BrainStorm 1 : Calcium Memory Sensors

How can we identify the group of neurons that encode a particular thought?  

I don’t want to simply see correlations of in activity of a few scattered neurons with a given thought, but identify the entire neuronal ensemble.  Which neurons are active at a precise moment in a task?  How are they wired together? Which are the drivers of activity?

Existing technology is inadequate to identify the entire neural ensemble that encodes a thought. Immediate early gene expression  patterns have not been shown to be precisely correlated with brain activity, and have a temporal resolution on the order of minutes. Genetically encoded calcium sensors (GECIs) have the necessary temporal and spatial resolution, but their response is nearly as fleeting as a thought, making it impossible to simultaneously record from networks of thousands of possible participants with current microscopy techniques.

Photoswitchable, genetically-encoded calcium memory sensorscan identify all the neurons in a large network that are active during user-specified, aribitrarly brief or long time periods.  I will propose four potential strategies for construction of these sensors, and detail practical considerations for sensor design, screening and application.


To be updated very very soon!

4 responses

9 01 2009
BrainStorm 1 : The Calcium Memory Sensor « Brain Windows

[…] BrainStorm 1, I will outline a technology, photoswitchable genetically-encoded calcium memory sensors, that can […]

11 05 2010

what happened to the four potential strategies???

9 08 2010
Andrew Hires

Hahaha…. well… I haven’t had time to write them up in sufficiently detailed format.

The most elegant is :

1) A single-FP GECI with a photoactivatible/convertable which can photoconvert only in the calcium bound state. Then you can say shine a photoconverting light on the brain only in the 50 ms following your sensory input and then post-hoc map the neurons that participated meaningfully in that sensory representation. Extrapolate to any event-locked illumination period.

2) FRET to a photoactivatible/convertable protein linked via Calcium binding protein. Unfortunately, not many PA-FPs that convert via frequencies usually emitted by normal FPs. Most are UV converters.

3) FRET to an easily bleached FP, like mStrawberry. The negative signal kinda sucks, but this one might take the least amount of engineering to get a proof of concept.

4) Don’t remember at the moment!

There are some fundamentally different strategies I’ve heard lately that have some merit, but I can’t share 😦

9 08 2010
Andrew Hires

Of course all of these have issues with background conversion rates.

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