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3G3 Introduction to Neuroscience
Dr. Henning Sprekeler ([email protected])!
Course aims !
• an introduction to how the brain !
– processes sensory information!
– makes decisions!
– learns through experience!
– lays down memories!
• from a computational and engineering perspective!
!
Lecture 1: Outline !
• Overview of course, lab, supervisions, exams …!
• Primer: neurons & synapses!
• Introduction to sensory processing
!1
Module overview
H. Sprekeler/R. Turner: Perception & Decisions (8L)!
Introduction (1L)!
–
Action Potential (1L)!
–
–
Rich Turner: Hearing (2L)
Multisensory Integration (1L)!
–
Attention (1L)!
–
Decision making (2L)!
–
!
Mate Lengyel: Learning and memory (8L)!
Molecular and cellular bases of learning and
–
memory (2L)!
Classical and instrumental conditioning (2L)!
–
Higher order learning and memory (2L)!
–
Neuroeconomics (2L)
–
2 supervisions
2 supervisions
!
Mon 20 Jan 11am L1 Sprekeler
Tue 21 Jan 9am L2 Sprekeler
Mon 27 Jan 11am L3 Turner
Tue 28 Jan 9am L4 Turner
Mon 3 Feb 11am L5 Lengyel
Tue 4 Feb 9am L6 Lengyel
Mon 10 Feb 11am L7 Lengyel
Tue 11 Feb 9am L8 Lengyel
Mon 17 Feb 11am L9 Lengyel
Tue 18 Feb 9am L10 Lengyel
Mon 24 Feb 11am L11 Sprekeler
Tue 25 Feb 9am L12 Sprekeler
Mon 3 Mar 11am L13 Sprekeler
Tue 4 Mar 9am L14 Sprekeler
Mon 10 Mar 11am L15 Lengyel
Tue 11 Mar 9am L16 Lengyel
http://www-g.eng.cam.ac.uk/lifesciences/ (teaching section)
• Handouts
• Short review papers
• General textbooks (In CUED and college library):
•Neurophysiology by Roger Carpenter
•[Principles of Neural Science by Kandel, Schwartz & Jessell]
Exam
•3 out of 4 short answer question
Relevant IIB Year Modules
• Computational neuroscience
• Machine learning
!2
Coursework
Runs 3 times in DPO [cluster 2]!
– Wednesday 29 January 11-1pm !
– Friday 31 January 11-1pm !
– Wednesday 5 February 11-1pm!
• Coding in visual cortex!
– Matlab simulation of two coding schemes!
• Principal components analysis!
• Sparse coding!
• Read handout introduction before lab
– Do vector differentiation before the lab!
– You can do lab in own time if necessary
Handouts in EIETL
Feedback sessions will
be arranged by email
!3
Status of neuroscience field
Trends in Neuroscience
•
First degree in neuroscience: 1973, Amherst College!
•
Annual meeting of Society for Neuroscience: ~30,000 attendees!
•
Number of papers published: ~30,000 / year!
•
Nobel laureates: ~43 between 1904-2004
Trends in Computational Neuroscience
• Annual Cosyne Meeting: ~600 attendees
• Nobel laureates: 2 (in 1963)
• Massive expansion in research and engineering fields
– positions in computational neuroscience in USA: Columbia, Cornell, MIT, NYU, Princeton,Yale, UCSF, ...
– background: engineering, physics, math, computer science, biology, psychology, ...
• Related field : neural networks, connectionism, machine learning, learning theory, ...
!4
David Marr’s levels of understanding (1982)
1)
Computational theory What is the goal of the computation, why is it
appropriate, and what is the logic of the
strategy by which it can be carried out?
2)
Representation and algorithm
How can this computational theory be
implemented? What is the representation for input and
output and what is the algorithm for the
transformation? !
!
3) !
Implementation
How can the representation and algorithm be
realised physically?
"5
Primer: Overview nervous system
Cerebrum, cerebellum, brainstem, and spinal cord
(analysis and integration of sensory and motor information)
Sensory
Components
•Sensory nerves and
ganglia
!
•Sensory Receptors (at
surface and within
body)
Central!
Nervous System
Motor Components
Somatic
Motor System
Autonomic
Systems
!
!
!
•Autonomic nerves
and ganglia
Peripheral!
Nervous System
•Motor nerves
Effectors
Internal and
external
environment
Smooth muscles,
cardiac muscle and
glands
Skeletal (striated)
muscles
!6
Primer
Neurons: The building blocks of the brain
Dendrites
Axon Terminal
Cell
Body
Axon
Myelin
Sheath
Interesting feature is electrical potential between inside and outside of cell !
!7
Primer: Intracellular recording
•
•
!
Neuron cell bodies vary in size from 4 to 100 µm!
Glass microelectrode produced by an "electrode puller" !
– Heats up the middle of a 1 mm glass tube and pulls ends apart at high velocity !
– The result is an electrodes with a very fine tip down to 0.1 µm.!
Electrode tip!
