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[12:00] IMBB Seminar
- Description:
- Johannes LUTHMAN
Title: "Neural coding in the cerebellum".
Tuesday, December 20th 2011 @ 12:00
"A. Payatakes" Seminar Room [FORTH's bldg]
Host: Y. Poirazi
Abstract:
The cerebellum is an ancient part of the brain that is estimated to contain more than half of all neurons in the human central nervous system. It has long been known to be involved in motor control, but has more recently been implicated in non-motor functions as well, with autism and schizophrenia being examples of disorders where cerebellar dysfunction may play a role.
A highly stereotypic circuitry is repeated in the millions to make up the cortex of the cerebellum, with the Purkinje cell as the central neuron. A Purkinje cell receives approximately 150,000 parallel fibre inputs with sensory information. When parallel fibre information reaches Purkinje cell synapses simultaneously with climbing fibre input - thought to constitute an error signal - the synapses respond with a long-term depression (LTD) of synaptic strengths. This LTD is thought to underlie much of the motor learning the cerebellum performs.
In my PhD programme, I have created a network model of biophysically realistic Purkinje cells and their output neurons in the deep cerebellar nuclei (DCN). The DCN constitute the output from the cerebellum as a whole, meaning that any result of learning that takes place in the Purkinje cells is processed by DCN neurons before providing instructions for the remainder of the nervous system.
I have evaluated how recognition of previously stored patterns in Purkinje cells is read out by DCN neurons and will in this talk show examples of the pattern recognition properties in this modelled network, including what features are best to signal pattern recognition from the DCN, and how the anatomical arrangement of the circuitry can function to improve signal discrimination.
I will also discuss findings of studies where I have modelled how the synapses between Purkinje cells and DCN neurons respond to differently regular/irregular inputs and how the synapses allow the network to translate a code based on differences in regularity of intervals between spikes to one that is based on rate of spiking.
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