#] #] ********************* #] "$d_web"'MindCode/0_MindCode notes.txt' # www.BillHowell.ca 08Jun2015 initial version see also link d_QNial_mine 'MindCode' [architecture, data, functions, processes, operating systems, conciousness, behaviours, personalities, creativity] 13Mar2022 Combine? : Liquid-State Networks (LSNs) Wolfgang Maass Super-Turing Machines (STMs) Hava Sieglemann She built a language!! 24************************24 24************************24 # Table of Contents, generated by : # $ grep "^#]" "$d_web"'MindCode/0_MindCode notes.txt' | sed 's/^#\]/ /' ********************* "$d_web"'MindCode/0_MindCode notes.txt' 10Mar2022 Liquid State Machines (LSMs) use Spiking Nerual Netorks! (Wolgang Mass 06Mar2021 Rademacher and Gaussian Complexities 24Feb2020 QNial basis 21Feb2020 Initial QNial programming (no more [yap, arm-waving]) 23Sep2019 HOX genes (architecture) 12Aug2019 Spikeless information transfer? 11Aug2019 Architectures & Function 10Aug2019 mRNA circulation - soma to synapse 25Jul2019 Alice Parker. Spiking Neural Networks & DNA. USC. Los Angeles. USA - Mindcode 07Mar2018 DNA storage for real computers #24************************24 # Principles 02Nov2023 [intra, inter]-cellular processes: intra - [DNA, RNA, etc], inter - multi-neuron 02Nov2023 keep data as bitL for easy manipulation 02Nov2023 DNA -> RNA polymerase -> transcribe to RNA, mRNA for protein coding. I must use all RNA "floats off" (NYET!! - see microtubules in "transport" below!!!), it doesn't follow a DNA, microtubule? DNA: ATGC - adenine, thymine, guanine, and cytosine: AT and CG this is boolean (binary) code RNA: transcript carries the same information as the non-template (coding) strand of DNA, but it contains the base uracil (U) instead of thymine (T) start codon (ATG), end: 5' cap and poly-A tail 02Nov2023 RNA transport https://theconversation.com/how-does-rna-know-where-to-go-in-the-city-of-the-cell-using-cellular-zip-codes-and-postal-carrier-routes-191155 Matthew Taliaferro ZIP codes that send RNAs to: neurites - precursors to the axons and dendrites on neurons that transmit and receive electrical signals epithelial cells Proteins Unkempt protein regulates neurite production LARP1, responsible for the transport of RNAs containing a particular ZIP code to both neurites and the bottom end of epithelial cells microtubules cellular streets, train track (bi-directionality) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6650148/ ...motor-driven transport along MT tracks, with instantaneous velocities ranging from 0.5-5 μm/s [46]. Howell: like a wave propagation, inventory of mRNA would allow very high speeds of getting a protein to intra-neuron site? 24************************24 #08********08 #] 02Nov2023 can I identify "MindCode useful" DNA sequences? (code first, then look?) 08********08 #] ??Nov2023 08********08 #] ??Nov2023 08********08 #] ??Nov2023 08********08 #] ??Nov2023 08********08 #] ??Nov2023 08********08 #] ??Nov2023 08********08 #] ??Nov2023 #08********08 #] 02Nov2023 search "How long does it take to transcribe and transport RNA?" +-----+ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8205424/ SIAM J Appl Dyn Syst. Author manuscript; available in PMC 2021 Jun 15. Published in final edited form as: SIAM J Appl Dyn Syst. 2018; 17(4): 2855–2881. Published online 2018 Dec 18. doi: 10.1137/18m1186083 PMCID: PMC8205424 NIHMSID: NIHMS1587888 PMID: 34135697 Modeling microtubule-based transport and anchoring of mRNA Maria-Veronica Ciocanel,† Björn Sandstede,‡ Samantha P Jeschonek,§ and Kimberly L Mowry Abstract Localization of messenger RNA (mRNA) at the vegetal cortex plays an important role in the early development of Xenopus laevis oocytes. While it is known that molecular motors are responsible for the transport of mRNA cargo along microtubules to the cortex, the mechanisms of localization remain unclear. We model cargo transport along microtubules using partial differential equations with spatially-dependent rates. A theoretical analysis of reduced versions of our model predicts effective velocity and diffusion rates for the cargo and shows that randomness of microtubule networks enhances effective transport. A more complex model using parameters estimated from fluorescence microscopy data reproduces the spatial and timescales of mRNA localization observed in Xenopus oocytes, corroborates experimental hypotheses that anchoring may be necessary to achieve complete localization, and shows that anchoring of mRNA complexes actively transported to the cortex is most effective in achieving robust accumulation at the cortex. >> YEAH! timing information!! Awesome differential equations >> OUCH! egg development timescale 24hours, way too slow for [neuron, muscle]s /home/bill/web/References/Neural Nets/MindCode/ Modeling microtubule-based transport and anchoring of mRNA: Ciocanel, Sandstede, Jeschonek, Mowry 18Dec2018.png +-----+ https://www.tandfonline.com/doi/full/10.1080/15476286.2020.1842631 RNA transport from transcription to localized translation: a single molecule perspective Eugenia Basyuk ORCID Icon, Florence Rage ORCID Icon & Edouard Bertrand Pages 1221-1237 | Received 02 May 2020, Accepted 22 Oct 2020, Published online: 13 Nov 2020 >> 5-40 minutes mentioned, not microtubule? +-----+ https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/rna-transport RNA Transport tRNA fragments (tRFs) are the second most abundant small noncoding RNAs in EVs followed by miRNAs (O'Brien et al., 2020). From: Handbook on the Toxicology of Metals (Fifth Edition), 2022 Axonal mRNA Transport and Functions F.P.G. van Horck, C.E. Holt, in Encyclopedia of Neuroscience, 2009 Axonal messenger RNA (mRNA) transport serves