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0:05
00:00 Today we're going to begin a voyage of 00:08 discovery of this most extraordinary 00:12 three pounds of jelly-like material 00:15 found within each of our skulls. A 00:19 neuroscience, as many of you know, is a 00:20 discipline that employs the tools in 00:23 language of anatomy, biochemistry, 00:26 physiology, pharmacology, molecular biology, 00:30 genetics, neurology and 00:33 psychiatry to understand the normal and 00:36 pathological functioning of the nervous 00:39 system.
0:40
It represents a rather unique 00:42 position in the curricula of the 00:45 biomedical sciences, in that, it addresses 00:50 an organ of obvious importance, the brain. 00:53 What organ could be more important? 00:55 But neuroscience also addresses more 00:57 philosophical questions, like the 00:59 physiological basis of mind or emotions 01:01 and our interactions with the world in 01:04 which we live. It's been said that 01:07 neuroscience is one of the last
1:10
01:09 disciplines of the biological sciences, 01:11 so when we know everything there is to 01:14 know about the heart and the lungs and 01:15 the kidneys, we'll still just be scratching the 01:18 surface in understanding the brain. Now, 01:21 this is not, hopefully, because people 01:24 doing brain research don't have any, but 01:27 rather it's because the brain is 01:30 extraordinarily complex. For one, it 01:33 contains more than 100 billion neurons,
1:36
01:36 so that's a lot of neurons, but it's just 01:39 not the number of neurons. Other organs, 01:41 like a liver, have billions of liver 01:44 cells. What makes the difference between 01:47 the brain and the liver are two things. 01:50 One is that, unlike the liver where most 01:54 of the cells are doing the same thing, 01:55 neurons in the brain all have specific 01:59 jobs or functions, so neurons and the 02:02 occipital cortex are involved in the
2:04
02:04 processing of visual information, neurons 02:06 in the temporal lobe involved in memory 02:09 function, neurons in the frontal lobe are 02:11 involved in planning neurons, in the 02:14 cerebellum involved in balance, and Loco 02:18 neurons in the brainstem involved in the 02:20 control of respiration. So what's almost 02:23 as though the brain is not a single 02:25 organ; it's hundreds of different organs 02:27 each carrying out their own specific 02:29 function.
2:31
02:32 The other major difference 02:33 between the liver and the brain is that 02:36 unlike the liver where most of the cells 02:38 are doing their own thing and working in 02:40 isolation, in the brain, neurons are 02:44 highly connected to each other. So it's 02:46 been estimated that any one neuron can 02:49 receive up to 10,000 connections or 02:52 messages from other neurons. And in turn 02:55 can make 10,000 connections to other 02:58 neurons.
2:59
So there's a whole lot of 03:00 talking going on. We used to think of the 03:03 brain like a computer, but it's more like 03:05 a computer network, with each one of 03:07 those neurons being one of the 03:08 individual computers. 03:10 So it's those 03:11 circuits they're formed by the 03:13 connections among neurons that give rise 03:15 to love, to charity, to Beethoven, to 03:20 Shakespeare, to travel to the moon and 03:23 beyond, to theories of relativity.
3:27
But 03:27 those circuits also give rise to hate 03:30 and the ability to commit mass murder. 03:32 And also, because there are so many 03:37 components of the brain, there's so many 03:39 things that can go wrong. 03:40 Let me just 03:41 introduce you to some of the things that 03:43 can go wrong. And one of the things that 03:45 you will be doing, as future biomedical 03:47 scientists and as clinicians, is to know,
3:51
03:51 know about the things that can go wrong 03:53 and know about how to fix them. 03:55 So here's just a brief summary of some 03:58 of the major neurological disorders that 04:01 we're going to talk about throughout the 04:04 course: 04:06 Alzheimer's Disease - that's a big 04:07 one that's become very prominent in the 04:09 news because it's associated with loss 04:12 of cognitive function and memory due to 04:14 neuronal degeneration. So this is a 04:16 so-called degenerative disorder (a
4:18
04:18 degenerative and neurological disorder) 04:20 and, in particular, cholinergic neurons in 04:23 the Central Nervous System. This 04:24 accounts for prevalence right now of 04:27 5.4 million individuals and the number 04:30 is growing on a yearly 04:31 basis as the population ages. 04:33 And this 04:34 Epilepsy – brain seizures due to 04:36 uncontrolled recruitment of electrical 04:38 activity and nerve cells, 3 million 04:40 people.
4:41
04:41 Huntington's Disease – another 04:42 neuro degenerative disease 04:44 associated with abnormal and voluntary 04:47 movements due to the repeated mutation 04:50 in the Huntington gene. 04:52 Multiple Sclerosis – an autoimmune disease, as many 04:55 of these, associated with sensory and 04:57 motor losses due to demyelination. 05:00 Myasthenia Gravis – another autoimmune 05:03 disease associated with muscular
5:05
05:04 weakness due to loss of acetylcholine 05:06 receptors at the Neuromuscular Junction. 05:09 Parkinson's disease – a movement disorder 05:12 due to degeneration of dopamine 05:15 containing neurons, another neuro 05:16 degenerative disorder, in the substantia 05:19 nigra basal ganglia pathway, a brain circuit. 05:22 Schizophrenia – delusions and 05:25 hallucinations believed to be due to 05:27 imbalances in dopamine and glutamate 05:29 neurotransmitter systems.
5:31
05:32 Stroke – loss of 05:33 specific function, due to occlusion of 05:35 blood supply through specific brain 05:37 regions, 4 million people. 05:39 What's not 05:40 indicated here and when you total these up, 05:42 you have about 15 million people. 05:43 What's not illustrated here is depression and 05:47 anxiety, and you can another 15 million 05:50 to that. 05:50 So when you add it all up you 05:51 have 10% of the population in United
5:53
05:53 States right now being afflicted with 05:55 some brain disorder. 05:58 Now despite the 05:59 great diversity of these disorders, there 06:02 are a couple of general themes that 06:03 emerge that are particularly important 06:05 to pay attention to. One is, there's the 06:08 involvement of genes, like the mutation 06:10 in the Huntington gene. There are 06:12 disorders that affect neuronal 06:15 properties like myelin and synapses.
