Unformatted text preview: Overview of Brain Structure telencephelon - neocortex diencephelon - limbic system mesencephelon - midbrain metencephelon - brainstem, cerebellum myelencephalon - spinal cord Neocortex cerebrum - 2 hemispheres corpus callosum lobes occipital temporal parietal frontal matter white - axons of neurons, tracts gray - cell bodies 1 Lobes occipital visual cortex parietal somatosensory spatial orientation temporal auditory cortex speech understanding visual, object/face recognition autobiographical memory frontal, prefrontal working memory planning cognitive assessment of emotion personality judgment, decision making.... motor, pre-motor etc., etc., most recently evolved part of brain Limbic system - Mammalian Brain hippocampus - memory cingulate gyrus - brain wrapped around the CC. entorhinal cortex thalamus - sensory relay amygdala - emotion detection/response (esp. fear) 2 Midbrain and Brain Stem - Reptilian Brain oldest parts of the brain Reptilian > 500 million years old cerebellum - the "little brain" of note for this course Subcortical sensory systems superior colliculus, optic tectum inferior colliculus midbrain structures in the auditory system function??????? Generic Neuron approximately 1011 in human brain! Parts of a generic neuron soma - cell body axon - up to 1 meter long dendrites - many >10,000 different kinds Nerve impulse - Action potential resting potential -70 millivolts Na+ K+ outside of the neuron Na+ K+ protein- on the inside Na+ constantly being pumped out to maintain -70mv Sodium pump activation - disruption of sodium pump Na+ rushes in, increases positivity inside K+ rushes out, returns toward resting impulse propagates down axon 3 Synapse end of axon synaptic vessicles filled with neurotransmitter fuse with the side of the membrane spill contents into synaptic cleft Time Course of Action Potential refractory period 1 millisecond 800-900 times per second Properties of Soma and Dendrites graded potential decremental conduction temporal and spatial summation Properties of axons non-graded potential (all or none) threshold Summary of the Generic Neuron Parts soma, dendrites, axon Statistics 10 to the eleventh of them 10,000 different kinds Physiology conduction of nerve impulse, action potential, fire function transmits a signal intensity of signal related to firing rate non-decremental conduction more intense firing -> more spikes/second 4 Receptive field (definition) - part or aspect of the world that can cause a change in the firing rate of a neuron response possibilities inhibit - firing rate decreases excite - firing rate increases Levels of the Receptive Fields in the Brain most peripheral receptor connected to a neuron Mechanoreceptor - under skin Levels of the Receptive Fields in the Brain Levels of the Receptive Fields in the Brain higher up neuron in a brain region that receives a reasonably direct projection from the "outside" Higher still..... no direct link to sensory world..... face cells..? hand cells...? invariance to retinal input? Modalities of Receptive Fields in the Brain vision hues spatial quality, line orientations motion - direction, speed audition frequency spatial location... ? touch chemical senses 5 3 Practical Questions What can a neuron do? What can a neuron know? How can a neuron learn? What can a neuron do? compute a threshold function of a spatial and temporal integration spatial which dendrites extent and direction of dendrites temporal time window of refractory period input x2 x3 x4 x5 x6 x1 dendrites "The amount of activity at any given point in the cortex is the sum of all the other points to discharge it...." Sxi axon William James (1890) Psychology: The Briefer Course Xn neurotransmitter input to the nth dendrite How can this be useful? Theoretical model of a neuron (McCulloch & Pitts , 1943) A logical calculus of the ideas immanent in nervous activity 1. neuron = binary device with (binary) inputs excitatory inputs add linearly inhibitory inputs prevent the neuron from firing 2. neuron has fixed threshold 3. neuron has binary output From J. A. Anderson (1998). An introduction to neural networks 6 Realization of Logical Propositions AND t =1.5 1 1 -> 1 1 0 -> 0 0 1 -> 0 0 0 -> 0 OR t =.5 1 1 -> 1 1 0 -> 1 0 1 -> 1 0 0 -> 0 Results & Implications by combining logical propositions into networks any finite logical expression can be realized paper had little effect in neuroscience literature paper had enormous effect in computer science binary operations, logic gates, computation...etc! "It is worth mentioning that the neurons of the higher animals are (relay-like) elements; They have an all-or-none character, that is, two states: quiescent and excited...an excited neuron emits the standard stimulus along many lines (axons). Such a line can, however, be connected in two different ways to the next neuron: First, in an excitatory synapsis, so that the stimulus causes excitation of that neuron: Second, in an inhibitory synapsis, so that stimulus absolutely prevents the excitation of that neuron by any stimulus. Following Pitts and McCulloch, we ignore the more complicated aspects of neuron functioning: Thresholds, temporal summation, relative inhibition...etc....It can easily be seen that these simplified neural functions can be imitated by telegraph relays or by vacuum tubes." John von Neumann father of computer science 1945 technical report Synaptic efficacy = how easy it is for input from a dendrite to excite the neuron What can a neuron know? How is one neuron different than another? anatomically- hardware number and extent of dendrites spatial outlay of the dendrites (receptive field) physiologically - software physically identical --- how can they differ?
w2x2 w3x3 w4x4 w5x5 w6x6 w1x1 dendrites Swixi axon inhibition w < 1 excitation w > 1 Xn neurotransmitter input to the nth dendrite wn weight with which input to the nth dendrite impacts neuron 7 Mach Bands - A simple visual illusion perception, physiology and neural computation What is really there What we perceive Lateral Inhibition Hartline, Wagner, & Ratliff (1956) horseshoe crab physiology Electrophysiology Experiment light on A record from cell A ||||||||||||||||||||||||||||||| light on B record from cell B ||||||||||||||||||||||||||||||| light on A and B record from cell A || | | | || | A B electrode 8 100 200 What we perceive -10% -10% -10% -10% 80 80 70 170 160 160 What can a neuron learn? Hebb (1949) - Organization of Behavior modification of the synaptic efficacy of dendrites changes in the values of the wi values of the model When axon A is near enough to cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency as one of the cells that fires B is A' increased" increased" Hebb (1949) "When 2 elementary brain processes have been active together or in immediate succession, one of them, on recurring, tends to propagate its excitement onto the other....." other....." "The amount of activity at any given point in the cortex is the sum of all other points to discharge it, such tendencies being proportionate to 1.) the number of times the excitement of each point may have accompanied that of the point in question; 2.) the intensity of such excitements; and 3.) the absence of rivals ....." .." William James (1892) - Psychology: Briefer Course associative memory 9 Perceptron - Rosenblatt (1958) Simple Operation Rules
output neurons Neural model - input from retina - output neuron "pattern detector" detector" set wi's to random values a set of input patterns to classify a set of targets apply first input pattern and compute output o = Swixi wi w2 w3 if correct - do nothing if incorrect retinal units If too high w <= w - cx If too low w <= w + cx xi x2 x3 x4 x5 input signal - light? Results neural network/perceptron categorizes input i.e., detects instances of a category limits linearly separable J.A. Anderson (1980's) - neural network (1980' William James (1892). 10 ...
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- Fall '07
- Cognitive Science, generic neuron