The electrodes are then filled with electrolytes (conducting solution)
in cell body
2 µm
!8
Primer: Extracellular recording
Glass electrode or metal shaft electrodes with tip diameter of ~3-10 µm!
Record electrical changes from outside cell
!9
Primer: Neurons. All or nothing firing
Dendrites
Axon Terminal
Cell
Body
Myelin
Sheath
3G2 covers this in detail
Depo
lariza
tion
0
Electrode
ation
Action potential= spike= firing=impulses
+40
Repolariz
At rest inside axon negative potential to outside
Activity passes down the axon
•
Wave of depolarization passes along axon
•
All or nothing action potential
•
The axon branches into axon terminals
Voltage across axon wall (mV)
Axon
Threshold
Resting state
-70
0
1
2
3
4
Time (ms)
5
=
Time (s)
!10
Primer: Synapse, the Connecting elements
Bouton
Axons contact next neuron at synapses !
• Boutons discharge chemical neurotransmitter!
• Neurotransmitter diffuses across gap!
• Binds to receptors and causes charge to be injected into the next neuron!
• Charge magnitude depends on synaptic strength
!11
Primer: Spike generation. Integrate-and-fire
rate
Time
Time (s)
rate
rate
Time
Time
rate
Time
•
•
•
Dendritic tree collects inputs from other neurons !
Spike generation: The axon generates a spike whenever enough charge has flowed in at synapses!
Learning takes place at synapses: depends on pre- and post-synaptic spikes, and their relative timing
!12
Brain’s building block: Neurons
!13
Nervous systems span a range of spatial scales
1m
10 cm
CNS
Systems
1 cm
Maps
1 mm
Networks
100 µm
Neurons
1 µm
Synapses
0.1 nm
Molecules
At every scale there is interesting structure that we would like to understand
But neuroscience is a huge field ! we will be highly selective
!14
Brain is very different from today’s computers
1 mm3 of cortex
1 mm2 of a CPU
Number of units
50,000 neurons
1 million transistors
Connections/unit
10,000
2
Total connections
500 million
2 million
Wiring
4 km of axons
0.002 km of wire
Whole brain
Whole CPU
Weight
1.3 kg
~0.4kg
Power
20 W
27 W
Units
1011
108 transistors
connections
1 x 1015
2 x 109
wiring
8 million km of axons
2 km of wire
neurons
Neurons!
– Slow and unreliable elements!
– Parallel and highly connected : memory and processing not separated!
– Learning involves neurons and synapses changing properties!
Currently brain has better engineering solutions for many task !
e.g. vision, speech recognition, control …!
But not all: information search, accurate memory
!15
Coding of sensory information
Function of a sensory system: To provide a constantly updating representation of the outside world
!
Perceptions are mental creations that differ qualitatively from the physical properties of stimuli
!
Electromagnetic waves ……………………. !……………………………………… colour
Pressure waves ……………………………..!……………………………………… sound
Chemicals in the air………………………..-.!……………………………………… smells
Perception
Sensory input
Physical
Stimulus
Transduction
Nerve
impulses
Neural
Processing
Conscious
experience
Non-conscious
use
!16
Sensation ≠ Perception
Sensory input
Perception
Physical
Stimulus
Transduction
Neural
Processing
Nerve
impulses
Conscious
experience
Perception is a mixture of !
– Ascending mechanisms e.g. receptor activation!
– Descending mechanisms e.g. attention, action!
Sensation ≠ Perception!
– Perception can change even through sensory input is fixed
!17
Receptors
•
Neurons do not respond directly to stimuli such as light, sound or pain!
–
Requires stimulus transduction !
• converting physical stimulus into electrical signal!
• performed by a specialized cell called a receptor
•
Receptors !
–
specialised to sense one stimulus type at normal levels !
• e.g. touch, taste, light, … !
• can sometimes react to other energy sources e.g. a blow to the eye!
–
•
stimulus causes receptor to change membrane potential (more later)
! leading to nerve impulse
Can reproduce sensation by stimulating nerves directly !
–
hitting funny bone is direct activation of ulnar nerve !
–
cochlear implant
Sensory input
Physical
Stimulus
Sound
Perception
Transduction
Nerve
impulses
Stimulate
Neural
Processing
Conscious
experience
Hearing
Sensory pathways carry 4 type of information: Modality, Location, Intensity and Timing
!18
1. Modality: Receptors
•
Act as filters of the environment: detect and respond to some stimuli but not others!
•
Different receptors are used to convert different energies into neural activity!
•
Convert energy into the language of the nervous system: action potential (spikes) !
•
Offer enormous diversity that reflect the survival needs of different animal!
• Snakes: infrared radiation, Fish: electrical energy, Bats: ultrasound, Birds: magnetic
Receptor class
Modality
“Energy”
Cell type
!
!
Touch
Pressure
Cutaneous mechanoreceptors
Proprioception
Displacement
Muscle and joint receptors
Hearing
Sound
Hair cell (cochlea)
Balance
Gravity
Hair cell (vestibular labyrinth)
Electromagnetic
Vision
Light
Rods, cones
!
