Choose one task from below, and I will provide you with the details on an individual basis (or in groups of those who are interested in the same task), plus the working code. Please choose your preference by Friday, 3/30/07.
Codebase | Environment | Replicate previous results | Modifications | Experiments |
Coupled integrate-and-fire neuron in XPPAUT (from
homework 1
[Use your own code] |
XPPAUT | Get both synchronization and desynchronization from excitatory (and inhibitory) coupling, by adjusting parameters (mostly the PSP time constant). | Revise equation to keep firing rate constant (or at least within a range), regardless of PSP amplitude. | Do a study where you change the simulation parameter systematically and record and analyze the results. Does the behavior change smoothly or abruptly? |
Choe's Thalamocortical circuit model in XPPAUT:
[Download: thalcor.tar.gz] [Dir] |
XPPAUT: use your own code from homework 1 | Run all experiments in Choe (2004) | Instead of exponentially decaying PSP, use alpha function. | Run the same simulations with the revised model, adjusting parameters to get the same qualitative behavior. |
Sensory-invariance-driven action code: [Download: sida-nat.tar.gz] [Dir] |
Octave | Run the basic simulation for autonomous learning of internal state, on synthetic and natural images. | Learn RF using a random policy and Hebbian weight adaptation. After RF learning is complete, learn R(s,a). | Run the same simulations with the revised model. |
Simple neuroevolution code for feedforward neural networks:
[Download: ga.m] |
Octave | Run the basic simulation for learning boolean functions. | Modify network to have context layer (hidden layer feeding back to itself). | Experiment with temporal sequence learning (supervised). |
Topographica
[Download: download page] |
Topographica | Run two tutorial simulations: Tutorial page | Add integrate-and-fire neuron types to the code base. | Extend the coupled-neuron experiment and get synchronization/desynchronization behavior |
Sarma and Choe's orientation energy code for contour saliency
detection and associated code
[TBA] |
Octave | Run code on new images and compare to your own perceptual experience. | Derive and implement a neural network to learn response threshold. Use a square-root activation function at some stage during the process. | Train and test using the human data set. |
Your own codebase, relating to cortical networks | ? | ? | Discuss with the instructor | Discuss with the instructor |