6:18
06:18 There are disorders that affect 06:20 neurotransmitter systems like dopamine 06:22 and acetylcholine and their receptors. 06:24 And there are disorders that affect 06:27 neural circuits. 06:28 So the key here is that 06:30 in order to understand the brain, how it 06:33 functions, and how it malfunctions, you 06:37 need to pay attention to the properties 06:40 of individual neurons: the electrical, 06:42 the biochemical, and molecular properties
6:45
06:45 of individual neurons, as well as, the ways 06:47 in which neurons are connected to each 06:49 other to form neural circuits. And that's 06:52 really good. Its gonna be the focus of this 06:54 course, and the focus of this first 06:57 lecture where I'm going to give you an 06:58 introduction to the basic properties of 07:01 nerve cells and the basic properties of 07:03 neural circuits. 07:04 A good place to start is 07:06 with the anatomy of a nerve cell, I've
7:08
07:08 shown in this illustration. So neurons 07:12 are special in that they're polarized. 07:13 Unlike a red blood cell where you can't 07:16 tell the top from the bottom, neurons 07:18 have a distinct polarity, and they can be 07:21 divided roughly into four domains, 07:24 spatial domains if you will. There is the 07:27 cell body or soma, and that's the region 07:29 of the neuron where most of the general 07:35 cell functions take place. Right? Then 07:38 extending from the soma are these tree
7:42
07:42 or branch like structures called the 07:43 dendrites. It's on the dendrites where 07:46 neurons receive connections, as I'll show 07:48 you in a moment, 07:49 from other neurons. The cell body gives 07:52 rise to a long tubular structure known 07:55 as the axon. It's over the axon where 07:58 information, electrical activity, is 08:00 propagated from one region of a nerve 08:02 cell to more distant regions. That axon 08:06 is, in many cases, covered by thick
8:09
08:09 insulating sheath known as myelin. 08:12 You've heard about Multiple Sclerosis 08:14 that affects this disease that affects 08:17 this myelin structure. The axon gives 08:20 rise to small branches and at the 08:22 terminal region of those branches are 08:24 these specialized structures called 08:26 synapses. It's at the synapse where 08:28 information transfer occurs between one 08:31 neuron and another neuron. 08:33 And the next 08:34 slide illustrates one of those so-called
8:36
08:37 postsynaptic neurons. So now you see two 08:39 neurons. This is the beginning of the 08:41 formation of a simple neural second 08:43 circuit. This is the beginning of the 08:45 formation of a simple neural circuit. You 08:47 have a presynaptic neuron and a 08:49 postsynaptic neuron. Right? 08:52 And this postsynaptic neuron gets 08:55 activated as a result of the release of 08:57 a transmitter substance from the 08:59 presynaptic neuron.
9:01
Now just as this is a 09:02 postsynaptic neuron to this presynaptic 09:04 neuron this presynaptic neuron is a 09:06 postsynaptic neuron to other neurons. So 09:09 here you see three different neurons 09:11 (nicely colored red, blue, and green) that 09:15 are making synaptic contacts on the 09:17 dendrites of this neuron-that we began 09:20 talking about. So here is a simple tri, 09:24 a three neuron circuit: a presynaptic
9:27
09:27 neuron, a postsynaptic neuron, and another 09:29 postsynaptic neuron. 09:30 Now the key in understanding how these 09:34 circuits work, is through this 09:35 specialized structure called the synapse, 09:37 and here is a blow-up view of the 09:39 synapse showing some of the key 09:41 morphological properties of synapses. 09:45 So, synapses are full with these specialized 09:50 structures called synaptic vesicles. It's 09:53 within the vesicles where the
9:54
09:54 neurotransmitter substance is located. 09:56 That neurotransmitter substance is 09:59 packaged in the synaptic vesicle, and 10:00 when an electrical signal invades the 10:03 terminal, it causes the release of those 10:05 synaptic vesicles, the content, into this 10:08 space between the presynaptic and the 10:10 postsynaptic neuron. So here are the 10:12 little molecules of neurotransmitter 10:15 substance. They diffuse across the space 10:18 between the presynaptic and the
10:19
10:19 postsynaptic neuron, and they bind to 10:22 receptors on the postsynaptic side of 10:24 the membrane where they induce 10:26 permeability changes that in turn leads 10:28 to a new round of potential changes in 10:31 the postsynaptic neuron. Okay so that's 10:36 the basic morphology of neurons and 10:39 synapses. Now an important part about the 10:42 way in which the nervous system works is 10:44 through electrical signaling, and here is 10:46 a cartoon that illustrates how one can
10:49
10:48 record the electrical signals that are 10:53 present in nerve cells. So here is an 10:55 idealized version of a nerve cell and 10:57 outside so you see the cell body and the 10:59 dendrites and the axon. Outside the 11:02 neuron is a device called a microelectrode 11:05 this is nothing more than a 11:06 piece of glass which is stretched under 11:08 heat to produce a very fine tip; the tip 11:11 can be less than a micron or so in 11:12 diameter.
