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Intro PattRecog Simulation Optimization Conclusions

COMPUTATIONAL INTELLIGENCE FOR NEUROSCIENCE

Concha Bielza, Pedro Larrañaga

Departamento de Inteligencia Artificial Universidad Politécnica de Madrid

Discovery Science and Algorithmic Learning Theory - 2009 Porto, October 3, 2009

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

Outline

1 Introduction

2 Pattern recognition Neuroimaging -omics Clustering of neurons Classification of neurons

3 Computer simulation of dendritic morphology Dendritic morphology Simulation models of dendritic morphology Comparing virtual and real cells

4 Parameter and structural optimization in neuronal and brain models Compartmental model Brain networks

5 Conclusions Challenging More material

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

Outline

1 Introduction

2 Pattern recognition Neuroimaging -omics Clustering of neurons Classification of neurons

3 Computer simulation of dendritic morphology Dendritic morphology Simulation models of dendritic morphology Comparing virtual and real cells

4 Parameter and structural optimization in neuronal and brain models Compartmental model Brain networks

5 Conclusions Challenging More material

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

Introduction

COMPUTATIONAL INTELLIGENCE

FOR NEUROSCIENCE

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

Introduction

COMPUTATIONAL INTELLIGENCE

FOR NEUROSCIENCE

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

Introduction

COMPUTATIONAL INTELLIGENCE

FOR NEUROSCIENCE

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

Neuroscience

Neuroscience, the science of the brain Goal: understand the relationship between the physical brain and the functional mind

—how we perceive, move, think, and remember

A principle: all behavior is an expression of neural activity

Involves answers to scientific fundamental questions:

How does the brain develop? Are specific mental processes located in specific brain regions? How do nerve cells in the brain communicate with one another? How do different patterns of interconnections give rise to different perceptions and motor acts? How is communication between neurons modified by experience? How is it altered by diseases or drugs?

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

The human brain

Brain

real sagittal view

The most complex organ of our central nervous system and the most complex biological structure known

Weight = 1.5kg, width = 140 mm, length = 167mm, height = 93mm

Consumes 20 % of total body oxygen

Stops growing at age 18

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

The human brain

Brain regions

regions lobes

Neuroanatomists distinguish main regions. Each has a complex internal structure

Left-right hemispheres covered by a thin layer of gray matter known as the cerebral cortex

Cerebral cortex, the most recently evolved region of the vertebrate brain, divided into four areas

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

The human brain

Brain areas: cerebral cortex

Some areas, such as the cortex and cerebellum, consist of layers, folded to fit within the available space

In mammals, neocortex: complex 6-layered structure, greatly enlarged in primates, especially frontal lobe part (in humans, extreme enlargement). Concerned with the most evolved human behavior

Outermost=layer 1, innermost=layer 6

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

The human brain

Functions of each area (as currently understood) Medulla: sensory and motor functions

Cerebellum: fine motor coordination and body movement, posture, and balance (not needed, but it makes actions hesitant and clumsy)

Thalamus: acts as a switching center for nerve messages

Hypothalamus: control of sleep/wake cycles, eating and drinking, hormone

Hippocampus: involved in memory storage

Olfactory bulb: processes olfactory sensory signals

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

The human brain

Functions of cortex (as currently understood)

Occipital lobe: visual information

Temporal lobe: auditory signals, processing language, meaning of words

Parietal lobe: touch, taste, pressure, pain, temperature

Frontal lobe: motor activity, integration of muscle activity, speech, thought

Remaining parts associated with higher thought processes, planning, memory, personality and other human activities

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

The human brain

Brain at microscopic level

Composed of neurons, blood vessels, glial cells (supporting cells of the CNS)

Neuron is the basic structural and functional unit of the nervous system –neuron doctrine– (S. Ramón y Cajal, late 19th century)

Just 4 microns thick→ could fit 30,000 neurons on the head of a pin ∼100,000 million= 1011 interconnected neurons

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

The neuron

3 parts of a neuron

1 Dendrites receive info from another cell and transmit the message to soma 2 Cell body (or soma) contains the nucleus, mitochondria and other organelles

typical of eukaryotic cells 3 Axon conducts messages away to the next neuron via a specialized structure

–synapse– Axons fill most of the space in the brain→ >150,000 km in the human brain!! Each neuron connected to 1,000 neighboring neurons

10,000 synaptic connections each→ 3 · 1014 synapses (adult) and 1015 (child)

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

The neuron

Variable in size and shape Neurons are more varied than cells in any other part of the body

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

The neuron

Different shapes for different functions?

Pyramidal activate other neurons, sometimes making connections far away→ long axons to carry the electrical impulses

Interneurons (e.g., chandelier, double bouquet, spiny stellate, basket cells) create network interactions between neighboring neurons→ short dendrites and axons

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

The neuron

Synapse: junction between 2 neurons

Messages travel within the neuron as electrochemical impulses called action potentials, lasting less than a thousandth of a second at 1-100 m/s

Action potential arrives at a synapse and causes a chemical called a neurotransmitter, stored in small synaptic vesicles, to be released

Neurotransmitter binds to receptor molecules in the membrane of the target cell

Excitatory receptors (↑ rate of action potentials in the target cell) vs. inhibitory Parkinson’s disease has a deficiency of the neurotransmitter dopamine. Alcohol weakens connections between neurons. Drugs such as caffeine, nicotine, heroin, cocaine, Prozac, act on some particular neurotransmitters

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

The neuron

Synapse Your brain generates nearly 25 watts of power while you’re awake, which is enough to light up a light bulb

—-See video

C. Bielza, P. Larrañaga Computational Intelligence for Neuroscience

Intro PattRecog Simulation Optimization Conclusions

The neuron

Special parts involved in synapses: spines

Spines are found on the dendrites of most principal neurons in the brain

Synapses typically formed on dendritic spines (discovered by Ramón y Cajal)

Many different shapes too. Spines with strong synaptic contacts typically have a large spine head

Dendritic spines are very “plastic”, i.e. change significantly in shape, volume, and number in small periods

Spine plasticity is implicated in motivation, learning, and memory