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ref: -0 tags: neuronal assemblies maass hebbian plasticity simulation austria fMRI date: 10-27-2020 21:39 gmt revision:0 [head]

PMID-32381648 A model for structured information representation in neural networks in the brain

  • Using randomly connected E/I networks, suggests that information can be "bound" together using fast Hebbian STDP.
  • That iss 'assemblies' in higher-level areas reference sensory information through patterns of bidirectional connectivity.
  • These patterns emerge spontaneously following disinihbition of the higher-level areas.
  • Find the results underwhelming, but the discussion is more interesting.
    • E.g. there have been a lot of theoretical and computational-experimental work for how concepts are bound together into symbols or grammars
    • Actually find the referenced fMRI studies interesting, too: they imply that you can observe the results of structural binding in activity of the superior tempporal gyrus.
  • I'm more in favor of dendritic potentials or neuronal up/down states to be a fast and flexible way of maintaining 'sumbol membership' --
    • But it's not as flexible as synaptic plasticity, which, obviously, populates the outer product between 'region a' and 'region b' with a memory substrate, thereby spanning the range of plausible symbol-bindings.
    • Inhibitory interneurons can then gate the bindings, per morphological evidence
    • But but, I don't think anyone has shown that you need protein synthesis for perception, as you do for LTP (modulo AMPAR cycling).
      • Hence I'd argue that localized dendritic potentials can serve as the flexible outer-product 'memory tag' for presence in an assembly.
        • Or maybe they are used primarily for learning, who knows!

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ref: Maass-2002.11 tags: Maass liquid state machine expansion LSM Markram computation cognition date: 12-06-2011 07:17 gmt revision:2 [1] [0] [head]

PMID-12433288[0] Real-time computing without stable states: a new framework for neural computation based on perturbations.

  • It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks.
    • Stable states, e.g. Turing machines and attractor-based networks are not requried!
    • How does this compare to Shenoy's result that neuronal dynamics converge to a 'stable' point just before movement?


[0] Maass W, Natschl├Ąger T, Markram H, Real-time computing without stable states: a new framework for neural computation based on perturbations.Neural Comput 14:11, 2531-60 (2002 Nov)

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ref: Legenstein-2008.1 tags: Maass STDP reinforcement learning biofeedback Fetz synapse date: 04-09-2009 17:13 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-18846203[0] A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback

  • (from abstract) The resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP.
    • This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker.
  • STDP is prevalent in the cortex ; however, it requires a second signal:
    • Dopamine seems to gate STDP in corticostriatal synapses
    • ACh does the same or similar in the cortex. -- see references 8-12
  • simple learning rule they use: d/dtW ij(t)=C ij(t)D(t) d/dt W_{ij}(t) = C_{ij}(t) D(t)
  • Their notes on the Fetz/Baker experiments: "Adjacent neurons tended to change their firing rate in the same direction, but also differential changes of directions of firing rates of pairs of neurons are reported in [17] (when these differential changes were rewarded). For example, it was shown in Figure 9 of [17] (see also Figure 1 in [19]) that pairs of neurons that were separated by no more than a few hundred microns could be independently trained to increase or decrease their firing rates."
  • Their result is actually really simple - there is no 'control' or biofeedback - there is no visual or sensory input, no real computation by the network (at least for this simulation). One neuron is simply reinforced, hence it's firing rate increases.
    • Fetz & later Schimdt's work involved feedback and precise control of firing rate; this does not.
    • This also does not address the problem that their rule may allow other synapses to forget during reinforcement.
  • They do show that exact spike times can be rewarded, which is kinda interesting ... kinda.
  • Tried a pattern classification task where all of the information was in the relative spike timings.
    • Had to run the pattern through the network 1000 times. That's a bit unrealistic (?).
      • The problem with all these algorithms is that they require so many presentations for gradient descent (or similar) to work, whereas biological systems can and do learn after one or a few presentations.
  • Next tried to train neurons to classify spoken input
    • Audio stimului was processed through a cochlear model
    • Maass previously has been able to train a network to perform speaker-independent classification.
    • Neuron model does, roughly, seem to discriminate between "one" and "two"... after 2000 trials (each with a presentation of 10 of the same digit utterance). I'm still not all that impressed. Feels like gradient descent / linear regression as per the original LSM.
  • A great many derivations in the Methods section... too much to follow.
  • Should read refs:
    • PMID-16907616[1] Gradient learning in spiking neural networks by dynamic perturbation of conductances.
    • PMID-17220510[2] Solving the distal reward problem through linkage of STDP and dopamine signaling.


[0] Legenstein R, Pecevski D, Maass W, A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback.PLoS Comput Biol 4:10, e1000180 (2008 Oct)
[1] Fiete IR, Seung HS, Gradient learning in spiking neural networks by dynamic perturbation of conductances.Phys Rev Lett 97:4, 048104 (2006 Jul 28)
[2] Izhikevich EM, Solving the distal reward problem through linkage of STDP and dopamine signaling.Cereb Cortex 17:10, 2443-52 (2007 Oct)