11:13
The electrode is filled with a 11:14 conducting solution and connected to 11:16 some device that can monitor the 11:18 electrical potential, like an 11:20 oscilloscope. 11:21 Now when the electrode is in the outside, 11:23 or the extracellular medium, there's no 11:25 potential recorded on the recording 11:27 device because the extracellular medium 11:29 is isopotential. But if one takes this 11:32 electrode and carefully inserts the tip 11:35 of the electrode so that the tip is
11:37
now 11:37 inside the cell, there is a sharp 11:40 deflection on the recording device so 11:42 that now the recording device reads a 11:44 potential of about -60 millivolts, 11:48 that's 60 millivolts inside negative 11:51 with respect to the outside. This is the 11:54 so-called resting potential. It is 11:55 actually a characteristic feature of all 11:57 cells in the body. All cells in the body, 12:00 including nerve cells, have a potential
12:02
12:02 difference between the outside and the 12:04 inside of the cell, and that potential 12:06 you'll learn much more about in this 12:08 course and in Physiology is full the 12:10 resting potential. 12:12 The resting potential, 12:13 in the absence of any stimulation, is 12:15 generally stable, as long as, the 12:18 electrode remains in the cell and, as 12:21 long as, there's no stimulation of the 12:23 type we'll talk about in a moment. If you 12:25 remove the electrode from the cell,
12:28
the 12:28 potential that’s recorded goes back to 12:30 zero. So the 60 millivolt level is 12:32 the resting potential. There's nothing 12:34 particularly special about -60, it’s 12:36 always about -60. It could be 12:38 -65; it can be -55; it can be -75, 12:42 but the universal aspect of this, the 12:46 universal aspect of this is that the 12:48 inside is negative with respect to the 12:50 outside, and about 60 millivolts in 12:54 potential. Okay, so this is the resting 12:57 potential.
12:58
12:59 Now what distinguishes nerve 13:00 cells and other excitable membranes, like 13:02 muscle cells, from other cells in the 13:04 body is that they not only have they not 13:07 just have resting potentials, but they're 13:09 also capable of changing their resting 13:11 potential for the part they're capable 13:14 of changing their resting potential for 13:17 the purpose of transmitting and 13:19 propagating, and processing information. 13:22 So here is another simple cartoon.
13:25
Here 13:26 is an idealized nerve cell, and this cell 13:27 has been impaled with one micro 13:30 electrode to record the membrane 13:33 potential. It's also been impaled with a 13:36 second electrode, and this second 13:37 electrode is connected up to a suitable 13:41 stimulating source like a battery. And 13:44 note the polarity of the battery here. 13:46 The polarity of the battery is such that 13:48 the positive pole is connected to this 13:50 arrow, which is a switch.
13:52
Now when the 13:53 switch is open no current flows from the 13:55 battery into the cell, but when the 13:57 switch is closed, there will be a current 13:59 flow from the battery into the cell and 14:01 what will be the consequences of closing 14:03 the switch? 14:05 Since the pole of the battery here is 14:07 plus (+), it will make the inside of the cell 14:09 more positive. So what we're going to see 14:11 in this animation is a series of switch 14:14 closures and openings with each time a
14:17
14:17 different sized battery placed in the 14:19 circuit. Now start with a small battery, 14:21 and then we're going to go to a large or 14:23 in larger battery, and look at the 14:24 consequences of giving these greater and 14:27 greater stimuli to the nerve cell. By the 14:32 way all these animations that I'm 14:34 showing you are in your online 14:35 electronic syllabus. Okay, so there is a 14:40 small battery, a bigger battery, a bigger
14:44
14:44 battery (note that battery gets bigger 14:46 each time) larger and larger battery and 14:48 even a larger battery. And what's 14:51 interesting here is you see as you 14:53 change the size of the battery, we change 14:55 the size of the current, and we make the 14:57 inside of the cell 14:58 more and more positive, and that's called 15:01 a depolarization. Depolarization, it's 15:04 more positive, and within a limit, the
15:08
15:08 size of those depolarizations is roughly 15:11 proportional to the size of the battery, 15:12 until we reach a certain potential, 15:15 called a threshold, when a totally 15:17 different type of signal is produced. and 15:20 this signal is the so called nerve 15:22 action potential or impulse. So from this 15:26 you see a very important property of the 15:29 action potential. You see that it's 15:31 elicited in an all-or-none fashion,
15:33
15:33 so stimuli below threshold failed to 15:37 reach an action potential; stimuli at or, 15:40 in this case, above threshold initiate an 15:43 action potential. 15:47 Now the duration of the stimulus here 15:50 was so short that only a single action 15:53 potential could be elicited. But, if we 15:56 use a longer duration stimuli, we see 15:59 that longer duration stimuli elicit a 16:01 greater number of action potentials with
16:04
16:04 each action potential having the same 16:06 all or nothing amplitude. So here's four 16:09 panels, and what we're going to see in 16:11 each one of these panels is a switch 16:13 closure and each panel is associated 16:15 with a larger battery. So here's a very 16:18 small battery and here's a very big 16:20 battery, right? You have different size 16:21 batteries: one 8-volt battery, 16:23 9-volt battery, 12 volt battery. and so 16:25 forth.
16:25
So the larger the battery, the 16:27 larger will be the depolarization of the 16:29 nerve cell. So here's a small battery, and 16:34 you can hear the sound of the 16:35 electricity and the animation. So this is 16:37 small battery, and you see that it 16:39 depolarize the cell but threshold was 16:43 not reached. You try a larger battery, a 16:45 somewhat larger battery, now we trigger 16:50 an action potential, but just one action 16:52 potential.
16:54
(Sorry about the sound, it's 16:57 kind of corny.) Okay. 17:01 Here's a bigger battery 17:04 two action potentials, no three action potentials and 17:07 here's an even bigger battery, 17:10 [rhythmic sound of action potentials] 17:15 a whole bunch of action potentials. The 17:18 key point here is two very important 17:21 points here: 1- is that the greater the 17:24 magnitude of the stimulus the greater is 17:27 the number or frequency of action 17:29 potentials.
17:29
17:29 That's called frequency coding on the 17:32 nervous system, and it's a very general 17:34 phenomenon. The greater the intensity of 17:36 a stimulus to the skin, the greater the 17:38 number of action potentials that are 17:40 propagated to the Central Nervous System, 17:42 telling you about the intensity of a 17:44 touch. The greater the stretch of a 17:46 muscle, the greater the number of action 17:47 potentials that are sent to the spinal 17:49 cord.
17:50
The greater the intensity of 17:51 illumination to the eye, the greater the 17:54 number of action potentials that will be 17:55 set to the Central Nervous System. And 17:57 the same thing works (the same principle 18:00 works) on the motor side of the system. 18:01 The greater the number of action 18:03 potentials in a motor neuron, the greater 18:05 will be the strength of a muscular 18:07 contraction. So this is called the 18:09 principle of frequency coding in the 18:11 nervous system.
