4/29/2017 Nervous System, Neurons The nervous system Two major divisions The Central Nervous System (CNS) – The brain and the spinal cord The Peripheral Nervous System (PNS) – Cells outside the brain and spinal cord The brain 1 trillion cells: 1,000,000,000,000 according to some estimates About 100 billion neurons 1,000,000,000,000 cells - 100,000,000,000 neurons, i.e., 10% -------------------------------------------------900,000,000,000, i.e., 900 billion other cells. 90% are “other”. Cerebral Cortex A 2 mm folded blanket of neurons that surrounds the rest of the brain structures. Contains from 15 to 30 billion neurons, averaging about 21 billion (It is estimated there are about 70 billion neurons in the cerebellum.) 1 4/29/2017 Nervous System, Neurons 2 Synapses Junctions between neurons Places where neurons communicate with each other. If you have K neurons, there will be K*(K-1)/2 places where pairs of neurons might communicate with each other - synapses. So If you have 2 neurons, there is 2*(2-1)/2 = 1 possible synapse If you have 3 neurons, there are 3*(3-1)/2 = 3 possible synapses If you have 4 neurons, there are 4*(4-1)/2 = 6 possible synapses If you have 10 neurons, there are 45 possible synapses. If you have 100 neurons, there are 4950 possible synapses If you have 1000 neurons, there are 499, 500 possible synapses And if you have 1,000,000 neurons there are 5 x 1012 or 5,000,000,000,000, that’s 5 trillion possible synapses. If you have 100 billion neurons . . . 100,000,000,000 x 100,000,000,000 /2 = 10,000,000,000,000,000,000,000/2 = 5,000,000,000,000,000,000,000 (100*109)2 = 10000 * 1018 = 1 * 1022 = 10*1021 = 5*1021) As we will see, synapses are where processing occurs in the brain. 4/29/2017 Nervous System, Neurons 3 History of discovery of brain anatomy and function 1800 1812 –War of 1812 1830 –Discovery that bodies of living creatures are composed of cells 1830 –Reticular theory: Gerlach and Golgi proposed that brain was composed of processes of cells fused together. 1860s –United States Civil War 1873 –Golgi developed a method of staining the cells of the neuron that showed the separate cells 1890 –Cajal (“kähä): Proposed the neuron doctrine. Brain composed of separate cells. 1900 1906 –Golgi and Cajal shared Nobel prize for discoveries of brain anatomy. Golgi “clung bitterly to reticular theory” 1915 – World War I 1936 –Dale and Loewi share Nobel Prize for work on chemical transmission between nerves 1939–Atanasoff-Berry Computer – World’s first electronic digital computer developed. 1943 – World War II 1953–Kuffler publishes work on center-surround on-off organization of retinal ganglion cell receptive fields 1960–DeValois discovered lateral geniculate cells that respond in an opponent process fashion 1961–Von Bekesy awarded Nobel Prize for work on function of the cochlea 1981–Hubel and Wiesel shart Nobel Prize for discoveries of functions of the visual system 1983 Dartnall, Bowmaker, & Mollon validate existence of three cone receptors in humans 2000 Personal computers able to perform 1 billion floating-point operations per second 2010 –Human genome sequenced 2015 –Computer surpasses computing power of mouse brain 2023 –Computer surpasses computing power of a human brain 2045 –The Singularity: Computer surpasses computing power of all human brains combined 2100 4/29/2017 The Neuron Various shapes of neurons Nervous System, Neurons 4 4/29/2017 Nervous System, Neurons 5 3 major parts of most neurons 1. Cell body or soma: Contains nucleus that contains DNA 2. Dendrites – A collection of projections from the cell body. “Receiving areas” 3. Axon – A single projection from the cell body: “Sending area” An axon may be a millimeter long. Axon may be up to 3 or more feet long in humans. Axons from retina to LGN are a few inches long. Neuron is filled with fluid and resides in fluid. from VL 2.1 Structure of Neuron There are many many many many entities within neurons and many many many processes going on among those entities. The study of those entities and processes within a single neuron could occupy the rest of your life. 4/29/2017 Nervous System, Neurons 6 The neuron at rest (G8, p. 28-29) The neuron is a fluid filled entity which rests in the cerebro-spinal fluid. Both the interior and exterior fluids contain charged ions – an atom or molecule with an extra electron or an electron absent. Primary among these are Chloride ions (Cl-) primarily on the inside, Protein ions (Protein-) primarily on the inside Potassium ions (K+) primarily on the inside Sodium ions (Na+) primarily on the outside (This is a simplification of the complex of ions inside and outside the neuron.) Na+ ClNa+ Cl- K+ Cl- Na+ Cl- Na+ Na+ Na+ K+ ClCl- Cl- Cl- K+ ClClProtein- Na+ Cl- Na+ Na+ Protein- Let us in. Let us in. Let us in. Na+ Cl- K+ K+ Cl- ProteinClClProtein- ProteinNa+ At rest, because there are more negatively charged ions than positively charged ones inside the neuron, the interior of the neuron is slightly negatively charged with respect to the exterior (about -.070 volts). Because the interior of the neuron is negative, the positively charged sodium (Na+) ions want in. Play VL 2.3 “Resting Potential” here. 4/29/2017 Nervous System, Neurons 7 