dr. thomas nowotny - university of...
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Computational Neuroscience of Olfaction
Dr. Thomas NowotnyCentre for Computational Neuroscience and Robotics,
Informatics, Biology & Environmental SciencesUniversity of Sussex, Brighton, UK
2327 March 2009Universidad Autónoma de Madrid
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Course outline
● Lecture 1: Olfaction – The sense of smell● Lecture 2: The connectionist approach I:
Tools Exercises 1: Statistical modeling
● Lecture 3: The connectionist approach II: Modelling insect olfaction
● Lecture 4: Rate models of the antennal lobe; Hopfield's model of olfaction Exercises 2: Hopfield's olfaction model
● Lecture 5: The pheromone subsystem
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Exercises
● You will learn twice as much if you practice some of the stuff we talk about
● I will review the material from the Exercises in a later Lecture
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Short Course: Computation of OlfactionLecture 1
Lecture 1: Introduction
Olfaction – the sense of smell
Dr. Thomas NowotnyUniversity of Sussex
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Olfactory space
● To smell is the process of detecting volatile chemicals
● The “olfactory space” of all possible stimuli is very different from other senses:– Many “chemical degrees of freedom”– No clear similarity structure– No absolute scale of concentration– No clear definition of objects
Let's have a closer look:
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Olfactory space – degrees of freedom
Visual space
● 2 spatial dof● Frequency (color)● Intensity
Auditory Space
● Frequency● (2 spatial dof)● Intensity
● Number of aromatic atoms● Number of hydrophobic atoms● Number of carbon atoms● Number of hydrogen atoms● Number of oxygen atoms● Sum of the atomic polarizabilities● Number of rotatable single bonds● Fraction of rotatable single bonds● ...
Schmuker et al. (2006) list about90 chemical descriptors, socalled“odotopes”:
Intensity (inhomogeneous)
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Olfactory space – structure
● No clear neighborhood structure
Visual space
● Euclidean distance of points● Similarity of colors
Auditory Space
● Frequency distance● (Euclidean distance of sound sources)
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Olfactory space – human perception
Enantiomers ofcarvone
carawaysmell
spearmintsmell
L. Turin, F. Yoshii, Structure odor relations: a modern perspective, http://www.flexitral.com/research/review_final.pdf
Musky odors
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Theories of odor perception
● There are (at least) two theories of odor perception:
● Odotope theory: The functional groups (odotopes) determine smells
● Vibrational theory: The resonance spectra of chemicals as witnessed by infrared spectrometry determine smells
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Olfactory space: Additional complexity
● “Odors” are typically complex mixtures of chemicals, e.g., the smell of coffee is believed to have about 1000 components, similarly, the smell of a rose etc.
● Animals (and humans) can, however, also recognize the components in a mixture (to some extent)
● Odors need to be recognized over large ranges of concentrations; However it is known that this ability sometimes breaks down
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Olfactory system humans
Olfactoryreceptorneurons (ORN)
Air flow
Olfactorybulb (OB)
Olfactoryepithelium
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Olfactory system – insects
Hei
senb
erg,
Nat
Rev
Neu
rosc
i 4,
266
(200
3)
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Two olfactory systems
● There are two separate olfactory systems the general olfactory system and the pheromone system
● In mammals:– General: Olfactory epithelium – olfactory bulb –
Piriform cortex– Pheromone: Vomeronasal organ – acessory
olfactory bulb – amygdala / hypothalamus
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Two olfactory systems
● In Insects:– General: Antenna – antennal lobe – mushroom
body/ lateral protocerebrum– Pheromone: Antenna – Macroglomerular complex
– lateral protocerebrum
We will first focus on the general olfactory system
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Olfactory transduction pathway (mammal)
Stages
● Mucus, odor binding proteins● Olfactory receptor neurons
● Mitral cells/ granule cells in the olfactory bulb● Piriform cortex
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Olfactory transduction pathway
● Sensillum on the antenna (sensillum lymph, OBP)
● ORN● Glomeruli, projection
neurons (PN), local neurons (LN)
● Mushroom body, lateral protocerebrum
● Olfactory epithelium in the nose (mucus, OBP)
● ORN● Glomeruli, mitral cells,
granule cells (periglomerular cells)
● Piriform cortex
Insect Mammal
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Antenna and sensilla
Male moth
Female moth
Antennae
Sensillae
Antennadetail (moth)
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Sensillum detail
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Sensillum recording
● One can record from single sensillae
● If the ORN respond to a stimulating chemical, one sees strongly elevated firing (bar = odor stimulation)
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Electrical transduction in odor receptors
● About 350 odor receptor genes known in humans, 1000 in mice, about 43 in Drosophila
● Receptors are expressed in ORNs
ORN
Mucus /sensillumlymph Cells are activated by
a second messengercascade
The influx of Na+ makesthe spikes.
