Natural neural networking

1,356 views

Published on

Published in: Health & Medicine, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,356
On SlideShare
0
From Embeds
0
Number of Embeds
108
Actions
Shares
0
Downloads
31
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • Divergence of Signals Passing ThroughNeuronal PoolsOften it is important for weak signals entering a neuronal pool to excite far greater numbers of nerve fibers leaving the pool. This phenomenon is called divergence. Two major types of divergence occur and have entirely different purposes.An amplifying type of divergence is shown in Figure 46–11A.This means simply that an input signal spreads to an increasing number of neurons as it passes through successive orders of neurons in its path. This type of divergence is characteristic of the corticospinalpathway in its control of skeletal muscles, with a single large pyramidal cell in the motor cortex capable, under highly facilitated conditions, of exciting as many as 10,000 muscle fibers.The second type of divergence, shown in Figure 46–11B, is divergence into multiple tracts. In this case, the signal is transmitted in two directions from the pool. For instance, information transmitted up the dorsal columns of the spinal cord takes two courses in the lower part of the brain: (1) into the cerebellum and (2) on through the lower regions of the brain to the thalamus and cerebral cortex. Likewise, in the thalamus, almost all sensory information is relayed both into still deeper structures of the thalamus and at thesame time to discrete regions of the cerebral cortex.Convergence of SignalsConvergence means signals from multiple inputs uniting to excite a single neuron. Figure 46–12A shows convergence from a single source.That is, multiple terminals from a single incoming fiber tract terminate on the same neuron. The importance of this is thatneurons are almost never excited by an action potential from a single input terminal. But action potentials converging on the neuron from multiple terminals provide enough spatial summation to bring the neuron to the threshold required for discharge.Convergence can also result from input signals (excitatory or inhibitory) from multiple sources, as shown in Figure 46–12B. For instance, the interneurons of the spinal cord receive converging signals from (1) peripheral nerve fibers entering the cord, (2) propriospinal fibers passing from one segment of the cord to another, (3) corticospinal fibers from the cerebral cortex, and (4) several other long pathways descending from the brain into the spinal cord.Then the signals from the interneurons converge on the anterior motor neurons to control muscle function.Such convergence allows summation of information from different sources, and the resulting response is a summated effect of all the different types of information.Convergence is one of the important means by which the central nervous system correlates, summates, and sorts different types of information.
  • White Matter vs. Gray MatterBoth the spinal cord and the brain consist of white matter = bundles of axons each coated with a sheath of myelingray matter = masses of the cell bodies and dendrites — each covered with synapses.In the spinal cord, the white matter is at the surface, the gray matter inside. In the brain of mammals, this pattern is reversed. However, the brains of "lower" vertebrates like fishes and amphibians have their white matter on the outside of their brain as well as their spinal cord
  • NervesNerves are bundles of neurons; either long dendrites and/or long axons.There are no cell bodies in nerves. The cell bodies are in the ganglia (PNS) or nuclei (in gray matter of the CNS).Most nerves contain both kinds of neurons (sensory and motor). The sensory neurons conduct information to the CNS, the motor neurons conduct away from the CNS.All of the neurons in some nerves conduct in the same direction.   These nerves contain either sensory or motor neurons.
  • Natural neural networking

    1. 1. Natural Neural Network Dr PS Deb MD, DM Neuro.
    2. 2. Basic Neural Network
    3. 3. Excitation/ Inhibition
    4. 4. Inhibition
    5. 5. Disinhibition
    6. 6. Neurotransmitter Excitatory/ Inhibitory Excitatory Inhibitory 1. Glutamate 1. GABA 2. Dopamine 2. Dopamine 3. Acetyl choline 3. Serotonin 4. Epinephrine 5. Nor Epinephrine
    7. 7. Convergence and Divergence
    8. 8. Reverberatory (Oscillatory) Circuit as a Cause of Signal Prolongation.
    9. 9. Nervous System Schema Brain Spinal Cord Nerves Gray Matter White matter Central NucleiBrain Stem
    10. 10. Types of Neural Network
    11. 11. Central Neural network
    12. 12. White and Gray Matter
    13. 13. Spinal Cord
    14. 14. Spinal Cord Section
    15. 15. Peripheral Nerves
    16. 16. Cranial Nerve network
    17. 17. Sensory Network
    18. 18. Somatosensory Network
    19. 19. Propioceptive Network
    20. 20. Auditory Network
    21. 21. Visual Network
    22. 22. Visual Network
    23. 23. Central Visual Network AIT = anterior inferior temporal area; CIT = central inferior temporal area; LIP = lateral intraparietal area; Magno = magnocellular layers of the lateral geniculate nucleus; MST = medial superior temporal area; MT = middle temporal area; Parvo = parvocellular layers of the lateral geniculate nucleus; PIT = posterior inferior temporal area; VIP = ventral intraparietal area.) (Based on Merigan and Maunsell 1993.)
    24. 24. Motor Network
    25. 25. Motor control Network
    26. 26. Cerebellar Network
    27. 27. Basal Ganglia Network
    28. 28. Corticospinal Motor System
    29. 29. Autonomic Nervous Network
    30. 30. Sensory Motor Integration: Reflex
    31. 31. Withdrawal Reflex
    32. 32. Withdrawal and Crossed Extensor Reflex
    33. 33. Polysynaptic Reflex
    34. 34. Intracerebral Neural Network
    35. 35. Secondary Sensory Network
    36. 36. Memory storage Network
    37. 37. Cognitive Network
    38. 38. Cholinergic Network
    39. 39. Serotonin Network
    40. 40. Dopamine Network
    41. 41. Norepinephric Network
    42. 42. Glutamate Network
    43. 43. GABA Network
    44. 44. Consciousness network (RAS)
    45. 45. Neural Net Universal Net

    ×