18:13
You'll note when the 18:13 stimulus intensity is larger, it's not 18:16 that the size and the action potential 18:17 changes. Each action potential has the 18:19 identical size. It's rather that the 18:22 number or frequency of action potentials 18:23 changes. 18:25 Okay, now with that very brief 18:27 introduction into the anatomy and 18:29 physiology of nerve cells, we can start 18:31 to talk about how nerve cells are 18:34 connected to each other.
18:37
And there's two 18:36 basic flavors of the ways in which 18:39 neurons talk to each other. There's a so 18:41 called excitatory connection and there's 18:44 a so called inhibitory connection. 18:47 So let's talk about the excitatory 18:48 connection first, because it's the most 18:50 obvious. So here's a cartoon of two 18:53 neurons: a presynaptic neuron. This one 18:56 nicely color-coded in green. We're going 18:59 to call this an excitatory neuron for
19:02
19:02 reasons that would become clear in a 19:03 moment. And that neuron is making a 19:05 synaptic contact with a postsynaptic 19:08 neuron, this blue one. And we're making 19:10 intracellular recordings, as shown below 19:13 here, from both the postsynaptic neuron 19:15 (that's the blue trace here) and the (green) 19:19 excitatory neuron. Now at this point in 19:22 time, an action potential is initiated in 19:24 the excitatory neuron. We don't have to 19:26 worry for the moment how that was
19:29
19:28 initiated, perhaps 19:29 through a intracellular injection of 19:31 current or perhaps by some sensory 19:33 stimulus. But what you see here, 19:35 importantly, is the action potential in 19:38 the presynaptic neuron as and as a 19:42 consequence of that action potential, 19:44 there is a potential change in the 19:47 postsynaptic neuron. And this potential 19:49 is a depolarization, the membrane 19:52 potential has become less negative. Such 19:55 a potential is called an excitatory
19:58
19:58 postsynaptic potential: excitatory, 20:00 because it depolarizes the cell 20:03 bringing the cell towards threshold, and 20:05 postsynaptic for obvious reasons - it's a 20:09 potential record on the postsynaptic 20:10 side of the synapse. Now in most cases a 20:13 single action potential in the 20:15 presynaptic neuron, while it produces a 20:18 depolarization of the postsynaptic 20:19 neuron, that depolarization is
20:22
20:22 insufficiently large to reach threshold 20:25 and trigger an action potential in the 20:28 postsynaptic neuron. But if two of those 20:30 action potentials are fired in quick 20:32 succession, the EPSP produced by the 20:37 second action potential adds to the EPSP 20:39 produced by the first action potential, 20:42 and now that summated EPSP is capable 20:46 of reaching threshold and firing an 20:49 action potential in the postsynaptic 20:50 neuron.
20:51
20:51 So now with this simple circuit, 20:53 we have allowed one neuron to activate 20:56 another neuron, and that's a go signal, if 20:59 you will, in a very simplistic manner for 21:01 propagating information through this 21:03 circuit. 21:04 Okay let's talk about that other 21:07 flavor of synaptic transmission. This is 21:10 the inhibitory synaptic transmission. So, 21:13 the top, you see the same excitatory 21:15 neuron that we talked about earlier.
21:17
The 21:18 bottom now shows this red neuron which, 21:20 for obvious reasons we’ll soon see, is 21:24 called an inhibitory neuron. Now the 21:27 first part of this panel is identical to 21:29 what I showed you previously: an EPSP 21:31 produced by the excitatory neuron by 21:35 single action potential produces a 21:37 sub-threshold EPSP. Two together produce 21:40 this sum aiding EPSP leading 21:42 to an action potential. 21:44 So here's the 21:45 inhibitory interneuron.
21:46
Now we produce an 21:47 action potential in the inhibitory 21:49 neuron, and what do you see in the 21:51 postsynaptic cell is that the membrane 21:53 potential becomes more hyperpolarized 21:56 (more negative) which is called a hyper 21:59 polarization, as opposed to, a 22:01 depolarization. So as a result of the 22:04 chemical transmitter substance released 22:05 here, the membrane potential of the 22:07 postsynaptic cell becomes more negative.
22:11
22:11 What's the functional consequence of 22:13 that? Well when you just look at this you 22:15 say, “Well it doesn't do anything.” The 22:17 neuron wasn't firing any action 22:19 potentials to begin with, and it's still 22:21 not firing any action potentials because 22:23 of the IPSP (Inhibitory PostSynaptic 22:26 Potential). This is called an EPSP (or 22:29 Excitatory PostSynaptic Potential). 22:31 So what good are these IPSPs? Somebody 22:38 is saying, “they reduce the probability
22:41
of 22:41 initiating an action potential.” 22:43 Absolutely correct, and you see that you 22:46 see that when you challenge the system 22:47 with a combination of excitatory and 22:51 inhibitory input. So the IPSP in 22:55 hyperpolarized of the cell decreases the 22:58 probability of the postsynaptic cell 22:59 from firing. Now here is you see this 23:02 process of integration. Here was the IPSP 23:04 produced by the red neuron. Now you 23:10 have another action potential in red
23:11
23:11 neuron producing the IPSP, but shortly 23:14 thereafter you produce the same two 23:17 action potentials in the excitatory 23:19 neuron that you produced over here. Over 23:22 here these two action potentials 23:24 produced EPSP s that summated to 23:27 reach threshold, and produce an action 23:29 potential. Over here, the two EPSP 23:32 summate, but because of the simultaneous 23:36 inhibition, now the summation fails to
23:38
23:38 reach threshold and the postsynaptic 23:40 neuron does not fire an action potential. 23:42 So this inhibitory neuron can regulate, 23:46 it can regulate the ability of the 23:48 excitatory neuron to drive the 23:50 postsynaptic neuron. 23:52 Or as was said by one of the students, it 23:55 can regulate the probability of the 23:57 postsynaptic neuron being active. 24:02 Okay, now let's talk about micronetwork 24:07 motifs; fancy word, but all it means
24:12
is 24:12 that despite the tens of thousands of 24:17 different ways neurons can connect with 24:21 each other, there's a certain number of 24:23 common motifs that occur very frequently 24:26 in neural circuits used by the nervous 24:30 system. And what I've Illustrated here is 24:33 some of those common motifs that you're 24:35 going to be seeing over and over again 24:36 throughout the course. Let me just give 24:39 you a very brief introduction to them
24:41
24:41 and then we'll talk about some of them 24:43 in more detail. So here is feed-forward 24:46 excitation. We've already seen that one 24:48 neuron excites 24:50 a postsynaptic neuron. Here is a 24:54 circuit called feed-forward inhibition. 24:56 In this diagram, by the way, the green 24:59 cells are ones that (produce) have 25:01 excitatory effects; the red cells have 25:03 inhibitory effects. So this one's called 25:06 feed-forward inhibition.