Language of the Neuron: The action potential The action potential occurs as a result of a brief change in the makeup of the neuron’s membrane. When that change occurs Na+ ions move into the neuron and the voltage of the interior of the neuron – going from -.070 to + .040 in a fraction of a second. When the neuron’s interior voltage reaches +.040, another change occurs that causes K+ ions to rush out of the neuron, resulting in the interior voltage going from +.040 back toward 0. This whole process – change in the membrane, movement of ions, and change in voltage - is called an action potential. Think of the rush of Na+ ions into the neuron as analagous to a department store on the morning of Black Friday. Before opening, the shoppers (Na+ ions) are outside the store. Think of the negatively charged Cl- ions on the interior as the things those shoppers want. When the door opens, the shoppers rush into the store. Then think of the rush of K+ ions out as the exit of clerks and other store personnel from the store. A plot of voltage inside the neuron relative to voltage outside is as follows . . . (G8 p. 29) 0 After the action potential, a process occurs that pumps the Na+ ions out of the neuron. It’s called the sodium pump. Show VL 2.4 “Phases of the Action Potential” here. 4/29/2017 Nervous System, Neurons 8 Details of the Action Potential (G8 p. 30) Propogation The action potential occurs first in the part of the neuron where the axon meets the cell body, called the axon hillock From the axon hillock, it travels down the axon toward the end. The shoppers buy everything they need in the departments near the door and then move on to other parts of the store. Speed Speed in fibers without a myelin sheath: About 10 meters per second. (About as fast as the fastest person in the world can run the 100 meters.) This is too slow for complex processing. The myelin sheath consists of cells of non-neurons that wrap about the axon. There are breaks in this sheath called nodes of Ranvier (pronounced Ronviay). The action potential jumps from node to node, speeding up the transmission to 120 meters/second. (12 times faster.) This is called saltatory conduction. When the action potential reaches the end of the axon, it releases neurotransmitter substance. 4/29/2017 Nervous System, Neurons 9 Rate of firing. This process can occur no more than about 800 times per second. Size of action potential within the neuron. The action potential remains the same size from the axon hillock to the end of the axon. Changes in the action potential in response to changing stimulation The action potential does not change size as the stimulation changes. The only effect of changing stimulation on the action potential is a change in rate of occurrence. Play VL 2.5 “Nerve Impulse Coding and Stimulus Strength” here. Spontaneous brain activity The neurons of the brain are constantly active. Video of zebrafish individual cell activity http://scopeblog.stanford.edu/2013/03/19/cellular-level-video-of-brain-activity-in-a-zebrafish/ Stylized video of left hemisphere brain activity http://www.youtube.com/watch?v=VaQ66lDZ-08 Videos of images reconstructed from brain activity OK, this doesn’t really belong here. It belongs in Chapter 5. Remind me to show it then. http://gizmodo.com/5843117/scientists-reconstruct-video-clips-from-brain-activity 4/29/2017 Nervous System, Neurons 10 The importance of speed of processing The world’s fastest supercomputer can run at a sustained 2.5 petaflops (a petaflop is a thousand trillion floating point operations per second). We would be smarter and more able if 1) our neurons could fire at rates > 1000/sec and if our action potentials could travel faster than 120 meters/second. We’d also eat a lot more, since neural activity consumes energy. How smart are computers relative to us? Is your brain capable of doing more computations than the world’s fastest computer? YES, today. But in a few years, probably not. 4/29/2017 Nervous System, Neurons 11 Neurons talking to each other: Synapses (G8 p. 31) Play VL 2.6 “Synaptic Transmission” here. The junction between adjacent neurons is called the synapse. Synapse Most neurons are separated by a few 100 billionths of a meter. When an action potential reaches the end of the axon, it causes the release of chemicals into the synaptic cleft. These chemicals mix with the extracellular fluid in the synapse. The neurotransmitters are released from structures called vesicles at the end of the axon. Over 40 types of neurotransmitter have been identified. Neurologists suspect there are no more than 100-200 in all. The neurotransmitter may attach to the dendrite a neuron near the end of the axon. If so, it has the potential to affect the probability of the following neuron emitting an action potential. 