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. 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 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, 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 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. 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. 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. 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, 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 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. 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 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. 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 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. 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 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 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 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 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 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. 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 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 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 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 10:48 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. 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 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 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, 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. 12:59 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. 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. 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 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 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 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 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 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. 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. (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 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. 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. 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. 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 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 19:28 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 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 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 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. 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. 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 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 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 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 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 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 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. 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 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 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 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. 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. 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 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 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 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. 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? 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 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. 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. 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. 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 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 32:14 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 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. 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. 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? 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) 34:32 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 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 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 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 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. 36:56 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? 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. 37:54 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 38:23 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. 38:57 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 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. 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. 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 40:31 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 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 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 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. 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 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? 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. 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 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. 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 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. 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 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. 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 46:20 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. 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 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.