25:07
An excitatory 25:08 neuron activates this red neuron, and 25:13 this red neuron in turn inhibits the 25:15 neuron that it's connected to. Seems kind 25:19 of odd, and we'll see how this actually 25:21 works in a real circuit. And then there's 25:23 this phenomenon of convergence and 25:25 divergence, and it simply is a 25:28 restatement of what I told you about the 25:29 basic connectivity patterns. So here you 25:32 see one postsynaptic neuron receiving
25:35
25:35 connections from three different neurons. 25:37 So these three different neurons are 25:39 converging on the same postsynaptic 25:41 neuron. So here's convergence, and here is 25:45 divergence. One individual presynaptic 25:47 neuron connects to many different 25:50 postsynaptic neurons. Here's a circuit 25:52 called lateral inhibition - an excitatory 25:55 neuron connects, or excites, an inhibitory 26:00 neuron, and that inhibitory neuron in
26:02
26:02 turn it feeds back to inhibit adjacent 26:05 neurons to the first one that was active. 26:08 I'll show you more details of this in a 26:10 moment. Here's an interesting circuit 26:12 called feedback or recurrent inhibition - 26:14 an excitatory neuron activates a 26:18 postsynaptic neuron the postsynaptic 26:19 neuron activates an inhibitory neuron 26:23 that makes an inhibitory connection onto 26:25 the neuron that was first activated. 26:28 Does anybody see a possible function for
26:30
26:30 this just thinking about it? It's kind of 26:37 like a braking mechanism. This neuron 26:39 gets active; the postsynaptic neuron gets 26:42 active; the postsynaptic neuron feeds 26:43 back and says, “Hold on I don't want you 26:45 to be active anymore. I had enough.” So 26:47 this is a braking mechanism. Once you 26:49 start this thing going, it comes back and 26:51 turns it off. Here's an interesting 26:53 circuit; look at this one.
26:55
An inhibitory 26:55 neuron connects to an inhibitory neuron 26:57 which connects to another inhibitory 26:58 neuron which connects to another 26:59 inhibitory interneuron which connects 27:02 back to itself. What could this possibly 27:04 do? We'll see in a moment. And then 27:10 there's just like there's feedback or 27:13 recurrent inhibition, there is feedback 27:15 or recurrent excitation. And what do you 27:18 think intuitively this kind of circuit 27:19 can do? It's a switch.
27:25
Once this neuron is 27:25 active, the postsynaptic neuron is active, 27:28 which then feeds back to activate the 27:30 neuron that started at all. So once you 27:32 get this thing going, it will go on and 27:34 on forever, in principle. So this is a 27:36 switch, and switches are really important 27:38 in some cases in the nervous system, 27:39 because you'd like to have some of your 27:41 neurons switching on right now, the 27:43 neurons that are involved in memory
27:45
27:45 mechanisms, so you can remember this 27:47 lecture tomorrow. 27:49 So let's talk about 27:50 some more these in more detail. Let's 27:52 talk about the feed-forward excitation 27:53 and feed-forward inhibition. And this is 27:55 particularly relevant to what you just 27:57 heard from Dr. Oakes. So here is the 28:02 stretch reflex, the monosynaptic reflex 28:05 that Dr. Oakes talked about because it's 28:08 called the monosynaptic reflex because 28:09 it involves a single synapse in the
28:12
28:12 spinal cord. It's actually a little more 28:14 complicated, but basically it can be 28:16 boiled down to that. 28:17 So here's that neurologist hammer 28:19 that Dr. Oakes talked about. The hammer 28:21 taps the tendon, stretches the tendon, 28:23 activates receptors (stretch receptors). 28:27 Those stress receptors initiate action 28:30 potentials, indicated by these little 28:32 circles. The action potentials then 28:35 propagate to the Central Nervous System
28:37
28:37 to the spinal cord where they produce a 28:42 feed-forward excitatory connection onto 28:44 this blue neuron. That neuron becomes 28:47 active. Action potentials propagate out 28:49 the ventral root because the contraction 28:52 of the skeletal muscle, and here is the 28:55 recent subsequent reflex. This would be a 28:59 “grade 2 reflex” based on your chart. 29:03 Simple monosynaptic reflex involving a 29:07 feed-forward excitation circuit.
29:10
Now what 29:10 you also see here is another interesting 29:12 circuit motif that we've talked about. 29:14 Look at this neuron here, which is called 29:16 an interneuron, this black one. Now an 29:19 interneuron is called an interneuron 29:21 because it's interposed between one 29:23 neuron and another neuron. So here the 29:26 interneuron is interposed between this 29:28 blue sensory excitatory neuron and this 29:31 red motor neuron, which happens to be a 29:33 flexor motor neuron, right?