4/29/2017 Nervous System, Neurons 12 Excitation and Inhibition Each neuron releases a specific neurotransmitter (think of the neurotransmitter as the neuron’s language – English, Dutch, Spanish, etc.). Some neurotransmitters cause excitation – increasing the likelihood of action potentials of neurons whose dendrites are nearby. Primary among these is glutamate. Release of others causes inhibition - resulting in decrease in likelihood of action potentials of neurons whose dendrites are nearby. Primary among these is the amino acid GABA (gammaaminobutyric acid). A given neurotransmitter may have one function in one part of brain and a completely different function in another part. A single synapse may be the connection point for 1000s of neurons, like an airport, with 1000s of incoming passengers. So there may a very complex mix of “languages” at any synapse – some excitatory and some inhibitory. 4/29/2017 Nervous System, Neurons 13 The mix of Neurotransmitters over time. A given neuron may be affected by neurotransmitters released by perhaps 1000s of other neurons. And a given neuron’s neurotransmitter may affect 1000s of other neurons. The result is that the extracellular fluid near the dendrites of a neuron will contain a changing mix of excitatory and inhibitory neurotransmitters. If the concentration of excitatory neurotransmitters is great enough, a sequence of changes in the chemical makeup of the receiving neuron will occur. The culmination of these changes may result in an action potential. Think of the proportion of Volkswagens at an intersection. When it reaches a certain level, everyone punches their friend on the shoulder. (This is a reference to an old VW commercial.) Graph of nature of neurotransmitter in a synaptic junction. Excitatory Threshold Excitatory of sufficient duration to cause an action potential. Neither Inhibitory Time in 1000s of a second Concepts Associated with synapses Base Rate – the rate of activity of a neuron as a result of random accumulations of excitatory or inhibitory neurotransmitter substances near its dendrites. Spatial Summation – the accumulation of a type of neurotransmitter because of the simultaneous action potentials of several neurons. Temporal Summation – the accumulation of a type of neurotransmitter because of the rapid action potentials of a single neuron. Comparison – the change in response rate of a neuron based on the release of excitatory and inhibitory neurotransmitters by other neurons near its dendrites. Sometimes the neuron will be very active. Other times the neuron will be very inactive depending on which neurons – excitatory or inhibitory – are most active. Play VL 2.7 “Excitation and Inhibition” here. Note the scales for “Excitation” and “Inhibition” are backwards. Moreover, the mechanics when Inhibition = 1 or 2 is wrong. 4/29/2017 Nervous System, Neurons 14 Neural Processing: A basic comparison circuit – Notation A ----> Excitation B ------| Inhibition Suppose A and B are “visual” neurons that respond to the amount of light striking them. (Obviously, the real circuitry is much more complicated than this. We’re simplifying for ease of presentation.) In the circuit above and below, A responds to the amount of light in the left side of the visual field. B responds to the amount of light in the right side of the visual field. Suppose neuron C receives input from A and B. Suppose further that if A “sees” light, it releases an inhibitory neurotransmitter, but if B “sees” light, it releases an excitatory neurotransmitter. This leads to the following scenarios . . . 1. Both A and B “see” blackness. A doesn’t release inhibitory neurotransmitter. B doesn’t release excitatory neurotransmitter. B A C Base rate C is neither excited nor inhibited. C responds at its base rate. 4/29/2017 Nervous System, Neurons 15 2. A and B both “see” light. A releases inhibitory neurotransmitter. B releases excitatory neurotransmitter. B A But the Inhibition from A cancels out the excitation from B, leaving C neither excited nor inhibited. C C responds at its base rate. Base rate 3. A “sees” blackness while B “sees” light. A A “sees” light While B “sees” blackness. A B B Whoopee! C A does not release inhibitory neurotransmitter. B releases excitatory neurotransmitter. C is excited and responds. Bummer. C A releases inhibitory Neurotransmitter. B release no excitatory Neurotransmitter C is depressed. This simple circuit can be the building block for much more complex processing. It allows the same neural structure – Neuron C in the example – to respond in one way to one type of stimulation and to respond in a different way to a different type of stimulation. This is all that is needed for perception. 4/29/2017 Nervous System, Neurons 16 Goldstein’s Neural circuit example, p. 33 (pdf’d) After explaining this, play VL 2.8 “Simple Neural Circuits”. The basic circuit, figure 2.14 No matter how many receptors are stimulated, the firing rate of B remains the same. 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Firing rate of B B 1 2 3 4 5 6 7 4 3-5 2-6 1-7 Receptors stimulated 4 3-5 2-6 1-7 Receptors stimulated 4 3-5 2-6 1-7 Receptors stimulated 4 3-5 2-6 1-7 Receptors stimulated Firing rate of B B 1 2 3 4 5 6 7 Firing rate of B B 1 2 3 4 B 5 6 7 Firing rate of B 4/29/2017 Nervous System, Neurons 17 G8 p 33 Firing rate of B From Goldstein’s figure. I added the arrow heads to make them correspond to my way of representing synapses. 