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| - | The first application is | + | The first application is a toggle switch, which holds an output stable in one of two states. This is modeled along the lines of a simple RS latch from digital logic. There is simultaneous, continuous activation of two clusters of neurons by pacemaker cells, where the two clusters cross-repress/mutually inhibit each other. The simultaneous activation by the pacemaker cells and the repression activated in one cluster of neurons at a time, allows the other cluster to be held in a stable state. This allows us to build a memory element similar to that proposed by Gardner et al. that is orders of magnitude faster. |
In the second application we are attempting to teach networks of neurons to recognize inputs in the form of neurotransmitters. By “teach,” we mean using genetic feedback mechanisms to strengthen the synapses that lead to the “correct” answer and weaken all others, thus making our network responsive only to the inputs we want it to recognize. | In the second application we are attempting to teach networks of neurons to recognize inputs in the form of neurotransmitters. By “teach,” we mean using genetic feedback mechanisms to strengthen the synapses that lead to the “correct” answer and weaken all others, thus making our network responsive only to the inputs we want it to recognize. | ||
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== Project Details== | == Project Details== | ||
Revision as of 02:33, 12 August 2008
PRINCETON IGEM 2008
| Home | Project Overview | Project Details | Experiments | Results | Notebook |
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| Parts Submitted to the Registry | Modeling | The Team | Gallery |
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Contents |
Overall project
The goal of the Princeton iGEM team is to utilize the immense capabilities of neurons and in particular of neuronal networks – in terms of efficiency, speed of transmission of information and output, physical size, robustness, reliability, and programmable versatility – by taking two very different approaches to neuronal networks using gene-regulatory circuits.
The first application is a toggle switch, which holds an output stable in one of two states. This is modeled along the lines of a simple RS latch from digital logic. There is simultaneous, continuous activation of two clusters of neurons by pacemaker cells, where the two clusters cross-repress/mutually inhibit each other. The simultaneous activation by the pacemaker cells and the repression activated in one cluster of neurons at a time, allows the other cluster to be held in a stable state. This allows us to build a memory element similar to that proposed by Gardner et al. that is orders of magnitude faster.
In the second application we are attempting to teach networks of neurons to recognize inputs in the form of neurotransmitters. By “teach,” we mean using genetic feedback mechanisms to strengthen the synapses that lead to the “correct” answer and weaken all others, thus making our network responsive only to the inputs we want it to recognize.
Project Details
Plasmid Designs
Experiments
Princeton iGEM Utility Belt
Will you be wanting the Batpod, sir?
