Feedforward Learning of Mixture Models

Authors: Matthew Lawlor, Steven W Zucker

NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We provide both proofs of convergence, and a close fit to experimental data on STDP.
Researcher Affiliation Academia Matthew Lawlor Applied Math Yale University New Haven, CT 06520 mflawlor@gmail.com Steven W. Zucker Computer Science Yale University New Haven, CT 06520 zucker@cs.yale.edu 1Now at Google Inc.
Pseudocode No The paper provides mathematical definitions and equations for the update rules but no structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any statement or link indicating the availability of open-source code for the described methodology.
Open Datasets Yes We first model hippocampus data from Mu-ming Poo [3], who applied repeated electrical stimulation to the preand post-synaptic neurons in a pairing protocol within which the relative timing of the two spike chains was varied. After repeated stimulation at a fixed timing offset, the change in synaptic strength (postsynaptic current) was measured. Froemke and Dan also investigated higher order spike chains...Results of their experiment along with the predictions based on our model are presented in figure 3.
Dataset Splits No The paper describes the data used (e.g., hippocampus data, Froemke and Dan's experiments) but does not provide specific train, validation, or test dataset splits.
Hardware Specification No The paper does not mention any specific hardware used for running the experiments.
Software Dependencies No The paper does not provide specific software dependencies or their version numbers.
Experiment Setup Yes Our (four) free variables are the voltage decay, which we set within reasonable biological limits; a bin width, controlling the distance between spiking triplet periods; θ, our sliding voltage threshold; and an overall multiplicative constant. We fit A, τ, θ, and the bin size of integration. ... For the top figure, θ = .65, our bin width was 2ms, and our spike voltage decay rate τ = 8ms. For the right figure θ = .45.