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Outline
Why model neural networks? A brief look at the neuron. A look at some current works. Adding an evolutionary strategy.
Modeling Neural Networks Paul Nuytten CPSC 607 Outline Why model - - PowerPoint PPT Presentation
Modeling Neural Networks Paul Nuytten CPSC 607 Outline Why model - - PowerPoint PPT Presentation
Modeling Neural Networks Paul Nuytten CPSC 607 Outline Why model neural networks? A brief look at the neuron. A look at some current works. Adding an evolutionary strategy. Why Model Neural Networks? The nervous system is a
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Why Model Neural Networks?
The nervous system is a very complex system with many
hidden properties.
Many experiments cannot be performed in vivo without
destroying the specimen; leaving many questions unanswered.
When looking at a model many of the redundant
structures and processes can be removed leaving a more focused picture.
The nervous system is a very efficient and massively
parallel computational device. Models may capture this property to solve a certain class of problems.
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The Neuron
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The Neuron
The neuron shares many of the same
components as many other types of cells.
It’s the unique structures that make the
neuron a powerful communication and computational device.
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The Neuron
The unique
components are
The dendrites The axon The synapses
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The Dendrites
The dendrites are short strands that
protrude from the cell body or the soma.
The dendrites are very receptive to
connections from other neurons.
The dendrites carry signals from the
synapses to the soma.
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The Axon
The axon is a long
extension from the soma.
Each neuron only has
- ne axon.
The axon is
myelinated if it is insulated with Schwann cells.
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The Axon
If the axon is
myelinated the action potential will travel much faster.
The axon carries
action potentials from the soma to the synapses.
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The Synapse
The synapses are the connections made
by an axon to another neuron.
When an action potential arrives at a
synapse from the postsynaptic cell, neurotransmitter is released into the synaptic cleft.
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The Synapse
The neurotransmitter will interact with ion
channels on the membrane of the postsynaptic cell causing them to open letting some ions into the cell while letting other ions escape.
A synapse is call excitatory if it raises the local
membrane potential of the post synaptic cell.
Inhibitory if the potential is lowered.
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What level should be modeled?
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Some Current Works
NEURON GENISIS Neural Swarm Evolutionary Artificial Neural Networks
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NEURON
NEURON is a simulation environment for
neurons and neural networks.
The NEURON simulation allows a user to
focus on the biological and biophysical aspects of a neurological system.
Great tool for biologists.
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NEURON
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GENISIS
GEneral NEural SImulation System Provides a simulation environment for
biologically realistic models.
Very similar to NEURON. Allows for parallel processing.
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Neural Swarm
Uses the concepts of swarm intelligence when
creating neurological models.
Still in its infancy. Instead of designing the network and defining
processes in mathematical terms, the network and processes are allowed to emerge from the simple interactions within the system.
The results are then compared to biologically
- bserved results.
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Neural Swarm
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Evolutionary Artificial Neural Networks
Focuses more on the computational aspect of
artificial neural networks (using them to solve problems).
Uses outgrowth and pruning rules to grow a
neural network.
Spontaneous neural activity also contributes to
the development of the network.
What is nice about this simulation is its ability to
apply a genetic algorithm to the above rules and the network’s morphology to specialize the network to a given problem.
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Evolutionary Artificial Neural Networks
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Evolutionary Artificial Neural Networks
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Conclusion
When creating a simulation it is important
to identify the level at which to model.
It is also important to identify the target
audience and intended use of your simulation.
Start simple and gradually add complexity. Collaborate.
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References
- F. Bloom, C. Nelson, A. Lazerson. Brain, Mind and Behavior Third
- Edition. Worth Publishers, USA, 2001.
- N. Campbell. Biology Fourth Edition, pages 993-1009. The
Benjamins/Cummings Publishing Company, Inc., Menlo Park, California, 1996.
- R. Rojas. Neural Networks A Systematic Introduction. Springer-
Verlag, Berlin, 1996.
Rust A.G., Adams R., Schilstra M. and Bolouri H. Evolving computational neural systems using synthetic developmental
- mechanisms. 2003.
http://www.rwc.uc.edu/koehler/biophys/4d.html
http://www.neuron.yale.edu/neuron/
http://www.genesis-sim.org/GENESIS/
http://strc.herts.ac.uk/bio/alistairr/neural_interests.html