I don't think that each inheritor receives a full set of the original's memories. But there may have *evolved* in spite of the obvious barriers, a means of transferring primary or significant experience from one organism to another in genetic form... we can imagine such a thing given this news!
On 12/11/08, Matt Mahoney <[email protected]> wrote: > --- On Thu, 12/11/08, Eric Burton <[email protected]> wrote: > >> You can see though how genetic memory encoding opens the door to >> acquired phenotype changes over an organism's life, though, and those >> could become communicable. I think Lysenko was onto something like >> this. Let us hope all those Soviet farmers wouldn't have just starved! >> ;3 > > No, apparently you didn't understand anything I wrote. > > Please explain how the memory encoded separately as one bit each in 10^11 > neurons through DNA methylation (the mechanism for cell differentiation, not > genetic changes) is all collected together and encoded into genetic changes > in a single egg or sperm cell, and back again to the brain when the organism > matures. > > And please explain why you think that Lysenko's work should not have been > discredited. http://en.wikipedia.org/wiki/Trofim_Lysenko > > -- Matt Mahoney, [email protected] > > >> On 12/11/08, Matt Mahoney <[email protected]> >> wrote: >> > --- On Thu, 12/11/08, Eric Burton >> <[email protected]> wrote: >> > >> >> It's all a big vindication for genetic memory, >> that's for certain. I >> >> was comfortable with the notion of certain >> templates, archetypes, >> >> being handed down as aspects of brain design via >> natural selection, >> >> but this really clears the way for organisms' >> life experiences to >> >> simply be copied in some form to their offspring. >> DNA form! >> > >> > No it's not. >> > >> > 1. There is no experimental evidence that learned >> memories are passed to >> > offspring in humans or any other species. >> > >> > 2. If memory is encoded by DNA methylation as proposed >> in >> > >> http://www.newscientist.com/article/mg20026845.000-memories-may-be-stored-on-your-dna.html >> > then how is the memory encoded in 10^11 separate >> neurons (not to mention >> > connectivity information) transferred to a single egg >> or sperm cell with >> > less than 10^5 genes? The proposed mechanism is to >> activate one gene and >> > turn off another -- 1 or 2 bits. >> > >> > 3. The article at >> http://www.technologyreview.com/biomedicine/21801/ says >> > nothing about where memory is encoded, only that >> memory might be enhanced by >> > manipulating neuron chemistry. There is nothing >> controversial here. It is >> > well known that certain drugs affect learning. >> > >> > 4. The memory mechanism proposed in >> > >> http://www.ncbi.nlm.nih.gov/pubmed/16822969?ordinalpos=14&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DefaultReportPanel.Pubmed_RVDocSum >> > is distinct from (2). It proposes protein regulation >> at the mRNA level near >> > synapses (consistent with the Hebbian model) rather >> than DNA in the nucleus. >> > Such changes could not make their way back to the >> nucleus unless there was a >> > mechanism to chemically distinguish the tens of >> thousands of synapses and >> > encode this information, along with the connectivity >> information (about 10^6 >> > bits per neuron) back to the nuclear DNA. >> > >> > Last week I showed how learning could occur in neurons >> rather than synapses >> > in randomly and sparsely connected neural networks >> where all of the outputs >> > of a neuron are constrained to have identical weights. >> The network is >> > trained by tuning neurons toward excitation or >> inhibition to reduce the >> > output error. In general an arbitrary X to Y bit >> binary function with N = Y >> > 2^X bits of complexity can be learned using about 1.5N >> to 2N neurons with ~ >> > N^1/2 synapses each and ~N log N training cycles. As >> an example I posted a >> > program that learns a 3 by 3 bit multiplier in about >> 20 minutes on a PC >> > using 640 neurons with 36 connections each. >> > >> > This is slower than Hebbian learning by a factor of >> O(N^1/2) on sequential >> > computers, as well as being inefficient because sparse >> networks cannot be >> > simulated efficiently using typical vector processing >> parallel hardware or >> > memory optimized for sequential access. However this >> architecture is what we >> > actually observe in neural tissue, which nevertheless >> does everything in >> > parallel. The presence of neuron-centered learning >> does not preclude Hebbian >> > learning occurring at the same time (perhaps at a >> different rate). However, >> > the number of neurons (10^11) is much closer to >> Landauer's estimate of human >> > long term memory capacity (10^9 bits) than the number >> of synapses (10^15). >> > >> > However, I don't mean to suggest that memory in >> either form can be >> > inherited. There is no biological evidence for such a >> thing. >> > >> > -- Matt Mahoney, [email protected] > > > > ------------------------------------------- > agi > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/ > Modify Your Subscription: > https://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com > ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=123753653-47f84b Powered by Listbox: http://www.listbox.com
