m8ta
You are not authenticated, login.
text: sort by
tags: modified
type: chronology
{1568}
hide / / print
ref: -2021 tags: burst bio plausible gradient learning credit assignment richards apical dendrites date: 05-05-2022 15:44 gmt revision:2 [1] [0] [head]

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

  • Roughly, single-events indicate the normal feature responses of neurons, while multiple-spike bursts indicate error signals.
  • Bursts are triggered by depolarizing currents to the apical dendrites, which can be uncoupled from bottom-up event rate, which arises from perisomatic inputs / basal dendrites.
  • The fact that the two are imperfectly multiplexed is OK, as in backprop the magnitude of the error signal is modulated by the activity of the feature detector.
  • "For credit assignment in hierarchical networks, connections should obey four constraints:
    • Feedback must steer the magnitude and sign of plasticity
    • Feedback signals from higher-order areas must be multipleed with feedforward signals from lower-order areas so that credit assignment can percolate down the hierarch with minimal effect on sensory information
    • There should be some form of alignment between feedforward and feedback connections
    • Integration of credit-carrying signals should be nearly linear to avoid saturation
      • Seems it's easy to saturate the burst probability within a window of background event rate, e.g. the window is all bursts to no bursts.
  • Perisomatic inputs were short-term depressing, whereas apical dendrite synapses were short-term facilitating.
    • This is a form of filtering on burst rates? E.g. the propagate better down than up?
  • They experiment with a series of models, one for solving the XOR task, and subsequent for MNIST and CIFAR.
  • The later, larger models are mean-field models, rather than biophysical neuron models, and have a few extra features:
    • Interneurons, presumably SOM neurons, are used to keep bursting within a linear regime via a 'simple' (supplementary) learning rule.
    • Feedback alignment occurs by adjusting both the feedforward and feedback weights with the same propagated error signal + weight decay.
  • The credit assignment problem, or in the case of unsupervised learning, the coordination problem, is very real: how do you change a middle-feature to improve representations in higher (and lower) levels of the hierarchy?
    • They mention that using REINFORCE on the same network was unable to find a solution.
    • Put another way: usually you need to coordinate the weight changes in a network; changing weights individually based on a global error signal (or objective function) does not readily work...
      • Though evolution seems to be quite productive at getting the settings of (very) large sets of interdependent coefficients all to be 'correct' and (sometimes) beautiful.
      • How? Why? Friston's free energy principle? Lol.

{1532}
hide / / print
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.

{1417}
hide / / print
ref: -0 tags: synaptic plasticity 2-photon imaging inhibition excitation spines dendrites synapses 2p date: 08-14-2020 01:35 gmt revision:3 [2] [1] [0] [head]

PMID-22542188 Clustered dynamics of inhibitory synapses and dendritic spines in the adult neocortex.

  • Cre-recombinase-dependent labeling of postsynapitc scaffolding via Gephryn-Teal fluorophore fusion.
  • Also added Cre-eYFP to label the neurons
  • Electroporated in utero e16 mice.
    • Low concentration of Cre, high concentrations of Gephryn-Teal and Cre-eYFP constructs to attain sparse labeling.
  • Located the same dendrite imaged in-vivo in fixed tissue - !! - using serial-section electron microscopy.
  • 2230 dendritic spines and 1211 inhibitory synapses from 83 dendritic segments in 14 cells of 6 animals.
  • Some spines had inhibitory synapses on them -- 0.7 / 10um, vs 4.4 / 10um dendrite for excitatory spines. ~ 1.7 inhibitory
  • Suggest that the data support the idea that inhibitory inputs maybe gating excitation.
  • Furthermore, co-inervated spines are stable, both during mormal experience and during monocular deprivation.
  • Monocular deprivation induces a pronounced loss of inhibitory synapses in binocular cortex.

{1422}
hide / / print
ref: -0 tags: lillicrap segregated dendrites deep learning backprop date: 01-31-2019 19:24 gmt revision:2 [1] [0] [head]

PMID-29205151 Towards deep learning with segregated dendrites https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716677/

  • Much emphasis on the problem of credit assignment in biological neural networks.
    • That is: given complex behavior, how do upstream neurons change to improve the task of downstream neurons?
    • Or: given downstream neurons, how do upstream neurons receive ‘credit’ for informing behavior?
      • I find this a very limiting framework, and is one of my chief beefs with the work.
      • Spatiotemporal Bayesian structure seems like a much better axis (axes) to cast function against.
      • Or, it could be segregation into ‘signal’ and ‘error’ or ‘figure/ground’ based on hierarchical spatio-temporal statistical properties that matters ...
      • ... with proper integration of non-stochastic spike timing + neoSTDP.
        • This still requires some solution of the credit-assignment problem, i know i know.
  • Outline a spiking neuron model with zero one or two hidden layers, and a segregated apical (feedback) and basal (feedforward) dendrites, as per a layer 5 pyramidal neuron.
  • The apical dendrites have plateau potentials, which are stimulated through (random) feedback weights from the output neurons.
  • Output neurons are forced to one-hot activation at maximum firing rate during training.
    • In order to assign credit, feedforward information must be integrated separately from any feedback signals used to calculate error for synaptic updates (the error is indicated here with δ). (B) Illustration of the segregated dendrites proposal. Rather than using a separate pathway to calculate error based on feedback, segregated dendritic compartments could receive feedback and calculate the error signals locally.
  • Uses the MNIST database, naturally.
  • Poisson spiking input neurons, 784, again natch.
  • Derive local loss function learning rules to make the plateau potential (from the feedback weights) match the feedforward potential
    • This encourages the hidden layer -> output layer to approximate the inverse of the random feedback weight network -- which it does! (At least, the jacobians are inverses of each other).
    • The matching is performed in two phases -- feedforward and feedback. This itself is not biologically implausible, just unlikely.
  • Achieved moderate performance on MNIST, ~ 4%, which improved with 2 hidden layers.
  • Very good, interesting scholarship on the relevant latest findings ‘’in vivo’’.
  • While the model seems workable though ad-hoc or just-so, the scholarship points to something better: use of multiple neuron subtypes to accomplish different elements (variables) in the random-feedback credit assignment algorithm.
    • These small models can be tuned to do this somewhat simple task through enough fiddling & manual (e.g. in the algorithmic space, not weight space) backpropagation of errors.
  • They suggest that the early phases of learning may entail learning the feedback weights -- fascinating.
  • ‘’Things are definitely moving forward’’.

{1411}
hide / / print
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.

{893}
hide / / print
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)