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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: -2016 tags: 6-OHDA parkinsons model warren grill simulation date: 05-10-2016 23:30 gmt revision:4 [3] [2] [1] [0] [head]

PMID-26867734 A biophysical model of the cortex-basal ganglia-thalamus network in the 6-OHDA lesioned rat model of Parkinson’s disease

  • Kumaravelu K1, Brocker DT1, Grill WM
  • Background: Although animal models (6-OHDA rats, MPTP mk) are rendered parkinsonian by a common mechanism (loss of dopaminergic neurons), there is considerable variation in the neuronal activity underlying the pathophysiology, including differences in firing rates, firing patterns, responses to cortical stimulation, and neuronal synchronization across different basal ganglia (BG) structures (Kita and Kita 2011;Nambu et al. 2000).
    • Yep. Highly idiopathic disease.
    • Claim there are good models of the MPTP monkey:
      • PMID-20309620 Modeling shifts in the rate and pattern of subthalamopallidal network activity during deep brain stimulation.
      • PMID-22805068 Network effects of subthalamic deep brain stimulation drive a unique mixture of responses in basal ganglia output.
  • Biophysical model of the cortex - basal ganglia - thalamus circuit
    • Hodgkin-Huxley type.
      • Single compartment neurons.
    • Validated by comparing responses of the BG to CTX stimulation.
    • Details, should they be important:
      • Each rCortex (regularly spiking) neuron
        • excitatory input from one TH neuron
        • inhibitory input from four randomly selected iCortex neurons.
        • Izhikevich model.
      • Each iCortex (fast inhibitory) neuron
        • excitatory input from four randomly selected rCortex neurons.
      • Each dStr (direct, D1/D5, ex) neuron
        • excitatory input from one rCortex neuron
        • inhibitory axonal collaterals from three randomly selected dStr neurons.
      • Each idStr (indirect, D2, inhib) neuron
        • excitatory input from one rCortex neuron
        • inhibitory axonal collaterals from four randomly selected idStr neurons.
      • Each STN neuron
        • inhibitory input from two GPe neurons
        • excitatory input from two rCortex neurons.
        • DBS modeled as a somatic current.
      • Each GPe neuron
        • inhibitory axonal collaterals from any two other GPe neurons
        • inhibitory input from all idStr neurons.
      • Each GPi neuron
        • inhibitory input from two GPe neurons
        • inhibitory input from all dStr neurons.
      • Some GPe/GPi neurons receive
        • excitatory input from two STN neurons,
        • while others do not.
      • Each TH neuron receives inhibitory input from one GPi neuron.
  • Diseased state:
    • Loss of striatal dopamine is accompanied by an increase in acetylcholine levels (Ach) in the Str (Ikarashi et al. 1997)
      • This results in a reduction of M-type potassium current in both the direct and indirect MSNs. (2.6 -> 1.5)
    • Dopamine loss results in reduced sensitivity of direct Str MSN to cortical stimulation (Mallet et al. 2006)
      • corticostriatal synaptic conductance from 0.07 to 0.026
    • Striatal dopamine depletion causes an increase in the synaptic strength of intra-GPe axonal collaterals resulting in aberrant GPe firing (Miguelez et al. 2012)
      • Increase from 0.125 to 0.5.
  • Good match to experimental rats:
  • Ok, so this is a complicated model (they aim to be the most complete to-date). How sensitive is it to parameter perturbations?
    • Noticeable ~20 Hz oscillations in BG in PD condition
    • ~9 Hz in STN & GPi.
  • And how well do the firing rates match experiment?
    • Not very. Look at the error bars.
  • What does DBS (direct current injection into STN neurons) do?
    • Se d,e,f: stochastic parameter; g,h,i: (semi) stochastic wiring.
  • Another check: NMDA antagonist into STN suppressed STN beta band oscillations in 6-OHDA lesioned rats (Pan et al. 2014).
    • Analysis of model GPi neurons revealed that episodes of beta band oscillatory activity interrupted alpha oscillatory activity in the PD state (Fig. 9a, b), consistent with experimental evidence that episodes of tremor-related oscillations desynchronized beta activity in PD patients (Levy et al. 2002).
  • What does DBS, at variable frequencies, do oscillations in the circuit?
  • How might this underly a mechanism of action?

