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Computational Tools to Analyze Neural Activity
Summary: Parvez Ahammad’s research interests are in signal processing, computer vision, and statistical learning with a specific emphasis on applications related to high-throughput biology and computational neuroscience.
One of the fundamental challenges in neuroscience is linking specific behaviors with specific neural activity in specific circuits. However, the process of identifying the components of the circuitry and obtaining meaningful statistical inferences about the relationships between various experimental parameters and measured variables is both time consuming and labor intensive.
As a Janelia junior fellow, I plan to improve current methods as well as develop new algorithms to extract relevant data from in vivo recordings of neuronal activity and relate that information to the animal's behavior. Specifically, I plan to
- Develop tools to analyze large-scale physiological datasets, in particular:
- Methods for spike detection and neuron identification methods for extra-cellular multi-electrode recordings.
- Methods for identifying multiple targets of interest (e.g. cells or parts of cells) in calcium activity imaging data.
- Develop mathematical tools for analyze multi-resolution brain networks.
- Develop an end-to-end multivariate Bayesian framework that incorporates the novel algorithmic modules to link the activity of populations of neurons to both continuous and discrete behavioral variables.
CONTACT
As of June 28, 2011
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