to spatially restrict protein synthesis to the axon and growth cone. The molecular mechanism for axonal mRNA transport involves the recognition of zipcodes by RNA-binding proteins, assembly into RNA granules, and transport along the microtubules and actin filaments in the axon and growth cone. Local translation of mRNAs provides developing growth cones with the capacity to autonomously regulate their structure and function during axonal growth and navigation. In mature axons, local protein synthesis is stimulated on injury and might facilitate the process of regeneration. in volume above? : https://www.sciencedirect.com/science/article/abs/pii/B9780124186750000067 Chapter Six - Local Protein Synthesis at Synapses Oswald Steward, Joseph Dynes, Shannon Farris The Synapse Structure and Function 2014, Pages 173-194 Live-cell imaging of Arc/MS2 mRNA particle movement in unstimulated, cortical neurons in vitro revealed bidirectional dendritic transport at a wide range of velocities ranging from <1 μm/s to about 70 μm/s Figure 4(B) illustrates a kymograph of a single dendrite imaged for 500 s in which particles can be seen to move at different rates. >> AWESOME!!!! >> paywall - can't get Figures +-----+ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6650148/ Curr Opin Neurobiol. Author manuscript; available in PMC 2019 Aug 1. Published in final edited form as: Curr Opin Neurobiol. 2019 Aug; 57: 110–116. Published online 2019 Feb 19. doi: 10.1016/j.conb.2019.01.016 PMCID: PMC6650148 NIHMSID: NIHMS1041609 PMID: 30784978 The travels of mRNAs in neurons: Do they know where they are going? Sulagna Das,1 Robert H. Singer,1,2,* and Young J. Yoon1,2,* Abstract Neurons are highly polarized cells that can extend processes far from the cell body. As such, transport of messenger RNAs serves as a set of blueprints for the synthesis of specific proteins at distal sites. RNA localization to dendrites and axons confers the ability to regulate translation with extraordinary precision in space and time. Although the rationale for RNA localization is quite compelling, it is unclear how a neuron orchestrates such a complex task of distributing over a thousand different mRNAs to their respective subcellular compartments. Recent single-molecule imaging studies have led to insights into the kinetics of individual mRNAs. We can now peer into the transport dynamics of mRNAs in both dendrites and axons. When the mRNA particles were in motion, they would either move processively (0.5-2.0 μm/s) or in a series of short distances (few microns) intervened by short pauses (<10 s), or remain corralled (diffusion within a small volume of space). The directed movement is indicative of motor-driven transport along MT tracks, with instantaneous velocities ranging from 0.5-5 μm/s [46]. >> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6650148/ motor-driven transport along MT tracks, with instantaneous velocities ranging from 0.5-5 μm/s [46]. /home/bill/web/References/Neural Nets/MindCode/ mRNA travel in neurons: Das, Singer,Yoon 19Feb2019 #08********08 #] 02Nov2023 search "Is RNA transported along DNA or microtubules?" Yes! at least some times? +-----+ https://www.nature.com/articles/s41467-021-26383-9 Microtubule-based transport is essential to distribute RNA and nascent protein in skeletal muscle Lance T. Denes, Chase P. Kelley & Eric T. Wang Nature Communications volume 12, Article number: 6079 (2021) Abstract While the importance of RNA localization in highly differentiated cells is well appreciated, basic principles of RNA localization in skeletal muscle remain poorly characterized. Here, we develop a method to detect and quantify single molecule RNA localization patterns in skeletal myofibers, and uncover a critical role for directed transport of RNPs in muscle. We find that RNAs localize and are translated along sarcomere Z-disks, dispersing tens of microns from progenitor nuclei, regardless of encoded protein function. We find that directed transport along the lattice-like microtubule network of myofibers becomes essential to achieve this localization pattern as muscle development progresses; disruption of this network leads to extreme accumulation of RNPs and nascent protein around myonuclei. Our observations suggest that global active RNP transport may be required to distribute RNAs in highly differentiated cells and reveal fundamental mechanisms of gene regulation, with consequences for myopathies caused by perturbations to RNPs or microtubules. +-----+ https://theconversation.com/how-does-rna-know-where-to-go-in-the-city-of-the-cell-using-cellular-zip-codes-and-postal-carrier-routes-191155 How does RNA know where to go in the city of the cell? Using cellular ZIP codes and postal carrier routes Published: March 6, 2023 8.34am EST Matthew Taliaferro - Assistant Professor of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus My research team focuses on this very question: What are the molecular mechanisms that control RNA transport? Our recently published research hints that some of the molecular language governing this process may be universal across all cell types. The molecular language of RNA transport For a handful of mRNAs – or RNA sequences coding for specific proteins – researchers have an idea about how they’re transported. They often contain a particular string of nucleotides, the chemical building blocks that make up RNA, that tell cells about their desired destination. These sequences of nucleotides, or what scientists refer to as RNA “ZIP codes,” are recognized by proteins that act like mail carriers and deliver the RNAs to where they are supposed to go. My team and I set out to discover new ZIP