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Response profile of receptorsO
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Odorant Receptor
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Glomerular map (honeybee)
S. Sachse, A. Rappert, C. G. Galizia, Europ. J. Neurosci. 11: 3970 – 3982 (1999)
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Ca imaging of activity in glomeruli
S. Sachse, A. Rappert, C. G.Galizia, Europ. J. Neurosci. 11: 3970 – 3982 (1999)
Ca imaging in the olfactorybulb of honeybee.
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Glomerular response maps
S. S
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. Rap
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. G
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. J. N
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Glomerular activity maps (moth)
1Hexanol (+/) Linalool
Grey – active glomeruli:Different odors activate different sets of glomeruliwhich can be overlapping.
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active active
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Olfactory pathway connectivity
Confirmed by Linda Buck and Richard Axel in mammals andDrosophila (using genetic tools):
● Each receptor neuron – one receptor type
● No spatial organization of receptor neurons
● Each ORN type projects to the same glomerulus
● Projection neurons (PN) typically sample only one glomerulus
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Projection neurons (moth)
● Projection neurons (PNs) are usually uniglomerular
● A: Three stained PN ● B: PN arborizing in g2
● C&D: PNs arborizing in g1 and g3 at different magnificationX.J. Sun L.P. Tolbert, J.G. Hildebrand, J. Comp.Neurol. 379:2–20 (1997)
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Local neurons (moth)
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. Chr
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. G. H
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Local neurons(LN)
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Local neurons
● Local neurons (LN) have been found in all species
● “local” means no axon to brain structures outside the antennal lob (AL)
● LN are spiking neurons in most insects (moths, honeybees, flies, ...)
● LN are nonspiking in locust● LN can be excitatory or inhibitory● Some LN arborize in specific glomeruli, others
in a few, some everywhere
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Antennal lobe circuitry
● The connectivity shown before is a minimal picture, reality is much more complicated:– There are LNs– LNs can be excitatory or inhibitory– LNs receive inputs from ORN, PN and other LN– LNs project to PN, LN, within and between
glomeruli● The circuits can look very different between
species (whether they are functionally different is an open question), e.g. Locust: 1000 microglomeruli; Moth, Honeybee, Fly, etc: Few (10s) of macroglomeruli
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Mushroom body
● Mushroom body are the secondary olfactory information processing centers
● Many, small Kenyon cells● Much less output cells in
the lobes● Have been implied heavily
in learning and memory
e.g. Dubnau J et al. Disruption of neurotransmission in Drosophila mushroom body blocks retrieval but not acquisition of memory. Nature. 2001 May 24;411(6836):47680
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Locust
● One of the best characterized system due to the work of Gilles Laurents lab
● Let's use this as an initial overview how things may work
● We will later build real models based on these ideas
Antennal lobe Mushroom bodyAntenna
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
PN responses to odor stimulation (locust)
G. L
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. Sto
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. Fr
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. I. A
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:263
297
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Complex slow patterning
Javier PerezOrive, et al., Oscillations and Sparsening of OdorRepresentations in the Mushroom Body, Science 297: 359 (2002)
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
PN responses (locust)
● Different for different odors● Not just tonic elevation of firing rate during odor
pulse– Substructure in the firing – Late or early onset– Some PN are inhibited rather than excited– Some PN react with inhibition first, then rebound
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
LN responses
● Nonspiking in locust● Spiking in other insects (bee, moth)● Are excited by ORN, PN● Not as well studied as PN
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
KC in the mushroom bodies
Javier PerezOrive, et al., Oscillations and Sparsening of OdorRepresentations in the Mushroom Body, Science 297: 359 (2002)
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Local field potential (LFP) (locust)
Similar oscillations have also been observed in most species,in particular mammals/ humans
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Summary
ComplexDynamics
SparseCoding
GainControl?
Learning& Memory
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Dr. Thomas Nowotny, Centre for Computational Neuroscience and Robotics
Next time ...
● I will discuss the connectionist approach to modeling neuronal systems
● We will use it on an interesting example (synchrony in feedforward networks)
● Wednesday: Connectionist modeling of the olfactory system of insects
List of references