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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