1Neural Networks B 2009

Neural Networks Bhttp://www.igi.tugraz.at/lehre/NNB/SS09/

Lecture 1

Wolfgang Maasshttp://www.igi.tugraz.at/maass/

Institut für Grundlagen der Informationsverarbeitung

Technische Universität Graz

Institute for Theoretical Computer Science http://www.igi.tugraz.at/maass/

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Which scientific disciplines are involved ?

• Computational Neuroscience• Cognitive Neuroscience• Molecular Biology• Neuroinformatics• Neuromorphic Engineering

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Some linkson http://www.igi.tugraz.at/lehre/NNB/SS09/

• Tutorials and other teaching material  www.sinauer.com/cogneuro http://www.science.smith.edu/departments/NeuroSci/courses/bio330/pedsites.html  http://psych.hanover.edu/krantz/neurotut.html  http://en.wikipedia.org/wiki/Computational_neuroscience

http://www.scholarpedia.org/article/Encyclopedia_of_computational_neuroscience  http://www.scholarpedia.org/article/Encyclopedia_of_cognitive_neuroscience  http://en.wikipedia.org/wiki/Cognitive_neuroscience  http://bluebrain.epfl.ch/

Conferences, Researchers, Resources  http://home.earthlink.net/~perlewitz/

Technological Applications and Related Research Projects  http://facets.kip.uni-heidelberg.de/  http://www.seco-project.eu/  http://reservoir-computing.org/  http://www.eventmakeronline.com/dso/View/index.asp?MeetingID=561  http://www.outlookseries.com/news/Science/3589.htm  http://siliconretina.ini.uzh.ch/wiki/index.php  http://www.smart-systems.at/rd/rd_smart_sensors_de.html  http://www.ini.uzh.ch/

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Difference of course content in comparison with last years

• Introduction to the PCSIM simulator of biological networks of neurons (with Python-interface)

• Recent research results on models for cortical micorcircuits

• Inclusion of results, models, and problems of cognitive neuroscience (memory, top-level-control)

• Discussion of work in related EU-research projects (in which students could become involved)

• Discussion of results and open problems in neuromorphic engineering

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Why is it so difficult to understand how the brain works ?

One reason: Different spatial scales are relevant, and in general mechanisms on different scales interact for each information

processing task

(whereas for a digital computer an algorithm designer does not have to look to levels below that of a logic gate)

• system level (the whole brain)• brain areas• microcircuits • neurons and synapses• molecular level (including gene regulation)

6Neural Networks B 2009Another reason: Time (on several time scales) plays an essential role for information processing in the brain, in quite

different ways than in computers

Various time scales are relevant, and different processes are not only superimposed on different spatial scales, but also on different time scales :

• Weeks/months (replacement of all active neuron components)

• A day (consolidation of changes of synaptic weights resulting from learning)

• Minutes (initiation of synaptic weight changes through training)

• Seconds (behavioural time scale, delay of „rewards“, fMRI)

• 150 – 500 ms (time for completing a fast computation in the brain, also time-scale of spatio-temporal patterns that encode memory items)

• 100s of ms (short term dynamics of neurons and synapses)

• 10s of ms (integration time for a neuron, learning window for spike-timing-dependent plasticity: STDP)

• 1- a few ms (spike, relevance of spike-order for STDP)

Analogously as for space, it is also not clear which time scale is „the right one“ for analyzing information processing algorithms of the brain

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A third reason: It is not clear to what extent there exists a division of labor for information processing tasks of the brain

Older models for information processing in the brain had assumed that the brain • first builds a model of the external world (e.g. of a visual scence) in the sensory cortex • then draws conclusions from that in the association cortices („inference“)• then initiates motor outputs on the basis of these conclusions in the motor cortex

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More recent experimental data suggest a quite different perspective

• The brain does not care to build a model of the external world (and probably could not even do that for a visual scene)

• Instead it aims at satisfying certain goals, and actively searches the sensory inputs for hints how these goals could be achieved in the current environment

• Sensory processing and motor processing cannot be separated. Rather behaviours are encoded as whole entities by the brain (integrating sensory and motor components).

• Cortical areas and circuits do not have genetically assigned processing tasks, rather their computational function emerges on the basis of genetically encoded learning algorithms and the statistics of their environment.

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Yet another reason for the difficulty in understanding how the brain works

• „Innenschau“

• All kinds of heuristic models that we have from everyday life, old ideas, naive psychology, etc

10Neural Networks B 2009 Cortical microcircuits of neurons as are the primary information processing devices in the brain

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The output of neurons consists of „spikes“ in continuous time (without a „clock“)

Each spike of a presynaptic neuron causes a temporary change of the postsynaptic membrane potential (EPSPs and IPSPs)

The EPSPs and IPSPs are added linearly, and the neurons emits a spike whenever the postsynaptic membrane reaches the „firing threshold“

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A protocol of a neural computation („spike raster“)

Shown here are the times when spikes are emitted by neurons in the primary visual cortex of a cat (when it was shown the letters A, D)

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Both the timing of spikes and the „firing rates“ of neurons change from trial to trial

Shown are spike rasters for 5 trials with each of the two stimuli A and D.

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Program of this course for the next few weeks

• Visualization of a model for a small network of spiking neurons

• Mathematical models for spiking neurons

• Why are neurons and synapses so difficult to model ? (a brief look at their molecular components)

• Methods for studying how the brain works