Download P312Ch02_Nervous System, Neurons Lecture

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

Biochemistry of Alzheimer's disease wikipedia, lookup

Cognitive neuroscience wikipedia, lookup

Neuroeconomics wikipedia, lookup

Environmental enrichment wikipedia, lookup

Haemodynamic response wikipedia, lookup

Connectome wikipedia, lookup

Neural engineering wikipedia, lookup

Brain wikipedia, lookup

Types of artificial neural networks wikipedia, lookup

Convolutional neural network wikipedia, lookup

Neuroregeneration wikipedia, lookup

Apical dendrite wikipedia, lookup

Action potential wikipedia, lookup

Artificial general intelligence wikipedia, lookup

Holonomic brain theory wikipedia, lookup

Neuromuscular junction wikipedia, lookup

Electrophysiology wikipedia, lookup

Activity-dependent plasticity wikipedia, lookup

Multielectrode array wikipedia, lookup

Axon guidance wikipedia, lookup

Neural oscillation wikipedia, lookup

Central pattern generator wikipedia, lookup

Metastability in the brain wikipedia, lookup

End-plate potential wikipedia, lookup

Caridoid escape reaction wikipedia, lookup

Mirror neuron wikipedia, lookup

Endocannabinoid system wikipedia, lookup

Premovement neuronal activity wikipedia, lookup

Synaptogenesis wikipedia, lookup

Development of the nervous system wikipedia, lookup

Circumventricular organs wikipedia, lookup

Nonsynaptic plasticity wikipedia, lookup

Neural coding wikipedia, lookup

Clinical neurochemistry wikipedia, lookup

Single-unit recording wikipedia, lookup

Optogenetics wikipedia, lookup

Axon wikipedia, lookup

Pre-Bötzinger complex wikipedia, lookup

Feature detection (nervous system) wikipedia, lookup

Biological neuron model wikipedia, lookup

Neuroanatomy wikipedia, lookup

Neurotransmitter wikipedia, lookup

Channelrhodopsin wikipedia, lookup

Chemical synapse wikipedia, lookup

Molecular neuroscience wikipedia, lookup

Synaptic gating wikipedia, lookup

Neuropsychopharmacology wikipedia, lookup

Stimulus (physiology) wikipedia, lookup

Nervous system network models wikipedia, lookup

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