Monthly Archives: July 2017

internal memory, Synaptic Plasticity

What is the most important untested computational prediction in neuroscience?

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The Organization for Computational Neuroscience has started a survey, asking people for their submissions, and here is my contribution:

Prediction: The basis for learning and memory exists primarily within the single neuron.

Rationale: (A) Dendrites/axons are adaptive, in particular the expression and contribution of ion channels adapts to use. This also extends to synaptic channels. (B) The decision on transforming a transient calcium signal into a permanent trace lies within the single neuron, within its protein signaling network and DNA readout mechanisms. The neuron’s memory traces are both use-dependent (dependent on shape and size of calcium signals received) and subject to additional internal computations, e.g. involving kinases/phosphatases, early genes, histones etc.

Remote memories are not coded by current synaptic connectivity, but internally by clusters of neurons, which become activated under certain conditions.

Conclusion: Memory research has to focus on the cellular (neuronal) basis of adaptation, synaptic connectivity will be predictable from adequate neuron models.

The statement in italics is extra. I have no papers, no references on that. For the rest, cf.

Scheler G. Regulation of neuromodulator receptor efficacy–implications for whole-neuron and synaptic plasticity. Prog Neurobiol. 2004 Apr;72(6):399-415. PMID: 15177784

Scheler G. Learning intrinsic excitability in medium spiny neurons. F1000Res. 2013 Mar 14 2:88. doi: 10.12688/f1000research.2-88.v2. eCollection 2013. PMID: 25520776

Scheler, G: Logarithmic distributions prove that intrinsic learning is Hebbian. F1000Res. 2017 (August).

 

internal memory, Neuromodulation ,

Dendritic computation

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A new paper  Universal features of dendrites through centripetal branch ordering published: July 3, 2017) shows more or less the opposite of what it cites as common wisdom: „neuronal computation is known to depend on the morphology of dendrites”

Namely, since all dendrites follow general topological principles, it is probably not the dendritic morphology that matters in a functional sense. To make a dendrite functional, i.e. let it participate in adaptive information processing, we have to refer to the ion channels and GPCRs that populate the spines and shafts and shape the generation of action potentials.

Compare:
Dendritic integration: 60 years of progress. (Stuart GJ, Spruston N.) Nat Neurosci. 2015 Dec;18(12):1713-21. doi: 10.1038/nn.4157. Epub 2015 Nov 25. Review. PMID:26605882.

Plasticity of dendritic function. Magee JC, Johnston D. Curr Opin Neurobiol. 2005 Jun;15(3):334-42. Review. PMID:15922583

Activation-dependent learning rule for GPCR localization – 5ht2AR regulation in prefrontal cortical neurons

Gabriele Scheler BMC Neurosci. 2013; 14(Suppl 1): P344. Published online 2013 Jul 8. doi: 10.1186/1471-2202-14-S1-P344. PMCID: PMC3704850

Neuromodulation of circuits with variable parameters: single neurons and small circuits reveal principles of state-dependent and robust neuromodulation. Marder E1, O’Leary T, Shruti S. Annu Rev Neurosci. 2014;37:329-46. doi: 10.1146/annurev-neuro-071013-013958.