Computational neuroscience, medicine

1. COMPUTATIONAL
NEUROSCIENCE
DOMINA PETRIC, MD

2. DESCRIPTIVE MODELS OF THE BRAIN
Neural encoding model: how the neurons
respond to external stimuli.
Neural decoding model: how can we
extract information from the neuron.

3. MECHANISTIC MODELS OF BRAIN CELLS AND
NETWORKS

4. INTERPRETIVE (NORMATIVE) MODELS OF THE BRAIN

5. CONCLUSION
Descriptive
models: WHAT?
Mechanistic
models: HOW?
Interpretive
models: WHY?

6. RECEPTIVE FIELD
• Defined by specific properties of a sensory stimulus that
generate a strong response from the cell.
Examples:
• spot of light turns on at a particular location on the
retina
• bar of light turns on at a particular location and
orientation on the retina

7. NEURON DOCTRINE
• fundamental structural and functional unit of the
brain
• discrete cells
• neurons are not continous with other cells
• information flows from the dendrites to the axon
via the cell body

8. IDEALISED NEURON
• Excitatory post-synaptic potential comes to
dendrites (inputs).
• Output spike (action potential) is created in
body (soma).
• Axons are outputs for next signal.

9. NEURON
https://askabiologist.asu.edu/sites/default/files/resources/articles/neuron_anatomy.jpg

10. NEURON
• It is a leaky bag of charged liquid.
• Has a cell membrane: lipid bilayer.
• Lipid bilayer is impermeable to charged ion species
(Na+, Cl-, K+).
• Ionic channels allow ions to flow in or out.

11. RESTING POTENTIAL OF THE NEURON
• Inside: -70 mV relative to the outside.
• There are more Na+ and Cl- ions outside than
inside.
• There are more K+ and organic anions A- inside
than outside.
• Ionic pump!

12. IONIC PUMP
https://qph.ec.quoracdn.net/main-qimg-a31ee43f9a921b6e607d9df5a7e54011-c

13. IONIC CHANNELS
• Are selective and allow only specific ions to pass through.
Types of ionic channels:
• voltage gated
• chemically gated
• mechanically gated
Depolarization: positive change in voltage.
Hyperpolarisation: negative change in voltage.
Only strong enough depolarization causes a spike or action potential.

14. Action potential: picture by Eric Chudler, UW.
https://faculty.washington.edu/chudler/ap3.gif

15. MYELINATION OF THE AXONS
• Myelin: oligodedrocytes or glial cells wrap axons and
enable fast long-range spike communication.
• Action potential hops from one to another NODE OF
RANVIER (saltatory conduction).

16. SYNAPSE IS CONNECTION BETWEEN TWO NEURONS.
Electrical synapse uses gap
junctions.
Chemical synapse uses
neurotransmitters.
http://ars.els-cdn.com/content https://thesalience.files.wordpress.com/2013/05/synapse-labeled1-300x271.jpg

17. SYNAPSES CAN BE EXCITATORY OR INHIBITORY
Example of excitatory synapse:
• input spike
• glutamate binds to ion channel receptors
• Na+ influx
• depolarization due to excitatory postsynaptic
potential

18. THE SYNAPSE DOCTRINE
• Synapses are the basis for memory and learning.
• Hebbian synaptic plasticity: if neuron A takes part in firing neuron B, than
the synapse from neuron A to neuron B is strengthened.
• Long term potentiation is experimentally observed increase in synaptic
strength that lasts for hours or days (size of excitatory postsynaptic potential
increases for same input over time).
• Long term depression is experimentally observed decrease in synaptic
strength that lasts for hours or days (size of excitatory postsynaptic potential
decreases for same input over time).

19. MAJOR BRAIN REGIONS
• Medulla oblongata: breathing, muscle tone, blood pressure.
• Pons: connected to the cerebellum, involved in sleep and arousal.
• Cerebellum: coordination and timing of voluntary movements, sense of
equilibrium, language, attention...
• Midbrain: eye movements, visual and auditory reflexes.
• Reticular formation: modulates muscle reflexes, breathing and pain
perception, regulates sleep wakefulness and arousal.

20. MAJOR BRAIN REGIONS
• Thalamus: relay station for all sensory informations (except
smell) to the cortex, regulates sleep and wakefulness.
• Hypothalamus: regulates basic needs (Fighting, Fleeing, Feeding,
Mating).
• Cerebrum (cerebral cortex, basal ganglia, hippocampus,
amygdala): perception and motor control, cognitive functions,
emotions, memory, learning...

21. LAYERS OF CORTEX
1. Input from higher cortical areas
2. Output to higher cortical areas
3. Output to higher cortical areas
4. Input from subcortical regions
5. Output to subcortical regions
6. Output to subcortical regions

22. COMPUTING PARADIGM
Brain
massively parallel
computation
adaptive connectivity
Digital computers
sequential
information
processing via CPUs
with fixed connectivity

23. NEURAL CODE MEASUREMENT
fMRI, EEG for multiple neural outputs measurement
multielectrode arrays and calcium imaging for single neural
output measurement (externally recorded activity)
looking inside single cell

24. NEURAL CODE ENCODING VS. DECODING
Encoding
How does a stimulus
cause a pattern of
responses?
Decoding
What do these
responses tell us
about the stimulus?

25. NEURAL ENCODING

26. LITERATURE
• https://www.coursera.org/learn/computational-
neuroscience/lecture: Week 1-8, Rajesh P.N. Rao,
Adrienne Fairhall, University of Washington, Seattle, USA
• Askabiologist.asu.edu
• http://ars.els-cdn.com