Neural transformations on spike timing information (Tripp and Eliasmith 2007)

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" ... Here we employ computational methods to show that an ensemble of neurons firing at a constant mean rate can induce arbitrarily chosen temporal current patterns in postsynaptic cells. ..."
1 . Tripp B, Eliasmith C (2007) Neural populations can induce reliable postsynaptic currents without observable spike rate changes or precise spike timing. Cereb Cortex 17:1830-40 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Activity Patterns; Temporal Pattern Generation;
Implementer(s): Tripp, Bryan [bryan.tripp at]; Eliasmith, Chris [celiasmith at];
- DONE refactor izhikevichNetwork in/outputs  
- DONE initialize ISI to empty matrix in synthetic.m
- DONE double-check power tests and RERUN
- DONE check f equations in anovaPowerExperiment
- DONE update data file names 
- DONE extract method in exp_COV exp_COVNS
- DONE clean up COV calculations
- DONE (tried) change decode to work on spikes rather than currents (faster)
- DONE standardize signal file name formats
- DONE upper case PDF in randpdf
- DONE prepend 'gen' to coarseCorrelated, correlated, synthetic, uncorrelated
- NO change jitter.m time resolution to .0002s (from .001s)
- DONE optimize jitter for common case (i.e. only uncorrelated)
- DONE check validity of more-spikes loop in uncorrelated.m
- DONE fix testF() in anovaTrials.m
- DONE review figure scripts

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