codes that send RNAs to neurites, the precursors to the axons and dendrites on neurons that transmit and receive electrical signals. We reasoned that these ZIP codes must lie somewhere within the thousands of nucleotides that make up the RNAs in neurites. But how could we find our ZIP code needle in the RNA haystack? ... We found that one protein that regulates neurite production, named Unkempt, repeatedly appeared with ZIP code-containing RNAs. When we depleted cells of Unkempt, the ZIP codes were no longer able to direct RNA transport to neurites, implicating Unkempt as the “mail carrier” that delivered these RNAs. Toward a universal language With this work, we identified ZIP codes that sent RNAs to neurites (in our analogy, the bank). But where would an RNA containing one of these ZIP codes end up if it were in a cell that didn’t have neurites (a city that didn’t have a bank)? To answer this, we looked at where RNAs were in a completely different cell type, epithelial cells that line the body’s organs. Interestingly, the same ZIP codes that sent RNAs to neurites sent them to the bottom of epithelial cells. This time we identified another mail carrier, a protein called LARP1, responsible for the transport of RNAs containing a particular ZIP code to both neurites and the bottom end of epithelial cells. How could one ZIP code and mail carrier transport an RNA to two different locations in two very different cells? It turns out that both of these cell types contain structures called microtubules that are oriented in a very particular way. Microtubules can be thought of as cellular streets that serve as tracks to transport a variety of cargo in the cell. Importantly, these microtubules are polarized, meaning they have ingrained “plus” and “minus” ends. Cargo can therefore be transported in specific directions by targeting to one of these ends. ... Understanding how ZIP code sequences work can help researchers design RNAs that deliver their payload instructions to precise locations in the cell. Given the growing promise of RNA-based therapeutics, ranging from vaccines to cancer therapies, knowing how to make an RNA go from point A to point B is more important than ever. >> WOW! exactly what I need!!! neuron-specific >> Gerald Pollacks microtubule transport!! Quick summary : ZIP codes that send RNAs to: neurites - precursors to the axons and dendrites on neurons that transmit and receive electrical signals epithelial cells Proteins Unkempt protein regulates neurite production LARP1, responsible for the transport of RNAs containing a particular ZIP code to both neurites and the bottom end of epithelial cells cellular streets, train track (bi-directionality) microtubules +-----+ https://www.nature.com/articles/s41580-021-00356-8 Intracellular mRNA transport and localized translation Sulagna Das, Maria Vera, Valentina Gandin, Robert H. Singer & Evelina Tutucci Nature Reviews Molecular Cell Biology volume 22, pages 483–504 (2021) Abstract Fine-tuning cellular physiology in response to intracellular and environmental cues requires precise temporal and spatial control of gene expression. High-resolution imaging technologies to detect mRNAs and their translation state have revealed that all living organisms localize mRNAs in subcellular compartments and create translation hotspots, enabling cells to tune gene expression locally. Therefore, mRNA localization is a conserved and integral part of gene expression regulation from prokaryotic to eukaryotic cells. In this Review, we discuss the mechanisms of mRNA transport and local mRNA translation across the kingdoms of life and at organellar, subcellular and multicellular resolution. We also discuss the properties of messenger ribonucleoprotein and higher order RNA granules and how they may influence mRNA transport and local protein synthesis. Finally, we summarize the technological developments that allow us to study mRNA localization and local translation through the simultaneous detection of mRNAs and proteins in single cells, mRNA and nascent protein single-molecule imaging, and bulk RNA and protein detection methods. +-----+ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411697/ Microtubule-Based Transport and the Distribution, Tethering, and Organization of Organelles Kari Barlan1 and Vladimir I. Gelfand2 Cold Spring Harb Perspect Biol. 2017 May; 9(5): a025817. doi: 10.1101/cshperspect.a025817 PMCID: PMC5411697 NIHMSID: NIHMS890978 PMID: 28461574 SUMMARY Microtubules provide long tracks along which a broad range of organelles and vesicles are transported by kinesin and dynein motors. Motor protein complexes also tether cargoes to cytoskeletal filaments, helping facilitate their interaction and communication. The generation of biochemically distinct microtubule subpopulations allows subsets of motors to recognize a given microtubule identity, allowing further organization within the cytoplasm. Both transport and tethering are spatiotemporally regulated through multiple modes, including acute modification of both motor–cargo and motor–track associations by various physiological signals. Strict regulation of intracellular transport is particularly important in specialized cell types such as neurons. Here, we review general mechanisms by which cargo transport is controlled and also highlight examples of transport regulated by multiple mechanisms. #08********08 #] 02Nov2023 search "DNA transcription software" +-----+ https://www.genomics-online.com/resources/16/5021/free-tools-and-software-for-genomics-transcriptomics-crispr-co/ >> lots of stuff... +-----+ online free once-at-a-time DNA translation -> protein https://web.expasy.org/translate/ https://en.vectorbuilder.com/tool/dna-translation.html Input your DNA sequence below to retrieve the translated amino acid sequence. The sequence should begin with the start codon (ATG) and be in a multiple of 3 for a complete codon sequence. +-----+ https://www.khanacademy.org/science/ap-biology/gene-expression-and-regulation/transcription-and-rna-processing/a/overview-of-transcription Key points: Transcription is the first step in gene expression. It involves copying a gene's DNA sequence to make an RNA molecule. Transcription is performed by enzymes called RNA polymerases, which link nucleotides to form an RNA strand (using a DNA strand as a template). Transcription has three stages: initiation, elongation, and termination. In eukaryotes, RNA molecules must be processed after transcription: they are spliced and have a 5' cap and poly-A tail put on their ends. Transcription is controlled separately for each gene in your genome. RNA polymerase The main enzyme involved in transcription is RNA polymerase, which uses a single-stranded DNA template to synthesize a complementary strand of RNA. Specifically, RNA polymerase builds an RNA strand in the 5' to 3' direction, adding each new nucleotide to the 3' end of the strand. Stages of transcription Initiation. RNA polymerase binds to a sequence of DNA called the promoter, found near the beginning of a gene. Each gene (or group of co-transcribed genes, in bacteria) has its own promoter. Once bound, RNA polymerase separates the DNA strands, providing the single-stranded template needed for transcription. The promoter region comes before (and slightly overlaps with) the transcribed region whose transcription it specifies. It contains recognition sites for RNA polymerase or its helper proteins to bind to. The DNA opens up in the promoter region so that RNA polymerase can begin transcription. The promoter region comes before (and slightly overlaps with) the transcribed region whose transcription it specifies. It contains recognition sites for RNA polymerase or its helper proteins to bind to. The DNA opens up in the promoter region so that RNA polymerase can begin transcription. Elongation. One strand of DNA, the template strand, acts as a template for RNA polymerase. As it "reads" this template one base at a time, the polymerase builds an RNA molecule out of complementary nucleotides, making a chain that grows from 5' to 3'. The RNA transcript carries the same information as the non-template (coding) strand of DNA, but it contains the base uracil (U) instead of thymine (T). [What do 5' and 3' mean?] RNA polymerase synthesizes an RNA transcript complementary to the DNA template strand in the 5' to 3' direction. It moves forward along the template strand in the 3' to 5' direction, opening the DNA double helix as it goes. The synthesized RNA only remains bound to the template strand for a short while, then exits the polymerase as a dangling string, allowing the DNA to close back up and form a double helix. In this example, the sequences of the coding strand, template strand, and RNA transcript are: Coding strand: 5' - ATGATCTCGTAA-3' Template strand: 3'-TACTAGAGCATT-5' RNA: 5'-AUGAUC...-3' (the dots indicate where nucleotides are still being added to the RNA strand at its 3' end) RNA polymerase synthesizes an RNA transcript complementary to the DNA template strand in the 5' to 3' direction. It moves forward along the template strand in the 3' to 5' direction, opening the DNA double helix as it goes. The synthesized RNA only remains bound to the template strand for a short while, then exits the polymerase as a dangling string, allowing the DNA to close back up and form a double helix. In this example, the sequences of the coding strand, template strand, and RNA transcript are: Coding strand: 5' - ATGATCTCGTAA-3' Template strand: 3'-TACTAGAGCATT-5' RNA: 5'-AUGAUC...-3' (the dots indicate where nucleotides are still being added to the RNA strand at its 3' end) Termination. Sequences called terminators signal that the RNA transcript is complete. Once they are transcribed, they cause the transcript to be released from the RNA polymerase. An example of a termination mechanism involving formation of a hairpin in the RNA is shown below. #08********08 #] 02Nov2023 DNA, RNA, mRNA +-----+ search "genomics databases" https://www.ncbi.nlm.nih.gov/genbank/ https://www.ncbi.nlm.nih.gov/genome/ Effective May 2024, NCBI's Assembly resource will no longer be available. NCBI Assembly data can now be found on the NCBI Datasets genome pages. Learn more. >> good, but no reference to actual [DNA, RNA, steps] +-----+ search "RNA versus mRNA" +--+ https://www.dictionary.com/e/dna-vs-rna-vs-mrna-the-differences-are-vital/ “DNA” vs. “RNA” vs. “mRNA”: The Differences Are Vital January 8, 2021 DNA stands for “deoxyribonucleic acid.” DNA is arranged in the shape of a double helix, which resembles a twisted ladder. The “rungs” of the ladder consist of base pairs of substances known as nitrogen bases. You might remember the four bases from science class: adenine, thymine, guanine, and cytosine. These base pairs are the reason why DNA is so important to life: the ordering of the base pairs results in a specific genetic code called a gene. RNA stands for “ribonucleic acid.” RNA is a large molecule made from a single strand of DNA, and one of its main roles is to transfer the instructions needed to make proteins. DNA vs. RNA DNA and RNA are very similar. After all, RNA is supposed to be a copy of DNA. However, there are a few differences between the two molecules. The biggest difference is in their shape: DNA is a two-stranded molecule in the form of a double helix. RNA, on the other hand, is a