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Human Vision: Electrophysiology and Psychophysics Why study biological systems? Pattern recognition A A A A A T A A A A Artificial neural networks are used to solve problems in fields as diverse as • pattern recognition • optimisation • locomotion and spatial recognition • sequence prediction In contrast to previous efforts in artificial intelligence, there is focus not only on the algorithm necessary for solving a problem, but also the underlying brain architecture and physiology that solves this problem. The brain is made up of neurons At one time there was a debate as to the question of whether the brain was composed of a continuous system of wires or whether it was a discontinuous network made up of individual neurons Soma – contains the nucleus Dendrite – receives input Axone – sends output The neuron is a superchip The neuron is the basic building block for all the activities in the brain – these activities are as diverse as • sensory function – vision, audition, touch, smell • motor functions – muscle contraction • perception eg • cognition For these functions, one neuron can receive upto 100000 contacts and send signals to hundreds of other neurons. Many levels of complexity in the models The simplest The McCullogh Pitts neuron The most complex model I know of The multicompartment model of the cerebellar neuron. The McCullogh Pitts neuron 1 or 0 1 or 0 a 1 or 0 a = threshold Output function - Heavyside step function The McCullogh Pitts neuron X1 X2 AND, a=2 Input X1 0 0 1 1 Input X2 0 1 0 1 Output 0 0 0 1 The McCullogh Pitts neuron X1 X2 OR, a=1 Input X1 0 0 1 1 Input X2 0 1 0 1 Output 0 1 1 1 Shelton DP (1985) Neuroscience 14: 111-31 A model of the cerebellar Purkinge neuron with 1000 compartments Uptil now we have discussed the conditions of a neuron at rest. But the neuron is in a network and the information processing capacities of the neuron lie in its capacity to communicate with the other neurons. Uptil now we have discussed mainly passive conductances ie The movements of current which do not require the expenditure of energy. The leak current primarily consisting of K+ dominates this state. Neuronal communication is via the action potential. This state is active and is dominated by the Na+ ions. Two sorts of synapses in the body Synapses between neurons Synapse from neuron to muscle The elements of the network communicate with each other. This is done by the transmission of chemicals called neurotransmitters. Presynaptic neuron Neurotransmitter (6) Neuroreceptor (3,4) Postsynaptic neuron A neuron sends a message via an action potential The action potential leads to the liberation of neurotransmitter molecules The action potential Action potential: Na channels are open and neurotransmitter is released in synapse Threshold: energy expenditure to open Na channels Na channels close Response to the presynaptic action potential The depolarization at the presynaptic terminal leads to the liberation of neurotransmitters presynapatic postsynaptic The neurotransmitter acts like a key. It opens the neuroreceptor channel and permits the passage of ions across the synaptic membrane. 2 types of postsynaptic effects a) Excitatory b) Inhibitory Effects of an excitatory neurotransmitter Presynaptic excitatory neuron PRESYNAPTIQUE Postsynaptic response is positive. The postsynaptic neuron is depolarized and has an increased probability of firing an action potential POSTSYNAPTIQUE Excitatory neurotransmitters usually work by opening postsynaptic Na+ or Ca2+ channels. Effects of an inhibitory neurotransmitter Inhibitory presynaptic neuron Postsynaptic response is negative. The neuron becomes more polarized. There is a decreased probability for the postsynaptic neuron to fire. PRESYNAPTIQUE POSTSYNAPTIQUE Inhibitory neurotransmitters work by opening K+ or Cl- channels Spatial and temporal summation Each neuron has thousands of synapses. The signals come in at different times and at different locations over the surface of the neuron The integration of signals at different locations is called spatial summation. The integration at different times is called temporal summation