29:35
Extensor motor 29:35 neuron. Flexor motor neuron. Note that 29:39 with this reflex we activate the 29:43 extensor motor neuron. Isn't that what 29:45 you want to do, to have the reflex? And 29:48 this is, by the way as was indicated, this 29:50 is a simple way of assessing the 29:52 integrity of the nervous system, with 29:54 this simple reflex test. 29:56 All right. So. It tests; it tells us how 29:59 well information is being propagated
30:01
30:01 here, it tells us something about 30:02 synaptic transmission, tells us about the 30:05 motor synapse here in the muscle cell. So 30:08 one can glean a lot of information about 30:11 the integrity of our nervous system just 30:12 by using this simple reflex and others. 30:16 Okay, the neuron, the presynaptic neuron 30:20 here, the green one, excites the 30:22 interneuron. The interneuron releases the 30:25 neurotransmitter producing an IPSP in 30:27 the flexor motor neuron.
30:31
Why do you want 30:31 to inhibit the 30:32 flexor motor neuron? Because you want to get 30:36 the extension. You don't want, you want to 30:38 decrease the probability of having this 30:41 neuron being active and producing a 30:43 inappropriate contraction, right? So, 30:47 this feed-forward inhibition is a way of 30:49 regulating neural circuits 30:52 Let's talk 30:53 about convergence and divergence using 30:55 again, the spinal circuit.
30:57
So here is the 30:58 very simplified version of the basic 31:00 circuit. The sensory neuron connects to 31:02 the extensor motor neuron. It also 31:04 connects to the inhibitory interneuron 31:05 which would, in this case, would inhibit 31:09 the flexor motor neuron. It's more 31:11 complicated than that is, that the 31:13 sensory neuron just doesn't connect to 31:14 one motor neuron, one extensor motor 31:17 neuron, it connects the many different 31:19 extensor motor neurons.
31:22
So this is the 31:22 principle of divergence. And here's the 31:26 principle of convergence, any one motor 31:28 neuron doesn't just receive synaptic 31:31 connections from a single sensory neuron. 31:33 It receives synaptic connections from 31:35 many different sensory neurons. So it's 31:37 this combination of convergence and 31:39 divergence that really makes this reflex 31:42 work. It's not just that one synapse, it's 31:44 a whole bunch of synapses.
31:47
31:47 Okay let's talk about lateral inhibition - lateral 31:49 inhibition is a very important circuit 31:51 that mediates a phenomenon called edge 31:54 enhancement. And what I mean by that is 31:58 shown in this little panel here, showing 32:02 two rectangles: a dark one and a lighter 32:05 one next to each other. Now if you look 32:10 at this carefully, I can tell you in 32:12 advance that the illumination here, the
32:15
32:14 intensity of the illumination, is totally 32:16 uniform throughout in the intensity for 32:19 the darker rectangle, and it's totally 32:22 uniform throughout for the lighter 32:25 rectangle. But if you look closely at the 32:29 border between these two rectangles, you 32:32 may notice that the at the border of the 32:37 darker rectangle, it seems a little bit 32:39 darker than the rest of the rectangle. 32:42 And at the border of the light rectangle, 32:45 with the dark
32:46
32:46 rectangle, it seems a little bit lighter 32:48 here than it does throughout the rest of 32:50 the rectangle, So this is, does 32:52 everybody see that? This is not a trick. 32:55 This is totally the same 32:57 illumination here and here. So this is edge 33:00 enhancement. This allows us to detect 33:02 edges, and that can be really important 33:04 if you're walking at night near a cliff. 33:07 You want to make sure you know where the 33:08 edge is, right? 33:09 So edge enhancement.
33:13
Now how does this 33:15 work this simple circuit of lateral 33:17 inhibition allows us to make this 33:19 distinction. And here's a very simple 33:22 cartoon of how it works. So here's the 33:26 dark rectangle, here's the light 33:27 rectangle. Now, here is a very simple 33:31 description of the circuitry in the 33:34 retina. So, we have light impinging on 33:37 photoreceptors, and as a result of the 33:39 light, the photoreceptors become active 33:42 and initiate action potentials.
33:45
Those 33:45 photoreceptors in turn make synaptic 33:47 connections to so called second-order 33:49 neurons, and those neurons then transmit 33:52 information to the Central Nervous System, 33:54 right? And the assumption here is 33:57 so these five, see the five, five, five, and 34:00 ten, ten, ten. That's just a reflection of 34:02 the intensity of illumination. Let's just 34:05 say this is 5 lumens here, and the 34:07 brighter structure is 10 lumens, right?
34:09
So 34:10 because you have 5 lumens, we're going to 34:11 make a very simple assumption that 34:13 intensity of light of 5 lumens produces 34:15 action potentials of 5 Hertz or 5 per 34:19 second, right? So 5 per second here, 5 per 34:22 second here, 5 per second here, and the 34:24 neurons that are activated by the 34:26 brighter intensity stimulus through the 34:29 retina, then fire more action potentials, 34:30 so 10 lumens (this great simplification)
34:33
34:32 fire 10 action potentials per second. So 34:35 if this was all there was through the 34:37 circuit without lateral inhibition, what 34:39 you would perceive is exactly what is 34:45 presented to the eye, right? This is dark 34:50 and this is bright, and there's no 34:52 difference at the edge. Now with lateral 34:54 inhibition things change. So here's a 34:57 lateral inhibition. Each one of these 34:59 retinal receptors makes an inhibitory
35:02
35:02 connection to adjacent downstream 35:05 second-order neurons. The connection is 35:08 weak, as indicated by this .2. The 35:10 normal excitatory connection is +1, 35:12 the inhibitory connection is -2. So 35:15 each one of these neurons makes a 35:17 inhibitory connection to its neighbor; 35:20 that's the lateral inhibition. Now note, 35:22 the consequences of this. Over here, away 35:25 from the edge, the 5 intensity of
35:29
35:29 illumination now produces fewer action 35:30 potentials because there's some 35:32 inhibition from the neighbor, but it's 35:33 still 3 here and 3 here, right? That's 35:36 fine. And the neurons that are responding 35:38 to the brighter illumination, they are 35:40 still firing at a constant rate. It's 35:43 somewhat reduced (6 & 6). But the real 35:46 interesting thing occurs here at the 35:48 edge, because now the neuron that is 35:51 receiving the illumination at the edge
35:54
35:54 receives less inhibition from this 35:58 neuron at the edge, and therefore its 36:00 output is greater because it's getting 36:03 less inhibition from its partner over 36:05 here. And this neuron, which is responding 36:09 to the dim illumination, is receiving 36:12 more inhibition from its neighbor to the 36:13 right in the bright region. So then, that 36:16 consequence is that the neuron that's 36:19 responding here is firing less than its 36:22 partners, and the neuron that's
36:24
36:24 responding here is responding more, 36:26 giving a perceived 36:28 light difference at the edge. Think about 36:33 this. It's not intuitively obvious, but if 36:35 you work through the numbers I think 36:37 will become clear. 36:39 Now lateral 36:40 inhibition is great because it provides 36:44 us with this edge enhancement, but it 36:47 also has a problem, and it leads to a 36:50 so-called visual illusion and there's 36:52 one very famous visual illusion called 36:54 Mach bands.
