Computational Neuroscience

Computational Neuroscience

Computational Neuroscience

Peripheral Nervous System (PNS)

  • Somatic: Nerves connecting to voluntary skeletal muscles and sensory receptors.
    • Afferent Nerve fibers (incoming): Axons that carry info away from the periphery to CNS.
    • Efferent Nerve fibers (outgoing):Axons that carry info from the CNS outward to periphery.
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Central Nervous System (CNS)

CNS = Spinal Cord + Brain

Spinal Cord

  • Local feedback loops controls reflexes (“reflex arcs”)
  • Descending motor controls signals from the brain activate spinal motor neurons.
  • Ascending sensory axons convey sensory information from muscles and skin back to the brain.
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Major Brain Regions:

  1. The Hindbrain
    • Medulla Oblongata: Controls breathing , muscle tone and blood pressure.
    • Pons : Connected to the cerebellum and involved in sleep and arousal.
    • Cerebellum: Coordination and timing of voluntary movements , sense of equilibrium, language, attention….
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  1. Midbrain and Retic Fromation
    • Midbrain: Eye movement , visual and auditory reflexes
    • Reticular Formation:Modulates muscle reflexes , breathing and pain perceptions. Also regulates sleep , wakefulness and arousal.
    • Thalamus : “Relay station” for all sensory info ( except smell) to the cortex regulates sleep/wakefulness.
    • Hypothalamus: Regulates basic needs: fighting ,fleeing , feeding and mating.
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  1. The Cerebrum
    • Consist of :cerebral cortex , basal ganglia , hippocampus and amygdala.
    • Involved in perception and motor control , cognitive function, emotions memory and learning.
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  1. Cerebral Cortex:A Layer sheet of Neurons. Convoluted surface of cerebrum , about \( 1/8^{th} \) of an inch thick.
    • Approximately 30 billion neurons.
    • Each neuron makes about 10,000 synapses , approximately 300 trillions connections in total.
  • Six Layers of neurons
    • Relatively uniform in structure.
                       ___________
                      |    1      |  <---- Input from
                      |___________|
                      |           |
                      |   2+3     |-------> Output to "Higher"
                      |___________|           cortical area
                      |           |
      Input  ----->   |    4      |
                      |___________|
Output to             |     5     |
subcortical  <------- |___________|
regions      <------- |     6     |
                      |___________|
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Neural versus Digital Computing.

  • Device Count
    • Human Brain: \(10^{11}\) neurons.(each neuron ~ \(10^{4}\) connections.)
    • Silicon Chip: \(10^{10}\) transistors with sparse connectivity.
  • Device Speed
    • Biology has 100 μs temporal resolution.
    • Digital circuits are approaching a 100 ps clock(10 GHz).
  • Computing paradigm
    • Brain: Massively parallel computation and adoptive connectivity.
    • Digital Computers: sequential information processing via CPUs with fixed connectivity.
  • Capabilities
    • Digital Computers: excel in math and symbol processing..
    • Brains: better at solving ill-posed problems (speech,vision) One of the computational advantages of the brain is the ability to dynamically re-weight it’s connections in order to find solution to a problem.
  • Structure and organization of the brain suggests computational analogies
    • Information Storage: Physical/chemical structure of neurons and synapses.
    • Information Transmission: Electrical and chemical signaling.
    • Primary computing elements: Neurons
    • Computational basic: Currently unknown.
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