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Biologically Inspired Intelligent Systems

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  1. Biologically Inspired Intelligent Systems Lecture 3 Dr. Roger S. Gaborski

  2. Quiz on Thursday • Lectures 1-3, recommended videos, etc.

  3. RECALL FROM LAST CLASS:Levels of OrganizationWhich level do we want to investigate? Adapted from “The Computational Brain,” Churchland and Sejnowski

  4. Levels of Implementation • Neuron level • Design individual neuron models • Design individual ‘circuits’ • Train networks of neurons • Evolve Networks • Function level • Algorithmic implementation of functions (hearing, vision, etc) • Abstract level • AI techniques

  5. In order to model a system, we need data • How do we get information about the brain? • In humans, imaging • Primates and other creatures – imaging, electrodes

  6. New (or Improved) Data Collection Techniques • Individual neuron recordings (100’s) • New dyes to observe brain metabolism • Brain imaging (MRI, fMRI, CAT, PET, …)- • Computer Simulation and Analysis • Opportunity to apply analysis and modeling techniques to gain new insights to the functioning of our brain

  7. Magnetic Resonance ImagingMRI • Visual internal structures in the body • Good at imaging soft tissue

  8. Functional MRI • Visualize neural activity • Indirect measurement • Commonly used in brain mapping • 2-3 mm resolution

  9. Functional MRIHearing Words and Speaking Words

  10. Seeing Words and Thinking About Words

  11. Functional MRI A 20-year old female drinker A 20-year old female nondrinker Response to the spatial working memory task. Brain activation is shown in bright colors.

  12. PET Scan (Positron Emission Tomography) • Measures emissions from radioactively labeled metabolically active chemicals • Tumors, Alzheimer’s disease cause change in metablism which can be detected by PET scans

  13. PET Scan 20 Year Old 80 Year Old

  14. PET Scan of Normal Brain and Alzheimer's Disease Brain

  15. Question?? • Can you analyze fMRI data and determine what someone is thinking??

  16. Can you analyze an fMRI signal to determine what you are thinking? • •

  17. Neural Networks as Systems Networks have specialized architecture in order to perform specific information processing actions Single neurons connected together to form networks of neurons Central nervous system strongly depends on interaction of many specialized subsystems

  18. System LevelWhat Function is Performed? • Evidence from: • Brain damage • Areas selectively activated during a particular activity (detected by fMRI, PET, etc.) • What inputs go to the region (record activity of neurons) • Which regions receive outputs from region being investigated?

  19. How Does a Particular Region Perform a Function? • How many inputs from each source received by a neuron in a given region? • Where do inputs terminate on a cell? • Rules and mechanisms that determine synaptic connectivity and modifiability • How is information represented by neural responses

  20. We need more than a ‘system level’ understanding of how the brain works We need to understand how each brain region operates How does each area perform its computations?

  21. At the Neuron Level • What calculations are performed by a single neuron or group of neurons?? • How are connections altered and why? • How is information about the problem to be solved represented and stored? • How do neurons in a region interact? • Together, how do these features enable useful computations to be performed?

  22. Our ability to • Perform complex behaviors • Process complex concepts • Learn • Remember • Depends on communications between a large number of neurons • Communication between neurons depend of action potentials (spikes) and synapses

  23. Connectionism • Compute using large number of elements • Elements do NOT model real neurons • Learning rules are not biologically feasible (backward error propagation) • This course is not about common connectionism approaches, i.e. backward error propagation • We are interested in how real neurons in networks compute • Basis for understanding the brain

  24. Neuron Models

  25. But First, Real Neurons

  26. How Do Real Neurons ‘Operate’ ?

  27. A Careful Examination of Neurons and the Flow of Information • BACKGROUND INFORMATION: Three factors influence the flow of ions into and out of neurons: • Charge • Diffusion • Concentration

  28. Charge • Recall,’ like charges’ repel (both positive or both negative), ‘opposite charges’ attract (positive and negative) • Ions are charged • Sodium, Na+ • Potassium, K+ • Chloride, Cl- • Bicarbonate HCO3- • Protein-

  29. Diffusion • Diffusion – random movement of ions or molecules from a high concentration to a low concentration • Illustration: • Place a drop of red dye into a container of clean water • The red dye will distribute itself equally moving from a high concentration to areas of low concentration

  30. Concentration Gradient • Concentration Gradient – the difference in concentration of a material between two spatial regions • Voltage Gradient- when a salt solution composed of positive and negative ions is poured into a glass of water • Ions move down a voltage gradient from area of high charge to an area of low charge • Positive and negative ions distribute themselves equally (electrostatic gradient)