single-stranded molecule. The other major difference is in the nitrogen bases: RNA shares three of DNA’s bases but has a substance known as uracil that replaces thymine when the DNA is copied. To put it very simply, uracil requires less energy to maintain than thymine, but the presence of thymine makes DNA more stable. mRNA “messenger RNA.”: is RNA that is read by ribosomes to build proteins (note: uracil) While all types of RNA are involved in building proteins, mRNA is the one that actually acts as the messenger. It is mRNA specifically that has the recipe for a protein. The mRNA is made in the nucleus and sent to the ribosome, like all RNA. Once it gets there, the mRNA bonds with the ribosome, which reads the mRNA’s nitrogen base sequence. Every three-bond sequence of mRNA relates to a specific amino acid, a “building block” of a protein. Amino acids must be arranged in a certain order to make a specific protein, and the mRNA has the blueprints that tell the ribosome which amino acids to get and how they should be arranged. Other types of RNA help the ribosome actually build the protein. Once the protein is built, the mRNA’s job is over and it will degrade. transfer RNA (tRNA) ribosomal RNA (rRNA) +--+ https://en.wikipedia.org/wiki/RNA Ribonucleic acid (RNA) is a polymeric molecule that is essential for most biological functions, either by performing the function itself (Non-coding RNA) or by forming a template for the production of proteins (messenger RNA). Some RNA molecules play an active role within cells by catalyzing biological reactions, controlling gene expression, or sensing and communicating responses to cellular signals. One of these active processes is protein synthesis, a universal function in which RNA molecules direct the synthesis of proteins on ribosomes. This process uses transfer RNA (tRNA) molecules to deliver amino acids to the ribosome, where ribosomal RNA (rRNA) then links amino acids together to form coded proteins. It has become widely accepted in science[1] that early in the history of life on Earth, prior to the evolution of DNA and possibly of protein-based enzymes as well, an "RNA world" existed in which RNA served as both living organisms' storage method for genetic information—a role fullfilled today by DNA, except in the case of RNA viruses—and potentially performed catalytic functions in cells—a function performed today by protein enzymes, with the notable and important exception of the ribosome, which is a ribozyme. Synthesis Synthesis of RNA is usually catalyzed by an enzyme—RNA polymerase—using DNA as a template, a process known as transcription. Initiation of transcription begins with the binding of the enzyme to a promoter sequence in the DNA (usually found "upstream" of a gene). The DNA double helix is unwound by the helicase activity of the enzyme. The enzyme then progresses along the template strand in the 3’ to 5’ direction, synthesizing a complementary RNA molecule with elongation occurring in the 5’ to 3’ direction. The DNA sequence also dictates where termination of RNA synthesis will occur.[27] Primary transcript RNAs are often modified by enzymes after transcription. For example, a poly(A) tail and a 5' cap are added to eukaryotic pre-mRNA and introns are removed by the spliceosome. There are also a number of RNA-dependent RNA polymerases that use RNA as their template for synthesis of a new strand of RNA. For instance, a number of RNA viruses (such as poliovirus) use this type of enzyme to replicate their genetic material.[28] Also, RNA-dependent RNA polymerase is part of the RNA interference pathway in many organisms.[29] In the early 1970s, retroviruses and reverse transcriptase were discovered, showing for the first time that enzymes could copy RNA into DNA (the opposite of the usual route for transmission of genetic information). For this work, David Baltimore, Renato Dulbecco and Howard Temin were awarded a Nobel Prize in 1975. In 1976, Walter Fiers and his team determined the first complete nucleotide sequence of an RNA virus genome, that of bacteriophage MS2.[79] #08********08 #] 31Oct2023 create ndf for [step-wise, procedural] program sequence BOTH [inter, intra]-cellular approaches p443sec LIST PARSE: A laminar model of variable-length sequences image p444fig12.42 LIST PARSE: Laminar cortical model of working memory and list chunking image p445fig12.43 LIST PARSE laminar cortical Cognitive Working Memory circuit, homologous to the LAMINART circuit circuit that models aspects of how visual cortex sees image p436fig12.30 The conscious ARTWORD, or cARTWORD, laminar cortical speech model simulates how future context can disambiguate noisy past speech sounds LIST PARSE is what I would start with for programs? >> maybe not, just steal von Neuman architecture? 08********08 #] 06Aug2023 What is a Spike? ROLES of neuron architecture : axon - power cable, tap into [glial, other]? * [flow battery, capacitor]'s dendrite synapse neuro-transmitter [axon, dendrite]s can do both jobs? MODES of signal interpretation [spike, continuous, other] : spiking mode - less sensitive to the strength of the signal that does get through continuous mode - neuron A's spike is other - electromagnetic field theories combined modes - consistently or depending on the situation? connections - synaptic, nerve [fibre, branch]s, SYNAPTIC_PATTERN of signals from ONE specific spike of neuron A, tells connected neuron B the : IDENTITY of neuron A : specifically that the pattern of neuron B's incoming synaptic signals, is from neuron A's spike, as distinguished thousands of other neurons that neuron B is connected to. In other words, the [ID, address, identifier] of neuron