Taste
Chemical
Taste buds
Smell
Chemical
Olfactory sensory neurons
Itch
Chemical
Chemical nociceptors
Mechanical
Chemical
!19
1. Modality: Labelled Line Code
The receptors and neural channels for the different senses are independent
Each sensory neuron responds to only one modality
We can “label” each sensory neuron (“line”) by the modality it codes
No matter how the eye is stimulated
• by light
• mechanical pressure
• electrical shock
resulting sensation is always visual
!20
1. Modality: Receptors tuned within modality
• Receptors respond to a limited range of stimulus energies!
– Bandwidth limited = tuning!
– Tuning curves!
• Modalities can have sub-modalities!
– e.g. taste , colour
Retinal receptors
Auditory receptor
Blue
Green
Red
Threshold
Characteristic Frequency
!21
2. Location: Receptive field
The spatial distribution of sensory neurons activated by a stimulus conveys information
about the stimulus location
Receptive field of a neuron
• subset of the sensory space in which
an appropriate stimulus elicits a
reaction in the neuron
• More generally the properties of a
stimulus that gives a response in a
neuron
– the frequency of a sound
– the properties of a face
– chemical content on tongue
Tactile
Receptive
Field
!22
2. Location: A. local coding scheme
• Tile sensory space with receptive fields!
• Discrimination of several stimuli is easy!
• For n bins and D dimensions !
– Number of neurons increase as nD!
– Combinatorial explosion
y
Receptive field of neurons
x
!23
2. Location: B. intensity coding scheme
Each neuron codes for one dimension by firing at different rates!
– Few neurons needed!
– But!
• Resolution determines by reliability of neuron (i.e. noise)!
• Time to determine spike rate precisely too long to explain behaviour!
• Hard to represent multiple stimuli
X neuron
y
y
Y neuron
Low firing rate
Time (s)
Low firing rate
Time (s)
High firing rate
•
High firing rate
x
x
!24
2. Location: C. ensemble coding scheme
• Neurons have large overlapping receptive fields!
• Position encoded by the ensemble activity!
– Can represent multiple stimuli !
– No need to estimate rate over a long time period
y
x
!25
2. Location: Receptive field (RF) size and performance
•
Salamander !
– can localise 0.5 mm mite at 20 cm (0.15O)!
– Minimum RF diameter of 10O with mean 41O
!
!
•
Human two point discrimination !
– 1.4mm!
– RF size ~7 x 7 mm!
!
•
Visual hyperacuity!
– few seconds of arc: corresponds to the width of
a pencil viewed at a distance of 300m ! !
– RF 30 seconds of arc diameter of a foveal
photoreceptor
!26
2. Location: ensemble (coarse) coding
r
What is optimal size of RF
• Consider binary neurons
• Accuracy
– number of different encodings as we transit a line
– each time the line crosses a RF the encoding changes
– number of encodings = 2 x RF that the line penetrates= proportional to the line
density and number of neurons
!27
Location: Limitations of coarse coding
•
•
achieves accuracy at the cost of resolution!
– Accuracy is defined by how much a point must be
moved before the representation changes.!
– Resolution is defined by how close points can be
and still be distinguished in the representation.!
Large RF makes it difficult to associate different
responses with similar points, because their
representations overlap!
– The boundary effects dominate when the fields are
very big.
Resolution
Accuracy
!28
Resolution 2 vs 3
Simulate 10,000 2D Receptive fields with radius r
randomly over x= [-50,50] & y=[-50,50]
Calculate probability of a different encoding for
• 2 stimuli at (-1,0) & (1,0)
• 3 stimuli at (-1,0), (0,0) & (1,0)
(r)
!29
2. Location: Density of receptors
The density of sensory receptors and the size of receptive field!
– determine the resolution of sensory systems !
– e.g. finger tips & fovea
!30
3. Intensity
As stimulus intensity increases!
• Firing rate increases. !
• Greater number receptors !
– population code !
– often different receptors for different levels
Perceived sensation intensity
Number of spikes
Magnitude estimation
Neural code of stimulus magnitude
Skin indentation
Stimulus intensity
!31
4. Timing
Temporal properties !
•
Coded by change in frequency of sensory neurons!
•
Sensors adapt to constant stimuli!
– removes constant stimuli from consciousness !
• slowly adapting receptors (minutes)!
• rapidly adapting receptors (seconds)!
•
Sensory processing interested in contrast !
– e.g. temporal and spatial changes
!32
Sensory processing is hierarchical
•
•
•
•
Sensory inputs are conveyed by a population of neurons. !
Hierarchical organisation with many relays !
Receptive field tend to become larger and more complex!
Convergence of inputs
Feedback inhibition
!33
Overview
• Building block of the brain!
– neurons !
– synapses!
• Sensory processing mechanisms for!
–
–
–
–
Modality!
Location!
Intensity!
Timing
!34