Download Modeling Synaptic Plasticity

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

Neuropsychopharmacology wikipedia, lookup

Synaptic gating wikipedia, lookup

Channelrhodopsin wikipedia, lookup

Nervous system network models wikipedia, lookup

Pre-Bötzinger complex wikipedia, lookup

Feature detection (nervous system) wikipedia, lookup

Electrophysiology wikipedia, lookup

Optogenetics wikipedia, lookup

Biological neuron model wikipedia, lookup

Stimulus (physiology) wikipedia, lookup

Premovement neuronal activity wikipedia, lookup

Spike-and-wave wikipedia, lookup

Neural coding wikipedia, lookup

Chemical synapse wikipedia, lookup

Molecular neuroscience wikipedia, lookup

Neuroanatomy wikipedia, lookup

End-plate potential wikipedia, lookup

Nonsynaptic plasticity wikipedia, lookup

Central pattern generator wikipedia, lookup

Neural oscillation wikipedia, lookup

Axon wikipedia, lookup

Mirror neuron wikipedia, lookup

Multielectrode array wikipedia, lookup

Caridoid escape reaction wikipedia, lookup

Clinical neurochemistry wikipedia, lookup

Development of the nervous system wikipedia, lookup

Metastability in the brain wikipedia, lookup

Neurotransmitter wikipedia, lookup

Artificial general intelligence wikipedia, lookup

Synaptogenesis wikipedia, lookup

Convolutional neural network wikipedia, lookup

Environmental enrichment wikipedia, lookup

Apical dendrite wikipedia, lookup

Neuroplasticity wikipedia, lookup

Neural modeling fields wikipedia, lookup

NMDA receptor wikipedia, lookup

Neuromuscular junction wikipedia, lookup

Activity-dependent plasticity wikipedia, lookup

Holonomic brain theory wikipedia, lookup

Neural engineering wikipedia, lookup

Types of artificial neural networks wikipedia, lookup

Neuroeconomics wikipedia, lookup

Connectome wikipedia, lookup

Memory consolidation wikipedia, lookup

Long-term depression wikipedia, lookup

Efficient coding hypothesis wikipedia, lookup

Synaptic noise wikipedia, lookup

Dendritic spine wikipedia, lookup

Long-term potentiation wikipedia, lookup

Transcript
Department of Statistics
STATISTICS COLLOQUIUM
NICOLAS BRUNEL
Departments of Statistics and Neurobiology
The University of Chicago
Modeling Synaptic Plasticity
MONDAY, November 25, 2013, at 4:00 PM
133 Eckhart Hall, 5734 S. University Avenue
Refreshments following the seminar in Eckhart 110
ABSTRACT
Synapses are the structures through which neurons communicate, and the loci of information
storage in neural circuits. Synapses store information (‘learn’) thanks to synaptic plasticity:
the efficacy of the communication between the two neurons connected by the synapse can
change, as a function of the history of the activity of these two neurons. Many experiments
have documented the phenomenology of synaptic plasticity in the last four decades, but the
precise ‘learning rule’ used by synapses and the mechanisms of plasticity still elude us.
In this talk, I will first review the relevant experimental data. I will then present a model
of synaptic plasticity which describes the temporal evolution of two variables: the calcium
concentration in the post-synaptic spine, which is driven by the activity of pre and postsynaptic neurons, and the synaptic efficacy, which is driven by the calcium concentration
variable. This model is simple enough so that it can be studied analytically, for deterministic
as well as stochastic activity patterns of the two neurons. I will show that it reproduces
naturally a large amount of experimental data in various preparations, and provides a mechanistic understanding of how various activity patterns provoke specific synaptic changes.
For further information and inquiries about building access for persons with disabilities, please contact
Kirsten Wellman at 773.702.8333 or send her an email at [email protected] If you wish to subscribe to our email list, please visit the following website: https://lists.uchicago.edu/web/arc/statseminars.