Rate coding After mastering this material, you should be able to: Explain the concept of neuronal encoding Explain the concept of rate coding, and contrast it with temporal coding List several examples of rate coding

Neuronal encoding: how a population of neurons represents incoming sensory information Incoming stimuli Neuronal encoding Further neuronal processing Behavioral output

Rate coding: the simplest form of neuronal encoding, in which neuronal firing rates increase as stimulus intensity increases --see “In Search of Memory” (Kandel), p. 77 Discovered by Edgar Adrian (Nobel Prize, 1932)

Stretch receptors --Edgar Adrian recorded from the stretch receptors of frog leg muscles; he would suspend the leg muscles and hang weights from them --the heavier the weight, the more the muscle was stretched, and the higher the firing rate of the nerves encoded muscle stretching Fundamentals of Comp. Neuro., Trappenberg, p. 71

Rate coding in the kneejerk circuit Afferent neuron: carries signal toward the CNS Efferent neuron: carries signal away from the CNS --read here about muscle spindles: http://michaeldmann.net/mann4b.html --the sensory neurons which wrap around the muscle fibers have very special ion channels that are connected by cytoskeletal strands made of a protein called spectrin; when the muscle is stretched, so are these cytoskeletal strands, which causes the ion channels to open --these ion channels allow sodium through, which depolarizes the cell and leads to action potentials Mechanical energy (stretching of muscle) is transduced to electrical energy (action potentials)

Applet demonstrating rate coding in stretch receptor http://www.angeltear.com/spindle/spindle.html

Other examples of rate coding Force of muscle contraction Sense of temperature Warm thermoreceptors Cold thermoreceptors Temp Firing rate Temp Firing rate

Other examples of rate coding Sense of tactile pressure -Adaptation Frequency -- Principles of Neural Science, 5th Ed., p. 453

Different sensations are a result of anatomical distinctions Adrian: “All impulses are very much alike, whether the message is destined to arouse the sensation of light, of touch, or of pain; if they are crowded together, the sensation is intense; if they are separated by any interval, the sensation is correspondingly feeble.” --it is anatomy that determines the difference between different modes of sensation, not the manner in which the signals are physically transmitted (all neurons transmit messages using AP’s, but different circuits produce different sensations)

Information transfer via rate coding

Information transfer via rate coding

Information transfer via synchronization (temporal coding) There is enough time in between presynaptic spikes for postsynaptic membrane voltage to decay near to resting state, preventing presynaptic neuron from spiking

Information transfer via synchronization (temporal coding) But if presynaptic spikes are close enough in time, then there is not very much decay of postsynaptic neuron’s membrane potential in between spikes, resulting in a postsynaptic spike

Suppose you and your roommate disagree on what temperature set your room to. Your roommate likes to set the room to 18o C, which you find much too cold, and you are constantly having to adjust the thermostat. One day, you arrive to a freezing room to find that your roommate has "accidentally" destroyed the thermostat, though they claim that it was set to 21o C before the thermostat was broken. You want to scientifically disprove your roommate's claim, but you don't have a thermometer. Fortunately, in your neuroscience class earlier that day you recorded from a thermoreceptive neuron in your arm and collected the following response curve data: Lucky for you, you happen to have the neuronal recording equipment in your backpack! You realize that if you record the firing rate of the thermoreceptive neuron, you can use the data to determine the room's temperature. Start by plotting the data in MATLAB. In the plot window, plot the firing rate data as the y-values and the temperature data as the x-values (since we typically think of temperature being the cause and firing rate being the effect). Plot the data as circles unconnected by lines. Go to Tools -> Basic Fitting, and fit a linear curve to the data. This will give you a mathematical model for the functional relationship between firing rate and temperature. Next, use this equation to implement a function find_temp in Matlab so that when you input the neuron's firing rate, the function outputs the room temperature. Include if statements so that if the firing rate is less than 8.9 Hz or greater than 68.2 Hz, a warning is issued saying that the firing rate is outside the bounds of the mathematical model.   When you measure the neuron's firing rate, you find it to be 16.5 Hz. What temperature does this correspond to, according to your mathematical model? Good luck convincing your roommate of the accuracy of your unorthodox thermometer!