  31. Cell Membrane V -+ +- -+ +- -+ -+ +- -+ +- -+ -+ + -+ + -+ - - - Add salt to a container of water with a barrier (cell Membrane). Ions cannot pass through Membrane now has holes so that only Cl- ions can pass. After a period of time Cl- ions will diffuse to right hand side. Cl- is not equally distributed because some are attracted to positive charges sodium, Na+ resultingin a Voltage difference across the membrane

  32. Axon • Assume axon is modeled by a cylinder. • The walls of the cylinder is the membrane. • This membrane contains ion channels that when open allow specific ions to pass • At rest, the channels are closed

  33. Action Potential Image from:

  34. Resting Potential • The unequal distribution of positively and negatively charged ions across the membrane results in a voltage potential • Resting potential is typically -70mv, but can vary for different neurons (inner surface of membrane more negative than the positive outer surface) - - - - - - + + + + +

  35. Action Potential • The action potential is a rapid change of the membrane potential caused by the opening and closing of ion channels • At the start of an action potential the sodium channels open (the nerve is stimulated) • What will the voltage difference between the inside and the outside of the axon have on the sodium ions?

  36. Action Potential • At the start of an action potential the sodium channels open (when the nerve is stimulated) • What will the voltage difference between the inside and the outside of the axon have on the sodium ions? • The positively charged sodium ions will move to the inside (opposite charges attract) • As more positive ions enter the axon it becomes more positively charged - + + + + + - - - - -

  37. Movement of Action Potential Down the Axon • You can think of the action potential being generated by the flow of ions crossing the membrane as first the sodium channel opens (and sodium ions enter) , then closes and the potassium channel opens (potassium ions defuse out), then closes • This sequence of opening and closing of channels flows down the length of the axon resulting in the action potential flowing down the length of the axon


  39. Segment of Axon Model with Ions-Resting Potential K+ Na+ Cl- HCO3- Protein- Ion Channels: Na+ Cl-

  40. Resting Phase (State) : Sodium ion concentration greater outside and Potassium ion concentration greater inside A few potassium channels are open at this time allowing a few K+ to move back and forth across the membrane K+ Na+ Cl- HCO3- Protein- Ion Channels: Na+ Cl-

  41. What happens when sodium gates open? ANSWER: Sodium ions would move in since random movement would result in more ions going in that out K+ Na+ Cl- HCO3- Protein- Ion Channels: Na+ Cl-

  42. Depolarization • The initial phase of the action potential is the depolarization phase • The depolarization stage is initiated by depolarizing stimulus arriving at this segment of the axon • A few sodium ion channels open and sodium ions enter the axon • The positive Na+ depolarizes the membrane making it less negative bringing it closer to the threshold

  43. Depolarization • If the depolarization reaches the threshold more sodium channels will open increasing the flow of sodium ions across the membrane • At the end of the depolarization phase the voltage inside the axon is positive and the concentration of sodium ions inside the axon is greater than at the start of the depolarization phase • At this time in the process the sodium ions will tend to stay outside of the axon and not move through the channel because the inside of the axon is now positive compared to the outside

  44. Repolarization • The sodium channels close when the inside of the axon is approximately +30mv to +50mv • The potassium channels now open allowing potassium ions (K+) to leave the inside of the axon. The concentration of potassium ions is lower outside of the axon • The membrane potential now drops back towards the resting potential as the positively charged potassium ions leave the inside of the axon

  45. Undershoot • Since even more potassium ion channels are open than in the resting stage allowing more positively charged potassium ions to leave the cell and the membrane potential drops below the resting potential • The potassium channels that opened during the action potential now close and the membrane returns to its resting potential

  46. Action Potential •

  47. Pre-Synaptic and Post-Synaptic Neurons • The action potential travels down the axon from the cell body to the synapse (pre-synaptic neuron) • At the synapse the electrical signal is converted to a chemical signal • The chemical signal is propagated to the post-synapse neuron’s dendrite (Dendrites extend from the post-synaptic cell body and receive inputs from other pre-synaptic neurons through connections called synapses) • Neurons also communicate with muscle cells through chemical synapses


  49. Synapse • Most General: • • •

  50. Neuron/muscle Communication • Action potential reaches the axon terminal • The depolarization of the terminal membrane causes Ca++ channels to open • Ca++ ions enter the terminal • Ca++ ions triggers the release of the neurotransmitter acetylcholine into the synaptic cleft • The neurotransmitter binds with a receptor on the post-synaptic membrane • A channel on the post-synaptic membrane opens and Na+ ions enter the post-synaptic cell • This accomplishes the transmission of information from the pre-synaptic cell to the post-synaptic cell • The neurotransmitter is broken down by an enzyme and the ion channel closes