A is well-recognized by neuron B. TIMING of neuron A's spike, in neuron B's absolute time clock : in spite of the lack of am "event timing signal" independant of neuron B's system. Over time as neuron B learns about neuron A, the estimated timing of neuron A's spike firing can be estimated from : the time of neuron B's first synaptic signal (implies "shortest synaptic distance" A-to-B) A-to-B-to-A "circular spike timing tuning training events" as part of a vast set of neuron [tune, learn, evolve] with respect to the network itself, as opposed to the exogenous information carried in the flow context of [shortest synaptic distance (SSD), circular spike timing tuning training events (CST)] results for other neurons that neuron B is connected to AMPLITUDE of neuron A's spike : Thresholding of weaker synaptic signals input to neuron B, changes neuron A's input synaptic pattern to neuron B, by keeping stronger parts of the SAME pattern but losing weaker parts. perhaps this thresholding could be [modulated, adaptive]? IMPLICATIONS of synaptic_pattern : RETENTION of [identity, timing, amplitude] properties of synaptic_patterns from spiking : are to some degree even amongst a cacophony of spikes from many neurons with synaptic connections (or not) to neuron B SPIKE-PATTERN of neuron A's spiking sequence over time, tells neuron B : 08********08 #] 15Mar2022 Reproducing Kernel Hilbert Space (RKHS) [description, reference]s see : "$d_web"'Mathematics/5_Math concepts, functions.odt' 17Mar2022 Aurel A. Lazar, Yevgeniy Slutskiy Nov2013 "Functional Identification of Spike-Processing Neural Circuits (RKHS)" November 2013Neural Computation 26(2), SourcePubMed Columbia University https://www.researchgate.net/publication/258425593_Functional_Identification_of_Spike-Processing_Neural_Circuits "... Employing the reproducing kernel Hilbert space (RKHS) of trigonometric polynomials to describe input stimuli, we quantitatively describe the relationship between underlying circuit parameters and their projections. ..." Lazar, Slutskiy Nov2013 Functional Identification of Spike-Processing Neural Circuits (RKHS).pdf Antonio R.C. Paiva, Il Park and Jose C. Principe 2008 "Reproducing kernel Hilbert Spaces for Spike Train Analysis" https://www.sci.utah.edu/~arpaiva/pubs/2008a_presentation.pdf {arpaiva, memming, principe}@cnel.ufl.edu Computational NeuroEngineering Laboratory University of Florida, Gainesville, FL32611 Paiva, Park, Principe 2008 Reproducing kernel Hilbert Spaces for Spike Train Analysis 08********08 #] 13Mar2022 Neto, Sieglemann, Costa 2003 "Symbolic processing in neural networks" Joao Neto, Hava Sieglemann, Felix Costa 2003 "Symbolic processing in neural networks" Journal of Brazilian Computer Society, 20pp With dynamical systems in general we have computation without programmability, i.e., the extra power these systems exhibit has to do with the decoupling between programming and computation. Up to the power of Turing machines, computations are describable by programs that correspond to the prescription by finite means of some rational parameters of the system. Beyond Turing power we have computations that are not describable by finite means: computation without a program. In this paper we want to shed some light on the programmability of neural nets. Combine? : Liquid-State Networks (LSNs) Wolfgang Maass Super-Turing Machines (STMs) Hava Sieglemann She built a language!! 08********08 #] 10Mar2022 Liquid State Machines (LSMs) use Spiking Nerual Netorks! (Wolgang Mass Wolfgang Maass, Thomas Natschläger, and Henry Markram. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation, 14:2531–2560, 2002. from IJCNN2022 peer review of : "$d_PROJECTS"'2022 WCCI Padua, Italy/2099 r Ground Reaction Force Estimation in a Quadruped Robot via Liquid State Networks.txt' 08********08 #] 06Mar2021 Rademacher and Gaussian Complexities >> reminds me of Bronstein WCCI2020 plenary on Deep geometry question arising from IJCNN2021 peer review : "1364 p Zhang, Zhu, Wu, Zheng - BDU-net, Towards Accurate Segmentation of Dental Image using Border Guidance and Feature Map Distortion.pdf" http://www.ai.mit.edu/projects/jmlr/papers/volume3/bartlett02a/bartlett02a.pdf Journal of Machine Learning Research 3 (2002) 463-482Submitted 11/01; Published 11/02 Rademacher and Gaussian Complexities: Risk Bounds and Structural Results Peter L. Bartlett Peter.Bartlett@anu.edu.au Shahar Mendelson shahar@csl.anu.edu.au Research School of Information Sciences and Engineering, Australian National University, Canberra 0200, Australia Editor:Philip M. Long Abstract - We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and Gaussian complexities. In a decision theoretic setting, we prove general risk bounds in terms of these complexities. We consider function classes that can be expressed as combinations of functions from basis classes and show how the Rademacher and Gaussian complexities of such a function class can be bounded in terms of the complexity of the basis classes. We give examples of the application of these techniques in finding data-dependent risk bounds for decision trees, neural networks and support vector machines. Keywords: Error Bounds, Data-Dependent Complexity, Rademacher Averages, Maximum Discrepancy Definition 2 Let μ be a probability distribution on a set X and suppose that X1,... ,Xn are independent samples selected according to μ. Let F be a class of functions mapping from X to R. Define the maximum discrepancy of F as the random variable ˆDn(F) = supf∈F2nn/2∑i=1f(Xi)−2nn∑i=n/2+1f(Xi). Denote the expected maximum discrepancy of F by Dn(F)=EˆDn(F). Define the random variable ˆRn(F)=E[supf∈F∣∣∣∣∣2nn∑i=1σif(Xi)∣∣∣∣∣∣∣∣∣∣X1,... ,Xn], where σ1,... ,σn are independent uniform {±1}-valued random variables. Then the Rademacher complexity of F is Rn(F)=EˆRn(F). Similarly, define the random variable ˆGn(F)=E[supf∈F∣∣∣∣∣2nn∑i=1gif(Xi)∣∣∣∣∣∣∣∣∣∣X1,... ,Xn], where g1,... ,gn are independent Gaussian N(0,1) random variables. The Gaussian complexity of F is Gn(F)=EˆGn(F). 