02knov26n3

02knov26n3 - Harvard-MIT Division of Health Sciences and...

Info iconThis preview shows pages 1–8. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Harvard-MIT Division of Health Sciences and Technology HST.508: Genomics and Computational Biology Net2: Last week's take home lessons • Biology to aid Computing … to aid biology • Molecular nano-computing (DNA) • Self-assembly (nano-I/O ) • Intra cellular network computing (oscillators) • Genetic algorithms (multi-align) • Neural nets (exons) 1 Net3: Today's story & goals • Multi-cellular models -- e.g. sensory integration • Systems biology, simulation & integration • Organ systems • Multi-organism - Ecological modeling – predator/prey - host/parasite - HIV • Global & socioeconomic considerations • Education – Model evaluation & sharing 2 Faster than exponential Example - Evolution of Computer Power/Cost 3 The human neural net See fig The retina's 10 million detections er second [.02 g] ... a risky (http://www2.ncsu.edu/ncsu/univ_relations/news_services/tiparch.html) xtrapolation ... 100 trillion Edge & motion detection instructions per second to emulate ( examples ) (http://iris.usc.edu/Vision-Notes/bibliography/motion-i687.html) the 1,500 gram human brain. ...Computer power for a given price oughly doubled each year in the 990s, ... thirty more years at the resent pace would close the illionfold gap.” (Morovec99) (http://cart.frc.ri.cmu.edu/users/hpm/project.archive/robot.papers/ 1999/SCIAM.robot.html) 4 Olfactory integration: glomeruli 1000 receptors, one per cell, +/-2sd olfactant concentration c threshold span 6.8 log10 units See fig (http://apu.sfn.org/content/Publications/BrainBriefings/smell.html) 5 Basic olfactory tasks ) Odor memory and recognition. ) Background elimination (one known + unknown thoroughly mixed) ) Component separation. (few known odors mixed) ) Odor separation (turbulent unknowns) cov i = cov min (c t /c thresh,t ) (1 or f it ) + cov min (c b /c thresh,b ) (1 or f ib ) c i = (c thresh /f it )(cov iu /cov min ) t= target , b=background, u= unknown, receptors i=1 to 1000. minimum coverage for concentration threshold. f it = fraction bound random 1 to 10-6 (log uniform pdf) Hopfield 1999; PNAS 96:12506-11 (http://www.hopfield.net/~john/pnas.html) 6 Odor space and olfactory processing: Collective algorithms & neural implementation 80 adapting neurons, two sniffs: 100-500 msec has a mixed odor 50*x + 1000*y. At 500 msec 75*x + 1100*y. The sniff at 100 milliseconds strongly activates more than half the neurons, after which they adapt. The changed sniff at 500 milliseconds is almost invisible. b) as in a), but the y-axis = firing rate at the time of each action potential. The second sniff is now clearly visible, and most spikes appear to belong to one of three patterns. A 20% spread in D was included to produce parameter-spread noise....
View Full Document

This note was uploaded on 01/24/2010 for the course HST. 508 taught by Professor Dr.georgechurch during the Fall '02 term at MIT.

Page1 / 52

02knov26n3 - Harvard-MIT Division of Health Sciences and...

This preview shows document pages 1 - 8. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online