Long (teaching, novice) version powerpoint summarizing research paper file 'Paper EGr 183 Modeling Neural Networks in silico'.

1. “ How Did Nature Solve the information processing problems through the development of neural networks as well as the subsequent training and coordination with other networks?” Daniel Carlin, Daniel Cook, Joshua Mendoza-Elias Macro Micro Nano Macro Micro The Physical and Chemical Limitations that Nature Overcame were: solubility, dispersibility, surface area , conduction, signal modulation The Problem Nature HAS in Disease is: the severing of a neural network: paralysis, loss of sensation. Nano LTP-KO: NMDA Receptor Agonist CPP No change synaptic efficacy No Learning Loss of memory

2. “ How Did Nature Solve the information processing problems through the development of neural networks as well as the subsequent training and coordination with other networks?” Daniel Carlin, Daniel Cook, Joshua Mendoza-Elias Macro Micro Nano Macro Micro The Physical and Chemical Limitations that Nature Overcame were: solubility, dispersibility, surface area , conduction, signal modulation The Problem Nature HAS in Disease is: the severing of a neural network: paralysis, loss of sensation. Nano

5. Component Design Nervous System by function CNS (encased in bone) PNS Brain Spinal Chord Somatic System Autonomic System Parasympathetic Enteric Sympathetic Movement Coordination Receive external stimuli 8 cervical 12 thoraic 5 lumbar 5 sacral 11 coccygeal Cerrvical Spinal: C1-C4 Brachial Plexus: C5-T1 Lateral Chord: C5-C6 Posterior Chord: C6-C8 Medial Chord: C7-T1 Rest Relaxation Digestion Flight/Fight

6. Component Design Continued

7. Component Design continued

9. Material Selection: Neurons Sensory Neuron - Converts external stimuli to electrical signals - Chemoreceptors (e.g. olfactory signals) - Mechanoreceptors (e.g. joint position detection) Motor Neuron: - Stimulated by interneurons (small feedback loops or from ANS/PNS) - Activates effectors (glands, muscles, ...) Interneuron: - Data processing, stimulated by: - sensory neurons - other interneurons or both. - many unknown types remain A: Cortical pyramidal cell - primary excitatory neurons of cerebral cortex B: Purjinke cell of cerebellum. Transmit output of cerebral cortex C: Stellate cell - provides inhibitory input to cerebral cortex Example neurons from the brain: In Out Process Basic building blocks of nervous system are neurons. Hundreds of different types, many uncatalogued. Three main categories:

10. Material Selection: Glia in the CNS - Structural & metabolic support (feed neurons) - Transmitter reuptake: express transporters for neurotransmitters - Regulate ion concentrations (potassium) - Act as immune cells of the nervous system - Responsible for myelin sheathing of axons - Single oligodendrocyte myelinates 10-15 axons - Modulates axon conduction speed Astrocytes Microglial cells Oligodendrocyte Approx. 3:1 ratio of glial cells to neurons in the brain Modulate signal propagation and neurotransmitter uptake at the synaptic cleft Provide scaffold for neural development, help in injury recovery

12. Materials Performance http://vadim.oversigma.com/MAS862/Project.html Number of neurons (adult)* 20,000,000,000 - 50,000,000,000 Number of neurons in cerebral cortex (adult)* about 20,000,000,000 (some sources have incorrect number 8,000,000) Number of synapses (adult) 1014 (2,000-5,000 per neuron) Weight Birth 0.3 kg, 1 y/o 1 kg, puberty 1.3 kg, adult 1.5 kg Power consumption (adult) 20-40 Watts (0.5-4 nW/neuron) Percentage of body 2% weight, 0.04-0.07% cells, 20-44% power consumption Genetic code influence 1 bit per 10,000-1,000,000 synapses Atrophy/death of neurons 50,000 per day (between ages 20 and 75) Sleep requirement (adult) average 7.5 hours/day or 31% Normal operating temperature 37 ± 2°C Maximum firing frequency of neuron 250-2,000 Hz (0.5-4 ms intervals) Signal propagation speed inside axon 90 m/s sheathed, <0.1 m/s unsheathed Processing of complex stimuli 0.5s or 100-1,000 firings

16. “ How Did Nature Solve the information processing problems through the development of neural networks as well as the subsequent training and coordination with other networks?” Daniel Carlin, Daniel Cook, Joshua Mendoza-Elias Macro Micro Nano Macro Micro The Physical and Chemical Limitations that Nature Overcame were: solubility, dispersibility, surface area , conduction, signal modulation The Problem Nature HAS in Disease is: the severing of a neural network: paralysis, loss of sensation. Nano

17. Design Methodology: Define the function component to carry a load Material Selection Component Design Tentative component design Approximate stress analysis Tentative choice of material Assemble Materials Data Analysis of Materials Performance iterate Detailed Specifications and Design Choice of Production Methods Prototype Testing Establish Production Further Development iterate iterate iterate

19. Function of Neurons Continued - ES & CS

20. Functions of Neurons Continued - ES Gap Junctions

21. Functions of Neurons continued - CS

29. Detailed Specifications & Design - Neurochemical Transport Cell membrane Small molecules Large molecules Active transport Targeting AA, peptide frags, Ions, H 2 O FA, aggregate complexes Clathrin COPI/II  pH Signal Transduction ATP Kinase Endocytosis Secretion Processing Diffusion

30. Choice of Production Methods Cells arise from previous cells. Genome encoded functions: -juxtracrine & paracrine signaling Germline eventually develops tissues and cellular domains. Neural tissues develop from ectoderm Ideally, we would like to work with neural precursor cells that have undetermined cell fates. .