36:55
36:56 You’ve probably all seen this again. So here, 36:58 what we see, is a gradient of 37:00 illumination. see it's bright, dark, bright, 37:03 dark, bright, dark, bright, dark. Right 37:05 throughout? It's all obvious. And also 37:08 what you see here is a horizontal stripe. 37:11 And you'll note, with this horizontal 37:13 stripe, that it appears to change in 37:17 illumination. Start here, it's bright here, 37:20 dark here, bright here. Does everybody see 37:22 that?
37:24
Well you're all seeing an illusion, 37:25 because it's the exact intensity here as 37:28 it is here. And you can see that by 37:31 putting a mask over this. So you don't 37:33 see this anymore, you just see the 37:36 horizontal bar. Here we go. Here's the 37:37 mask, and of course, this is, there's no 37:39 tricks here. 37:41 There's the mask. It's the exact same 37:47 intensity, so you've all been tricked, and 37:50 this shows that what you think you see, 37:52 is sometimes is not really there.
37:55
37:54 This is just a simple illusion. How does 37:57 this work? So the property of lateral 38:00 inhibition explains this. Why is this 38:02 dark here? This region is perceived as 38:05 being dark here because the neurons that 38:07 are responding to this intensity in the 38:10 retina have neighbors that are 38:13 responding to a bright light, so they are 38:16 inhibited by their neighbors. And why does 38:18 this appear to be darker here, because 38:21 this region is dark so these neurons are
38:24
38:23 inhibiting the neurons responding to the 38:26 band less so. Alright, so there is your 38 :30 visual illusion and just as the lateral 38:33 inhibition can explain edge enhancement, 38:36 it can also explain this very 38:39 ubiquitous visual visual illusion. 38:44 Let's talk about feedback or recurrent 38:47 inhibition. And before I talk about in 38:51 terms of neural network or a microbe 38:53 network, I want to talk about it in terms 38:54 of that so-called nanonetwork.
38:56
38:57 So, there's three levels of networks that 38:58 we're going to be talking about: micro 39:00 network - that's these types we, 39:02 involving a small number of cells, a 39:05 nanonetwork - involving biochemical reactions 39:09 works, and finally macronetworks - that 39:12 involve large connections or collections 39:14 of micro networks. So a nanonetwork of 39:18 feedback inhibition is important for 39:20 producing a phenomenon as shown here.
39:22
39:22 Now, the standard textbook version of neurons 39:25 is either they are a silent or they fire 39:28 action potentials when they are 39:29 stimulated with a synaptic input, but 39:32 some neurons, like this one, have a so 39:34 called endogenous of bursting behavior. 39:37 So in the absence of any input, this 39:40 neuron is firing bursts of action 39:42 potentials, becoming silent, firing bursts 39:45 of action potentials again and becoming 39:46 silent.
39:47
So this is called an endogenous 39:48 bursting activity. These kinds of 39:50 activity patterns could be important for 39:52 rhythmic behavior, like respiration. Well 39:55 How is it that we breathe in and out? 39:56 This kind of neuron could be responsible 39:58 for those kinds of rhythmic behaviors> 40:01 How does this work? This works through a 40:04 nanocircuit. Here is an idealized 40:07 version of that neuron, the outside of 40:09 the cell, the inside of the cell.
40:10
These 40:10 cells have specialized channels that 40:12 allow for calcium influx to occur. 40:15 Calcium is a positively charged ion; it's 40:17 at high concentration outside, low 40:18 concentration inside, the Feedback 40:21 Inhibition Network, we want to compare 40:23 this network, with this network. So the 40:26 calcium comes in and when calcium comes 40:28 in to depolarizes the cell, and as a 40:30 depolarize of the cell it brings the
40:32
40:31 cell to threshold, causing the initiation 40:34 of action potentials. But the interesting 40:37 part here is that, the increase in 40:38 calcium also reduces the conductance of 40:42 this channel to allow calcium inflow to 40:44 occur, so the calcium inflow stops. But 40:48 that calcium, then, which is inhibiting 40:50 the inflow of calcium, then is taken up 40:52 by buffers. And so the calcium 40:54 concentration is reduced allowing the
40:57
40:57 influx of calcium to occur again, and 40:59 repeating the cycle. So the point that 41:02 I'm trying to make here is that this 41:03 type of feedback inhibition circuit is 41:06 capable of generating an oscillatory 41:08 behavior, an oscillatory behavior in a 41:11 neuron, and oscillatory behavior in an 41:14 nerve circuit. This is a relatively short 41:17 lasting type of phenomena, short term 41:19 dynamics (once every 10 to 15 seconds).