Overall, not a bad paper. Not very well organized, which is not assisted by the large amount of information presented, but having slogged through the figures, I'm somewhat convinced that the model is good. This despite my general reservations of these models: the true validation would be to have it generate actual behavior (and learning)!

Lacking this, the approximations employed seem like a step forward in understanding how PD and DBS work. The results and discussion are consistent with {1255}, but not {711}, which found that STN projections from M1 (not the modulation of M1 projections to GPi, via efferents from STN) truly matter.

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ref: Lee-2005.12 tags: micromotion silicon michigan array simulation strain date: 01-28-2013 03:13 gmt revision:1 [0] [head]

PMID-16317231[0] Biomechanical analysis of silicon microelectrode-induced strain in the brain.

  • Simulation.
  • Our analysis demonstrates that when physical coupling between the electrode and the brain increases, the micromotion-induced strain of tissue around the electrode decreases as does the relative slip between the electrode and the brain.
  • Argue that micromotion and shear cause lost recording sensitivity due to inflammation and astroglial scarring around the electrode.
    • This seems to be the scientific consensus ATM.

____References____

[0] Lee H, Bellamkonda RV, Sun W, Levenston ME, Biomechanical analysis of silicon microelectrode-induced strain in the brain.J Neural Eng 2:4, 81-9 (2005 Dec)

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ref: Holgado-2010.09 tags: DBS oscillations beta globus pallidus simulation computational model date: 02-22-2012 18:36 gmt revision:4 [3] [2] [1] [0] [head]

PMID-20844130[0] Conditions for the Generation of Beta Oscillations in the Subthalamic Nucleus–Globus Pallidus Network

  • Modeled the globus pallidus external & STN; arrived at criteria in which the system shows beta-band oscillations.
    • STN is primarily glutamergic and projects to GPe (along with many other areas..)
      • STN gets lots of cortical afferent, too.
    • GPe is GABAergic and projects profusely back to STN.
    • This inhibition leads to more accurate choices.
      • (Frank, 2006 PMID:,
        • The present [neural network] model incorporates the STN and shows that by modulating when a response is executed, the STN reduces premature responding and therefore has substantial effects on which response is ultimately selected, particularly when there are multiple competing responses.
        • Increased cortical response conflict leads to dynamic adjustments in response thresholds via cortico-subthalamic-pallidal pathways.
        • the model accounts for the beneficial effects of STN lesions on these oscillations, but suggests that this benefit may come at the expense of impaired decision making.
        • Not totally convinced -- impulsivity is due to larger network effects. Delay in conflict situations is an emergent property, not localized to STN.
      • Frank 2007 {1077}.
  • Beta band: cite Boraud et al 2005.
  • Huh parameters drawn from Misha's work, among others + Kita 2004, 2005.
    • Striatum has a low spike rate but high modulation? Schultz and Romo 1988.
  • In their model there are a wide range of parameters (bidirectional weights) which lead to oscillation
  • In PD the siatum is hyperactive in the indirect path (Obeso et al 2000); their model duplicates this.

____References____

[0] Holgado AJ, Terry JR, Bogacz R, Conditions for the generation of beta oscillations in the subthalamic nucleus-globus pallidus network.J Neurosci 30:37, 12340-52 (2010 Sep 15)

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ref: -0 tags: conjecture entropy simulation date: 03-25-2011 18:16 gmt revision:0 [head]

Conjecture: the entropic cost of simulating a system (complex evolved or not) in a system (e.g. machine) versus physical reality is proportional to the information content of original system * energy per bit of simulation * time to simulate / KL divergence between simulation and reality, where information content KL divergence are calculated within the space of measured dimensions.

Am I missing terms? Or rather: what terms am I missing? :-)