32. “ How Did Nature Solve the information processing problems through the development of neural networks as well as the subsequent training and coordination with other networks?” Daniel Carlin, Daniel Cook, Joshua Mendoza-Elias Macro Micro Nano Macro Micro The Physical and Chemical Limitations that Nature Overcame were: solubility, dispersibility, surface area , conduction, signal modulation The Problem Nature HAS in Disease is: the severing of a neural network: paralysis, loss of sensation. Nano

33. Introduction Synapse Video:

34. Neurotransmitters Release Function Functions of Neurotransmitters Neurotransmitters are chemicals that are used to relay, amplify and modulate signals between a neuron and another cell. According to the prevailing beliefs of the 1960s, a chemical can be classified as a neurotransmitter if it meets the following conditions: (i.) It is synthesized endogenously, that is, within the presynaptic neuron; (ii.) It is available in sufficient quantity in the presynaptic neuron to exert an effect on the postsynaptic neuron; (iii.) Externally administered, it must mimic the endogenously-released substance; and (iv.) A biochemical mechanism for inactivation must be present. Functions of Vesicles A vesicle is a relatively small and enclosed compartment, separated from the cytosol by at least one lipid bilayer. If there is only one lipid bilayer, they are called unilamellar vesicles; otherwise they are called multilamellar. Vesicles store, transport, or digest cellular products and waste. Functions of Protein Pumps Ion protein pumps establish the electrochemical gradient required for the transmission of action potentials. Functions of Receptors Receptors are responsible for binding neurochemicals and causing propogation of the signal being transmitted by the synapse. Receptors are also responsible for the upregulation of Cellular Pathways that form the basis of “learning.” Functions of Cellular Pathways in LTP and LTD Once a receptor binds a neurochemical, many changes begin to occur within the neuron that determines if the connection will be reinforced or weakened. The timing, frequency, and strength of stimulus will determine which genetic programs are activated. In terms of learning, genes associated with long-term potentiation (LTP) “learning” or long-term depression “unlearning” will be activated.

35. Synapse Function - Protein Pumps/Channels Types of Proteins Establishing Ion Gradients: Ion transporters use energy (ATP) to establish electrochemical gradients. Ion Channels use diffusion down chemical gradients. Ion balance will “prime” the neuron and also determine parameters of time as firing cannot occur until the neuron is reset. Resetting results in a return in ion balance and neurotransmitter levels.

36. Synapse Function - Neurotransmitters Types of Neurotransmitters: Acetylcholine - voluntary movement of the muscles Norepinephrine - wakefulness or arousal Dopamine - voluntary movement and motivation, &quot;wanting&quot; Serotonin - memory, emotions, wakefulness, sleep and temperature regulation GABA (gamma aminobutyric acid) - inhibition of motor neurons Glycine - spinal reflexes and motor behaviour Neuromodulators - sensory transmission-especially pain *Neurotransmitters achieve neurotransmission by binding neuroreceptors.

37. Synapse Function - Vesicles Types of Macromolecules in Vesicles: V and T Snares: Proteins that facililtate vesicle fusion and release COPI/COPII/Clathrin: Protein responsible for targeting of vesicles. Neurotransmitters are loaded onto vesicles for release into the synaptic cleft.

38. Receptors are any membrane bound protein that binds a neurochemical and facilitates synaptic transmission. There is a large repertoire of receptors that bind neurochemicals in different ways and illicit different effects in the neuron. This is achieved by differences in: (i.) affinity for the neurochemical (ii.) specialized receptors that activate specific secondary mechanisms and/or cellular pathways. Synapse Function - Receptors A B C

39. Long-term potentiation (LTP) is the mechanism by which neural networks are “trained”. LTP: High fast peak rise in Ca+2  increase efficacy  Activates LTP second messengers, phosphorylation pathways (short-term)  increrase Ampa receptors  genetic pathways e.g. thicker axons (long-term) *All proteins involved in LTP have not been fully characterized. Synapse: The Process of Learning - LTP and LTD

40. Repeated stimulation causes changes and activation of LTP causes reinforcement of learning. Synapse: The Process of Learning cont’d - LTP and LTD

41. Synapse: The Process of Learning cont’d - LTP and LTD Long-term depression (LTD) is The mechanism by which neurons are “detrained.” LTD: Slow rise peak in Ca+2  decrease synaptic efficacy  Activated LTP secondary messengers (phosphatases) (short-term)  decrease Ampa Receptors  genetic pathways e.g. thinner axons (long-term). Similar short-term and long-term changes are observed as in LTP. *All of the proteins involved involved in LTD have not been fully characterized.