41:21
41:21 There 41:22 is a sub cellular network, that's due to 41:25 feedback inhibition, that underlies 41:27 24-hour rhythms. And the big 24-hour 41:30 rhythm that you will know about is the 41:31 circadian rhythm. Circadian rhythms are 41:34 due to oscillations produced in a 41:37 special part of the brain called the 41:39 suprachiasmatic nucleus. It's located 41:42 just above the optic nerve, and what the 41:44 suprachiasmatic nucleus does is it
41:47
41:47 generates rhythmic patterns of output, 41:49 and those rhythmic patterns of output 41:51 have widespread effects on the release 41:54 of hormones from the pineal gland, 41:56 melatonin, regulation of the autonomic 41:58 nervous system, the daily cycle of our 42:00 body temperature, regulation of hormonal 42:03 release. Now it's interesting that this 42:05 very complex and profound phenomena can 42:08 really be boiled down to a simple 42:11 feedback inhibition circuit among one 42:14 gene.
42:15
So there's a gene called per, and 42:17 per leads the synthesis of the per 42:19 messager and in turn the PER protein. PER 42:23 protein, then, feeds back to inhibit the 42:26 transcription of the gene. And this 42:28 inhibitory cycle has a time course of 42:31 about 24 hours. So the protein is made, it 42:34 then inhibits the cycle, the protein is 42:36 degraded relieving the inhibition, 42:39 allowing the cycle to happen again.
42:41
42:41 Making more protein inhibiting the 42:43 transcription the protein is degraded 42:45 and the cycle repeats itself over and 42:47 over again at the 24 hour period. 42:55 Remember that crazy circuit with the 42:57 four inhibitory neurons inhibiting each 42:59 other? Well that can explain, at least in 43:03 principle, quadrupedal and locomotion. So 43:05 here's a little doggie showing the 43:07 pattern of a walk. It's a gate, so called 43:10 gate, right?
43:11
And a walk is associated with 43:12 a movement of the left front limb, and 43:15 quarter of a cycle later, the right hind 43:17 limb. And then half cycle later, the right 43:19 front, and then the left hind. So question 43:22 is, how can this kind of gate be produced 43:24 by the nervous system? Can a simple 43:26 circuit do it? Another secondary question 43:29 is, that animals can do more than just 43:31 walk. As shown over here, they can also 43:33 trot.
43:34
Trot is a gait associated with a 43:37 in phase movement of the left front and 43:39 the right hind, and with 180 degrees out 43:42 of phase of the opposite limb. But they 43:44 can also do something called a bound - 43:46 where the two front limbs are in phase, 43:49 and a 180 degrees out of phase with the 43:51 rears. And then there's something like a 43:53 gallop - which is very similar to a bound. 43:55 So we don't understand the neural 43:57 circuits that produce these, but let's
44:00
43:59 think about ways in which, in principle, 44:01 you could do it. There's all the dogs. (You 44:06 can get these animations online) so one 44:10 way you could do it is as follows: 44:12 Remember that endogenous bursting neuron, 44:14 you could take four of those endogenous 44:16 bursting neurons and have one neuron 44:19 hooked up to the left front limb, another 44:22 hooked up to the left hind limb, and so 44:25 forth.
44:26
And if you set up the timing just 44:28 right so you put these two guys in phase 44:31 with each other, and these guys (hind 44:33 limbs) 180 degrees out of phase with the 44:35 first two, you could, in principle, produce 44:38 a gallop. These two limbs would go first, 44:40 and then, would be followed half a cycle 44:43 later by the hind limbs. 44:45 Alright, in theory 44:46 it would work, but in practice there'd be 44:48 problems, because one is you'd have
44:50
to 44:50 set these up so they're exactly in phase, 44:53 right like this. And what would happen if 44:55 there was some drift in the activity, so 44:58 this one started firing a little bit 44:59 earlier or later. Well, your problem would 45:02 be that things would get very 45:04 uncoordinated. So this doggy is not 45:08 going to gallop. In fact, it's not even 45:09 going to walk very well. 45:10 So one way you could do this 45:12 is with this circuit here.
45:17
You take now, 45:17 four of these neurons, and each one 45:21 becomes a bursting neuron, and when you 45:23 do that you get a circuit like this. And 45:26 if you do a simple twist of the circuit, 45:28 shown here, it's the same circuit, this 45:30 blue one that was, that green one down 45:31 here is now up here. Now this circuit 45:33 with just those four neurons with those 45:36 endogenous bursting properties can 45:38 produce each of those four different
45:41
45:41 gallops with slight modifications and 45:43 the strength of the synaptic connections 45:44 between the neurons. So here's an example, 45:47 with this relatively simple circuit, can 45:48 at least, in principle, produce a fairly 45:50 complex behavior, which gives us hope for 45:52 understanding more complex behaviors in 45:55 the nervous system. 45:57 Here's a circuit. I'm 45:59 not going to go into you, can read about 46:00 it.
46:01
This is a circuit for the recurrent 46:02 feedback excitation. This is important in 46:05 the hippocampus, that's a region of the 46:06 brain important in memory mechanisms 46:08 where the memory is distributed among 46:10 the strengths of the synaptic 46:11 connections. And then finally we have 46:14 macro circuits. All these different of 46:17 the brain, regions of the brain, are 46:18 highly interconnected to each other and
46:21
46:20 within each one of these regions are 46:22 micro circuits, but this micro circuit is 46:24 communicating with another micro circuit 46:26 over here. And collectively these 46:28 circuits form something that looks like 46:30 this. This is a macro circuit for the 46:32 processing of visual information. You 46:34 need to commit this memory. No I'm only 46:36 joking. But this is just as to illustrate the 46:39 point of how complicated things are.
46:40
So 46:41 here, down here processing of information 46:43 from the retinal ganglion cells, the 46:46 lateral geniculate, up to various visual 46:48 cortical areas. All these different 46:50 cortical areas, are communicating with 46:52 each other through feed-forward and 46:54 feedback connections. 46:56 We've given you a 46:57 brief introduction to the properties of 46:59 neurons and neural circuits. What you're 47:02 going to learn about next in the lecture
47:03
47:03 from Dr. Bean is how these circuits are 47:06 formed. How do they form in development, 47:08 and how is it possible that one neuron 47:11 can be specifically wired and connected 47:13 to its appropriate partner. 47:15 Okay. Thank you.
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