08********08 #] 24Feb2020 QNial basis see link d_QNial_mine 'MindCode/1_MindCode summary ref.txt' to test spiking, I must build some microNNs but first build a few basic arithmetic operators 08********08 #] 21Feb2020 Initial QNial programming (no more [yap, arm-waving]) Izhikevich-like neuron - with [DNA-mRNA-epi, addressable synapses] loaddefs link d_QNial_mine 'MindCode/Izhikevich-like neuron - with [DNA-mRNA-epi, addressable synapses].ndf' 08********08 #] 23Sep2019 HOX genes (architecture) https://en.wikipedia.org/wiki/Hox_gene ... Hox genes, a subset of homeobox genes, are a group of related genes that specify regions of the body plan of an embryo along the head-tail axis of animals. Hox proteins encode and specify the characteristics of 'position', ensuring that the correct structures form in the correct places of the body. For example, Hox genes in insects specify which appendages form on a segment (e.g. legs, antennae, and wings in fruit flies), and Hox genes in vertebrates specify the types and shape of vertebrae that will form. In segmented animals, Hox proteins thus confer segmental or positional identity, but do not form the actual segments themselves. >> exactly what I need! from the sci-fi film "Annialation" 08********08 #] 12Aug2019 Spikeless information transfer? - are current concepts too focused on spikes? - can synapses transfer information (states) without a spike? - if so, what does a spike do? When does a neuron spike? - build-up of membrane potential might be driven by [multiple conflicting states, neighboring neuron membrane potentials, spiking in region]? - but a neuron may have MANY co-existing states - states can relate a neuron to many other neurons - are synaptic changes very [fast, short-lived]? - spiking may "clear the states"?, signal that a state is confirmed, ... all of the above Synaptic information is different for same neuron but [different, same] synapse - as allowed by many different mRNA at synapse Multiple conflicting states - states may relate to completely different functionalities for the same neuron - logic gates have small number of states, low diversity of [broad, general] interest - numbers - arithmetic, functions, - dynamics - calculus - ADP, solution to simultaneous equations, - sensory, attention, conciousness - pattern matching (can subsume logic, etc) - [morphing, overloading] of neurons to be much more powerful within a class of [similar, related] [functions, patterns, etc] architectures - perceptron - of very general use - kernels (Johan Suyken's comment for Deep Learning - convexity) 08********08 #] 11Aug2019 Architectures & Function Shapiro & Benenson - finite automata built from phages or something, programmed cell kill if cancerous Logic gates - use them! start very simple, improve over time McCulloch & Pitts - collect their functional architectures! State representations - especially inactive SNNs - simplest model - computer [CPU, memory, etc] - finite automata - is there anything besides Shapiro & Benenson? (probably - normal biology) - state - epigenetic (methylation), [express, modify] DNA coding availability - How many "states" do the various biological cells have? - what are the state categories for each type of cell? - what are the states of each state category? - states versus data - does data have to be [collected, structured, object oriented, networked]? - or can a general list do? (DNA, RNA work like that?) 08********08 #] 10Aug2019 mRNA circulation - soma to synapse reference from Alice Parker Karl E. Bauer, Inmaculada Segura, Imre Gaspar, Volker Scheuss, Christin Illig, Georg Ammer, Saskia Hutten, Eugénia Basyuk, Sandra M. Fernández-Moya, Janina Ehses, Edouard Bertrand, Michael A. Kiebler 25Jul2019 "Live cell imaging reveals 3′-UTR dependent mRNA sorting to synapses" Nature Communications, 2019; 10 (1) DOI: 10.1038/s41467-019-11123-x https://www.sciencedaily.com/releases/2019/07/190725102933.htm Neurobiology: Sushi for synapses Date: July 25, 2019 Source: Ludwig-Maximilians-Universität München The human brain is like a long-term construction site -- there's always something else to be done. This is certainly true of synapses, the functional links between nerve cells, which are constantly being strengthened, attenuated or demolished. Indeed, this process termed synaptic plasticity is the basis of our ability to store and recall information -- in other words, to learn. The instructions for the synthesis of necessary components, which are encoded in molecules known as messenger RNAs (mRNAs), are delivered to the specific synapses that need them by a specialized transport system. But how the blueprints reach their destinations is poorly understood. In order to learn more about the underlying mechanisms, cell biologist Professor Michael Kiebler and his group at the LMU Biomedical Center have now followed the transport of individual mRNAs to specific synapses. Their analysis shows that the same mRNA can be presented to potential addresses several times -- a system which the researchers compare to running sushi, the use of an 'endless' conveyor belt to enable patrons to pick and choose from the delicacies on offer. In order to serve the extensive network of synapses on a typically elongated process termed dendrite, the mRNAs must be transported from the nucleus in the cell body to the terminal