4 3-5 2-6 1-7 Receptors stimulated 4 3-5 2-6 1-7 Receptors stimulated 4 3-5 2-6 1-7 Receptors stimulated Firing rate of B Firing rate of B This illustrates spatial summation – the greater the number of adjacent receptors that are stimulated, the greater the activity of “higher level” neurons receiving input from them. Firing rate of B 4 3-5 2-6 1-7 Receptors stimulated 4/29/2017 Nervous System, Neurons 18 Note the inhibitory synapses from neuron A and neuron C. (G8 p 33) Firing rate of B 4 3-5 2-6 1-7 Receptors stimulated 4 3-5 2-6 1-7 Receptors stimulated 4 3-5 2-6 1-7 Receptors stimulated 4 3-5 2-6 1-7 Receptors stimulated Inhibition Firing rate of B Firing rate of B Mike – use the short bar on the right to introduce the concept of center-surround antagonism. Firing rate of B 4/29/2017 Nervous System, Neurons 19 Receptive Fields – the collection of receptors connected to a given neuron. (G8, p 34) Play VL 2.9 here – it lasts about 10 minutes. This is most relevant to the structure of receptors in the eye. 1. Neurons have been found to be connected to a field of receptors – the cell’s receptive field. The receptive fields are usually circular. Only stimulation of those receptors affects that particular cell. Receptors that are connected to a neuron G Neuron : G for ganglion cell since they’re most relevant vision Each receptive field is located at a different place, responding to stimulation at a different part of the visual field. 2. Most receptive fields are characterized as center surround. This means that there is a center, circular area, with a donut-like surround. Stimulation of the center yields one type of response (either an increase in activity or a decrease). Stimulation of the surround yields the opposite type of response. Center Surround Antagonism between Center and Surround (G8 p. 35) Stimulation of the surround results in a response opposite that of application of the same stimulus to the center. So if a light applied to the receptors in the center of a receptive field cause excitation, shining that same light in the surround will cause inhibition. This type of receptive field is called an antagonistic center surround field. On-center Off-surround + - - Off-center On-surround + 4/29/2017 Nervous System, Neurons 20 Effects of various types of stimuli on a On-center/Off-surround receptive field . . . Spot of light in center – positive response response + Spot of light in surround – diminished - - Spot of darkness in center – diminished response response + Spot of darkness in surround – positive + - Diffuse light – no response reason Because center and surround cancel each other + - Diffuse darkness – no response for same + - - Edge of light – a response almost anywhere it is, in any orientation. Hmm. + + - + - + - + - This suggests that an antagonistic circular surround receptive field is an ideal shape for detecting light-dark borders of any orientation – a wonderful building block for perception. - 4/29/2017 Nervous System, Neurons The other cells in the central nervous system They’re collectively called neuroglia (hard g) or glial or “glue” cells. Outnumber neurons by 9+ to 1 – 900 billion to 100 billion. Some types a. Those that provide structural support b. Those that repair following injury c. Those involved in metabolic functions involved in neural activity d. Those that form myelin sheath around axons of CNS neurons. MS is a degeneration of the myelin sheath. e. Those that move to injury sites and remove debris 21 4/29/2017 Nervous System, Neurons 22 The stimulus code (G8 p. 36-37) 1. Specificity Coding Every individual “thing” in the environment is assigned to a different neuron. The activity of that neuron represents that environmental “thing” – person, object, sound, touch, temperature, smell, taste After all, we have 100 BILLION neurons. That should be plenty to represent all the “things” we’ll experience in our lives. Problems . . . 1. Suppose my dog, Hannah, is represented by a single neuron. Hannah is a wonderful dog, who sometimes barks, oftentimes sleeps, sometimes begs for food, sometimes won’t eat food because she knows that it contains a pill, sometimes barks and sometimes wags her tail, and does all the other things that a dog will do. But a single neuron can only do one thing – change its rate of emitting action potentials – from slow to fast. Its responses are ONE-DIMENSIONAL, but Hannah responds on many many dimensions – alterness vs sleepy, hungry vs satiated, friendly vs aggressive, playful vs resting, etc, etc, etc. A one-dimensional neuron cannot possibly represent Hannah. 2. Even the picture of Hannah changes every second. Even if the neuron represented just one view of Hannah, what about the other views of her = standing, sitting, sleeping, etc. How can the responses of one neuron represent the 1000s of different views of her? So no one believes that a single neuron can all the aspects of the experience associated with an external object. 2. Distributed coding. Each “thing” in the environment is assigned a pattern of activity of a collection of neurons. This clearly seems correct, although there may be individual neurons that represent the presence/absence of specific characteristics of the external world, such as the extent to which an object looks like a face, for example. Problem – Requires a lot of energy since many neurons have to be active whenever each external “thing” is present.