations: and organizing principle for the cerebral cortex

  • Distal tuft dendrites have a second spike-initiation zone, where depolarization can induce a calcium plateau of up to 50ms long.  This depolarization can cause multiple spikes in the soma, and can be more effective at inducing spikes than depolarization through the basal dendrites.  Such spikes are frequently bursts of 2-4 at 200hz. 
  • Bursts of spikes can also be triggered by backpropagation activated calcium (BAC), which can half the current threshold for a dendritic spike. That is, there is enough signal propagation for information to propagate both down the dendritic arbor and up, and the two interact non-linearly.  
  • This nonlinear calcium-dependent association pairing can be blocked by inhibition to the dendrites (presumably apical?). 
    • Larkum argues that the different timelines of GABA inhibition offer 'exquisite control' of the dendrites; but these sorts of arguments as to computational power always seem lame compared to stating what their actual role might be. 
  • Quote: "Dendritic calcium spikes have been recorded in vivo [57, 84, 85] that correlate to behavior [78, 86].  The recordings are population-level, though, and do not seem to measure individual dendrites (?). 

See also:

PMID-25174710 Sensory-evoked LTP driven by dendritic plateau potentials in vivo

  • We demonstrate that rhythmic sensory whisker stimulation efficiently induces synaptic LTP in layer 2/3 (L2/3) pyramidal cells in the absence of somatic spikes.
  • It instead depends on NMDA-dependent dendritic spikes.
  • And this is dependent on afferents from the POm thalamus.

And: The binding solution?, a blog post covering Bittner 2015 that looks at rapid dendritic plasticity in the hippocampus as a means of binding stimuli to place fields.

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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: -2014 tags: dopamine medium spiny neurons calcium STDP PKA date: 01-07-2020 03:43 gmt revision:2 [1] [0] [head]

PMID-25258080 A critical time window for dopamine actions on the structural plasticity of dendritic spines

  • Remarkably short time window for dopamine to modulate / modify (aggressive) STDP protocol.
  • Showed with the low-affinity calcium indicator Fluo4-FF that peak calcium concentrations in spines is not affected by optogenetic stimulation of dopamine fibers.
  • However, CaMKII activity is modulated by DA activity -- when glutamate uncaging and depolarization was followed by optogenetic stimulation of DA fibers followed, the FRET sensor Camui-CR reported significant increases of CaMKII activity.
  • This increase was abolished by the application of DRAPP-32 inhibiting peptide, which blocks the interaction of dopamine and cAMP-regulated phospoprotein - 32kDa (DRAPP-32) with protein phosphatase 1 (PP-1)
    • Spine enlargement was induced in the absence of optogenetic dopamine when PP-1 was inhibited by calculin A...
    • Hence, phosphorylation of DRAPP-32 by PKA inhibits PP-1 and disinihibts CaMKII. (This causal inference seems loopy; they reference a hippocampal paper, [18])
  • To further test this, they used a FRET probe of PKA activity, AKAR2-CR. This sensor showed that PKA activity extends throughout the dendrite, not just the stimulated spine, and can respond to DA release directly.

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ref: -2017 tags: calcium imaging seeded iterative demixing light field microscopy mouse cortex hippocampus date: 02-13-2019 22:44 gmt revision:1 [0] [head]

PMID-28650477 Video rate volumetric Ca2+ imaging across cortex using seeded iterative demixing (SID) microscopy

  • Tobias Nöbauer, Oliver Skocek, Alejandro J Pernía-Andrade, Lukas Weilguny, Francisca Martínez Traub, Maxim I Molodtsov & Alipasha Vaziri
  • Cell-scale imaging at video rates of hundreds of GCaMP6 labeled neurons with light-field imaging followed by computationally-efficient deconvolution and iterative demixing based on non-negative factorization in space and time.
  • Utilized a hybrid light-field and 2p microscope, but didn't use the latter to inform the SID algorithm.
  • Algorithm:
    • Remove motion artifacts
    • Time iteration:
      • Compute the standard deviation versus time (subtract mean over time, measure standard deviance)
      • Deconvolve standard deviation image using Richardson-Lucy algo, with non-negativity, sparsity constraints, and a simulated PSF.
      • Yields hotspots of activity, putative neurons.
      • These neuron lcoations are convolved with the PSF, thereby estimating its ballistic image on the LFM.
      • This is converted to a binary mask of pixels which contribute information to the activity of a given neuron, a 'footprint'
        • Form a matrix of these footprints, p * n, S 0S_0 (p pixels, n neurons)
      • Also get the corresponding image data YY , p * t, (t time)
      • Solve: minimize over T ||YST|| 2|| Y - ST||_2 subject to T0T \geq 0
        • That is, find a non-negative matrix of temporal components TT which predicts data YY from masks SS .
    • Space iteration:
      • Start with the masks again, SS , find all sets O kO^k of spatially overlapping components s is_i (e.g. where footprints overlap)
      • Extract the corresponding data columns t it_i of T (from temporal step above) from O kO^k to yield T kT^k . Each column corresponds to temporal data corresponding to the spatial overlap sets. (additively?)
      • Also get the data matrix Y kY^k that is image data in the overlapping regions in the same way.
      • Minimize over S kS^k ||Y kS kT k|| 2|| Y^k - S^k T^k||_2
      • Subject to S k>=0S^k >= 0
        • That is, solve over the footprints S kS^k to best predict the data from the corresponding temporal components T kT^k .
        • They also impose spatial constraints on this non-negative least squares problem (not explained).
    • This process repeats.
    • allegedly 1000x better than existing deconvolution / blind source segmentation algorithms, such as those used in CaImAn