branches at the end of the process. To monitor this process, the LMU team used cell cultures derived from neurons isolated from the hippocampus of the rat, which serves as a model for the human hippocampus. "We labelled specific mRNAs in living cells with a fluorescent dye, which enabled us to track their progress in real time," Kiebler explains. "This approach permitted us to determine, for the first time, whether or not a given molecule is delivered directly to a particular synapse, and whether different mRNAs are handled differently in this respect. In one case, we were able to follow how an mRNA entered one of the spine-like processes extended by a dendrite," he says. "Dendrites act as antennas that receive inputs from synapses on other cells." The observations revealed that one and the same mRNA may repeatedly circulate back and forth between the cell body and the nerve processes -- like sushi wending its way between the tables in a restaurant -- until it finds a synapse that needs it. Certain recognition sequences located in the segment of the mRNA that follows the stop codon (which marks the end of the protein-coding blueprint) serve as both the postage stamp and the address to direct the molecule to ensure that the molecule reaches the right region of the cell. "We have also demonstrated that, if the postage stamp is left intact, transport from the cell body to the neural processes is more effective and the mRNA is brought closer to the synapse than when it has been removed," says Kiebler. In addition, RNA-binding proteins such as Staufen2 play an important role in the regulation of mRNA transport by this cellular sorting system. Earlier studies had previously shown that Staufen2 is capable of binding several different mRNAs -- so that the same mechanism can distribute distinct mRNAs. In addition, the new report confirms early results which had suggested that uptake of the mRNA by the synapse depends on both the nature of the binding protein and the level of activity of the synapse. Taken together, the new data provide further details on the mechanisms underlying the delivery of proteins to synapses, and will have an impact on future efforts to understand the molecular basis of synaptic plasticity in mammals. 08********08 #] 25Jul2019 Alice Parker. Spiking Neural Networks & DNA. USC. Los Angeles. USA - Mindcode Lunch at restaurant after IJCNN2019 in Budapest I sent an email about DNA-SNNs /media/bill/SWAPPER/Projects - big/MindCode/Howell 050824 Junk DNA & NeuralNetworks, conjecture on directions and implications, IJCNN05 workshop panel presentation.ppt /media/bill/SWAPPER/Projects - big/MindCode/Howell 060215 Genetic specification of neural networks, draft concepts and implications.odt /media/bill/SWAPPER/Projects - big/MindCode/Howell 060215 Genetic specification of neural networks, draft concepts and implications.pdf /media/bill/SWAPPER/Projects - big/MindCode/Howell 060716 Genetic specification of recurrent neural networks, Initial thoughts, WCCI 2006 paper 1341.ppt /media/bill/SWAPPER/Projects - big/MindCode/Howell 060721 Genetic Specification of Recurrent Neural Networks Initial Thoughts, WCCI 2006 presentation.ppt /media/bill/SWAPPER/Projects - big/MindCode/Howell 150225 - MindCode Manifesto.odt /media/bill/SWAPPER/Neural Nets/Confabulation/Howell 110903 - Confabulation Theory, Plausible next sentence survey.pdf /media/bill/SWAPPER/Website/Social media/Howell 110902 – Systems design issues for social media.pdf /media/bill/SWAPPER/Website/Social media/Howell 111006 – Semantics beyond search.pdf /media/bill/SWAPPER/Website/Social media/Howell 111117 - How to set up & use data mining with Social media.pdf /media/bill/SWAPPER/Website/Social media/Howell 111230 – Social graphs, social sets, and social media.pdf http://www.billhowell.ca/Neural%20nets/MindCode/Howell%20050824%20Junk%20DNA%20&%20NeuralNetworks,%20conjecture%20on%20directions%20and%20implications,%20IJCNN05%20workshop%20panel%20presentation.ppt http://www.billhowell.ca/Neural%20nets/MindCode/Howell%20060215%20Genetic%20specification%20of%20neural%20networks,%20draft%20concepts%20and%20implications.odt http://www.billhowell.ca/Neural%20nets/MindCode/Howell%20060215%20Genetic%20specification%20of%20neural%20networks,%20draft%20concepts%20and%20implications.pdf http://www.billhowell.ca/Neural%20nets/MindCode/Howell%20060716%20Genetic%20specification%20of%20recurrent%20neural%20networks,%20Initial%20thoughts,%20WCCI%202006%20paper%201341.pdf http://www.billhowell.ca/Neural%20nets/MindCode/Howell%20060721%20Genetic%20Specification%20of%20Recurrent%20Neural%20Networks%20Initial%20Thoughts,%20WCCI%202006%20presentation.ppt http://www.billhowell.ca/Neural%20nets/MindCode/Howell%20150225%20-%20MindCode%20Manifesto.odt http://www.billhowell.ca/Social%20media/Howell%20111230%20–%20Social%20graphs,%20social%20sets,%20and%20social%20media.pdf http://www.billhowell.ca/Social%20media/Howell%20110902%20–%20Systems%20design%20issues%20for%20social%20media.pdf http://www.billhowell.ca/Social%20media/Howell%20111006%20-%20SPINE,%20Semantics%20beyond%20search.pdf http://www.billhowell.ca/Social%20media/Howell%20111117%20-%20How%20to%20set%20up%20&%20use%20data%20mining%20with%20Social%20media.pdf 08********08 #] 07Mar2018 DNA storage for real computers This DIRECTLY relates to MindCode https://spectrum.ieee.org/the-human-os/biomedical/devices/dna-data-storage-gets-random-access?utm_source=thehumanosalert&utm_campaign=thehumanosalert-03-07-18&utm_medium=email Prachi Patel 20Feb2018 | 15:00 GMT DNA Data Storage Gets Random Access : Researchers have devised a system to recover targeted files from 200 megabytes of data encoded in DNA enddoc