Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Real-Time Decoding of an Integrate and Fire Encoder
Authors: Shreya Saxena, Munther Dahleh
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We numerically validate the effect of these parameters on the reconstruction error. We numerically show the utility of the theory developed here. 4 Numerical Simulations |
| Researcher Affiliation | Academia | Department of Electrical Engineering and Computer Sciences Massachusetts Institute of Technology Cambridge, MA 02139 EMAIL |
| Pseudocode | Yes | Recursive Algorithm ti+1 Ati+1gk 1 t0 = 0, and g0 sinc(t si). We denote fti(t) = limk!1 f k gti+1(t) = limk!1 gk ti+1(t). We deο¬ne the operator AT f used in Equation 12 as the following. f( )d sinc (t si) (13) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper describes a synthetic signal generation process ('We simulated signals f(t) of the following form, for t 2 [0, 100], using a stepsize of 10 2.', 'Here, the wk s and dk s were picked uniformly at random...'), but it does not provide concrete access information (link, DOI, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes simulating signals for evaluation but does not provide specific dataset split information (e.g., percentages, sample counts, or predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper only states 'All simulations were performed using MATLAB R2014a.' and does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | Yes | All simulations were performed using MATLAB R2014a. |
| Experiment Setup | Yes | We simulated signals f(t) of the following form, for t 2 [0, 100], using a stepsize of 10 2. Here, [Ξ², K] = [2, 500], and qi = 0.01 8i. A window size of = 3s was used. The thresholds qi were chosen to be constant a priori, but were reduced to satisfy the density constraint wherever necessary. |