List of the Essential Things for computations with the brain

Ambreaux · 2018-02-05 12:46:09 UTC

Dear community,

I am looking at the biological brain from the machine learning point of view and have formulated next question:

What is the List of the Essential Things for computation with the neuronal network inspired by the biological brain, rather than restrictions and shortcomings of the underlay chemical processes and biophysics?

Draft v0.1:

Input to the neuron is the analog signal
Output of the neuron is the digital signal
Connections between neurons:
a) Multi-synaptic
b) Recurrent, neuron itself or group of the neurons can form number of the feedback loops
c) Weight, the postsynaptic side signal strength - post synaptic potential (PSP)
d) Transmission speed - dendritic transmission speed rather than synaptic delay
e) Unique profile to be depressing, facilitating or pseudo-linear
f) Input timing matters, for spike time dependent plasticity (STDP)
g) Postsynaptic feedback, spike back-propagation involved in STDP mechanism
h) Place of the synapse matters, where in the dendritic tree it is placed
i) Type of the synapse matters, defining excitatory or inhibitory behavior
Post-synaptic potential propagation - having all above for the connections, one can assume that the every single event in the dendrite can be treated as the propagating wave

I) Does the assumptions above are correct?
II) What of the above should be dynamic and what can be freezed?
II) How does the STDP transformed into long-term memory, caused by Protein Kinase M Zeta^?
III) How does long-term memory transformed into the genetic memory?
IV) What is the motivation to establish new connection between neurons, “Glial Guidance” ^^?
V) What is the motivation to move the neuron, the neuronal locomotion?
VI) What is the motivation to born new neuron?
VII) What is the type of the new born neuron?
VIII) How the new born neuron evolve?

Maybe some of my questions are too naive and some are too abstract, as well as some already solved by neuroscience, but, anyway, let me know that! =)

Thank You for support!

ncbi.nlm.nih.gov

Protein kinase M zeta synthesis from a brain mRNA encoding an independent protein kinase C zeta catalytic domain. Implications for the molecular mechanism of memory.

AI Hernandez, N Blace, JF Crary, PA Serrano, M Leitges, JM Libien, G Weinstein, A Tcherapanov and TC Sacktor, The Journal of biological chemistry, Oct 2003 10

Protein kinase M zeta (PKM zeta) is a newly described form of PKC that is necessary and sufficient for the maintenance of hippocampal long term potentiation (LTP) and the persistence of memory in Drosophila. PKM zeta is the independent catalytic domain of the atypical PKC zeta isoform and produces long term effects at synapses because it is persistently active, lacking autoinhibition from the regulatory domain of PKC zeta. PKM has been thought of as a proteolytic fragment of PKC. Here we report that brain PKM zeta is a new PKC isoform, synthesized from a PKM zeta mRNA encoding a PKC zeta catalytic domain without a regulatory domain. Multiple zeta-specific antisera show that PKM zeta is expressed in rat forebrain as the major form of zeta in the near absence of full-length PKC zeta. A PKC zeta knockout mouse, in which the regulatory domain was disrupted and catalytic domain spared, still expresses brain PKM zeta, indicating that this form of PKM is not a PKC zeta proteolytic fragment. Furthermore, the distribution of brain PKM zeta does not correlate with PKC zeta mRNA but instead with an alternate zeta RNA transcript thought incapable of producing protein. In vitro translation of this RNA, however, generates PKM zeta of the same molecular weight as that in brain. Metabolic labeling of hippocampal slices shows increased de novo synthesis of PKM zeta in LTP. Because PKM zeta is a kinase synthesized in an autonomously active form and is necessary and sufficient for maintaining LTP, it serves as an example of a link coupling gene expression directly to synaptic plasticity.

**

ncbi.nlm.nih.gov

The role of glial cells in axon guidance, fasciculation and targeting.

AR Learte and A Hidalgo, Advances in experimental medicine and biology, 2007

Axons navigate step-wise, from one intermediate target to the next, until they reach their final destination target. In the central nervous system, intermediate targets are often glial cells, and final targeting is also aided by glia. In the peripheral nervous system, however, glial cells most often follow axons, which therefore navigate following other, nonglial clues. Even in the central nervous system, interactions between axons and glia are dynamic and reciprocal, as the neurons regulate migration, survival and proliferation of the glia cells they need for guidance. We review here the experimental evidence investigating roles of glia in axon guidance. Some molecules are known to influence either the neurons or the glia, but the molecular mechanisms underlying axon-glia interactions during pathfinding are only beginning to emerge.

P.S.
…and one day we will realize, that we have built in-silico the General Purpose Artificial Intelligence