42. Timing is important for LTP and LTD. -Connections that are established by LTP are specific . -Connections may be associative in that “neurons that fire together, fire together.” Synapse: The Process of Learning cont’d - Spike Driven Plasticity

43. “ How Did Nature Solve the information processing problems through the development of neural networks as well as the subsequent training and coordination with other networks?” Daniel Carlin, Daniel Cook, Joshua Mendoza-Elias Macro Micro Nano Macro Micro The Physical and Chemical Limitations that Nature Overcame were: solubility, dispersibility, surface area , conduction, signal modulation The Problem Nature HAS in Disease is: the severing of a neural network: paralysis, loss of sensation. Nano LTP-KO: NMDA Receptor Agonist CPP No change synaptic efficacy No Learning Loss of memory

45. NMDAr in LTP NMDAr (i.) Binds glutamate (ii.) Relieves Mg+2 block (iii.)Allows for characteristic rapid spike in Ca +2 levels that (iv.) cause LTP mechanisms to be activated.

47. Detailed Specifications and Design

54. Evolutionary tree of GRIN1 http://www.ncbi.nlm.nih.gov/blast/treeview/blast_tree_view.cgi?request=page&rid=SSRCRD3F013&dbname=nr&queryID=lcl|30291

55. That reminds me of…

56. “ How Did Nature Solve the information processing problems through the development of neural networks as well as the subsequent training and coordination with other networks?” Daniel Carlin, Daniel Cook, Joshua Mendoza-Elias Macro Micro Nano Macro Micro The Physical and Chemical Limitations that Nature Overcame were: solubility, dispersibility, surface area , conduction, signal modulation The Problem Nature HAS in Disease is: the severing of a neural network: paralysis, loss of sensation. Nano LTP-KO: NMDA Receptor Agonist CPP No change synaptic efficacy No Learning Loss of memory

57. Remember the role of NMDAr in LTP?

58. Loss of function: NMDA Receptor Antagonist Knocking out LTP Agonist - def - An antagonist is a chemical agent that is able to prevent the normal functioning and of a neuroreceptor. CPP ( 3-(2-Carboxypiperazin-4-yl)propyl-1-phosphonic acid ) blocks the normal functioning of the NMDA receptor and by causing loss of function of the NMDA receptor. The NMDAr is an important part component in the mechanisms of LTP and LTD. (C.) Loss of LTP causes the inability of neurons to create synaptic pathways (attractors) that will store information. Meanwhile LTD will cause information degradation by “detraining” established synaptic pathways. The result is a loss of memory. (A.) Loss of LTD causes the inability to degrade information. (B.) Normal functioning of LTP and LTD in concert Nature Neuroscience . 5 (1): 48-52 (2002) Nature Neuroscience . 5 (1): 6-8. (2002)

59. Recovery of CPP Antagonist KO effect The antagonist effect of CPP can be compensated by increasing the amount of NMDA receptors, increasing the amount of AMPA receptors, or a combination of both. In addition, overtime receptors will be replaced and new ones that have not bound CPP will begin to compensate. The process of replacement can also be accelerated by using monoclonal antibodies against CPP to aid in elimination of CPP bound NMDA receptors from the body. Increase NMDAr expression Increase AMPAr expression Antibodies against CPP Time

62. Foreward Engineering: Building a “Wet Drive” Daniel Calrin B.S.E. Daniel Cook Joshua Mendoza-Elias

64. Short-term and Long-term Effects

66. The Basis of Hebbian Learning

67. Foundation for our Computer Model

68. Types of Neuron Inhibitory Neurons Input Neuron Output Neuron Neuron is voltage-clamped, presynaptic to all neurons in model Inhibitory neurons depress post-synaptic neurons Excitatory Neurons Average firing rates solved at each time step Learning rule determines change in synaptic strength inhibitory synapse excitatory synapse Key: 1 2 3 4 α = - 1 4

69. Synaptic strengths 1.0 1.0 t=t 0 t=t 0+1 + α In phase Out of phase + α Two neurons are firing full-speed: Strengths increase by factor of alpha 1.0 0.0 - β One neuron v i is firing but v j is not: Strengths decrease by factor of beta - β v i v j v i v j v i v j v i v j

70. Inhibitory Neurons t=t 0 t=t 0+1 Excitatory Inhibitory = ...+ w i,j v i +... = ...- w i,j v i +... An inhibitory neuron v i is firing, depressing the post-synaptic neuron Weighted v i is summed negatively into v j Weighted v i is summed positively into v j An excitatory neuron v i is firing, potentiating the post-synaptic neuron v i v j v j v i v i v j v i v j

73. Output Neuron Maxima & Minima Local max near minimum Local min near maximum Maximum at maximum time firing rate Input Neuron

75. Biological parallel with In Silico Starvoytov et al. 2005 Light-drected stimulation of neurons on silicon wafers. J Neurophysiol 93 : 1090-1098.