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
simulation of the behavior of the single neuron on the computer
simulation of the network of neurons

4. Interpretive (normative) models of the brain
how the brain circuits operate
computational principles underlining the brain circuits

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. Excitatory post-synaptic potential comes to dendrites (inputs).
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

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

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.

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.

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. 4.
Input from subcortical regions 5.
Output to subcortical regions 6.

22. sequential information processing via CPUs with fixed connectivity
Computing paradigm
Brain
Digital computers
massively parallel computation
adaptive connectivity
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
How does a stimulus cause a pattern of responses?
What do these responses tell us about the stimulus?

25. Neural encoding
Linear filter
Input/output function

26. Literature
neuroscience/lecture: Week 1-8, Rajesh P.N. Rao, Adrienne Fairhall, University of Washington, Seattle, USA
Askabiologist.asu.edu