Computational neuroscience Domina Petric, MD

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.

Mechanistic models of brain cells and networks simulation of the behavior of the single neuron on the computer simulation of the network of neurons

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

conclusion Descriptive models: WHAT? Mechanistic models: HOW? Interpretive models: WHY?

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

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

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.

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

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.

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!

Ionic pump https://qph.ec.quoracdn.net/main-qimg-a31ee43f9a921b6e607d9df5a7e54011-c

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.

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

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).

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

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

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).

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.

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...

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.

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

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

Neural code encoding vs. decoding How does a stimulus cause a pattern of responses? What do these responses tell us about the stimulus?

Neural encoding Linear filter Input/output function

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