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| | A group
can comprise of 1-2 persons enrolled in the class. Each group will
pick a particular "mini-research" question pertinent to this course
and investigate the question using simulations and/or analysis. Each
group will present their results to class on Monday, June 4 (10:30-12:20pm),
and will submit a short printed report (10-15 pages including figures
and references) via email to both Raj and Adrienne before midnight June 4.
Presentation details: You may email Raj your slides by 8am on the day of
the presentation and use his laptop for your presentation or bring your
own laptop if your presentation requires special software or uses demos.
In the latter case, you should test your laptop with the projection system a day or two before to make sure the projector recognizes your laptop.
Potential Topics for Group Projects:
- Simple models of spiking neurons (see papers by Izhikevich under Papers).
- Neural networks for principal component analysis, sparse coding, ICA, etc.
- Information theoretic analysis of spiking data (contact Adrienne for suggestions)
- Neural implementation of Bayesian models
- Synchronous coding and multiplexing information using spikes and spike-timing dependent plasticity
- Dendritic computation
- Computational role of backpropagating spikes in cortical neurons
- Computation using dynamic synapses and spikes
- Cortical feedback and hierarchical computation
- Using spike-timing based plasticity for predictive coding
- Models of nonclassical receptive fields in the visual cortex
- Relationship between firing rate and membrane potential of a neuron
- Effects of noise in neuronal networks and "stochastic resonance"
- Learning spatiotemporal filters from natural movies
- Learning spectrotemporal filters from natural sounds and speech
- Models of reinforcement learning and applications
- Unsupervised learning and its relation to statistical machine learning
- Supervised learning and its application in robotics, BCI, etc.
- Non-traditional learning in neurons (adapting channel densities, dendritic structure adaption, adding new neurons in a network, etc.)
- Evolutionary methods for learning the structure of networks
- Decoding and classification of brain-derived signals (spikes, EEG, etc.) for applications such as Brain-Computer Interfaces (BCIs)
Contact Adrienne and Raj if you are interested in pursuing one of these
topics. Projects on other topics related to the course or to your research interests are also welcome.
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