Online Learning Of Neural Computations From Sparse Temporal Feedback
Authors: Lukas Braun, Tim Vogels
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results are separated into a theoretical and an empirical part. In the theoretical part, we derive the partial derivatives (sec. 3.1) that are required for the parameter update rule (eq. 1) and motivate their event-based versions (sec. 3.2 and derivation in appendix E). In the empirical part, we first show that the EDS rule can recover teacher s parameters in LIF and LRF neurons (sec. 3.3). Subsequently, we investigate the influence and functionality of the EDS factor and compare it to the Super Spike surrogate gradient [23] in a lesion study (sec. 3.4). Finally, we test the robustness of the algorithm to temporal noise in the teacher signal (sec. 3.5). |
| Researcher Affiliation | Academia | 1. Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom 2. Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany 4. Institute of Science and Technology Austria, Klosterneuburg, Austria |
| Pseudocode | No | The paper does not include a clearly labeled 'Pseudocode' or 'Algorithm' block, nor does it present structured steps in a code-like format. |
| Open Source Code | Yes | C++ code to replicate all simulations and plots is publicly available1 under GPLv3 license and uses the MPL 2.0 licensed Eigen software library v3.3.7 [40]. 1https://github.com/lukas-braun/learning-neural-computations |
| Open Datasets | No | The paper uses a 'teacher-student paradigm' where a teacher neuron generates target spike times, and input spike trains are homogeneously Poisson distributed. This describes a data generation process rather than the use of a pre-existing publicly available dataset. |
| Dataset Splits | No | The paper describes a continuous online learning process using a teacher-student paradigm, and thus does not refer to traditional dataset splits like training, validation, and test sets with specific percentages or counts. |
| Hardware Specification | Yes | For example, 30 independent simulations of 12,000 minutes of simulated time at 1ms temporal resolution (as in Fig. 3), take less than 30 minutes of real time on a single AMD Ryzen 5950x. |
| Software Dependencies | Yes | C++ code to replicate all simulations and plots is publicly available1 under GPLv3 license and uses the MPL 2.0 licensed Eigen software library v3.3.7 [40]. |
| Experiment Setup | Yes | To speed up learning, we use the Adam optimiser [33] with default parameters (β1 = 0.9, β2 = 0.999, ϵ = 1e 8) and per parameter learning rates, which are scaled according to the range from which target values are sampled (ηw = 35e 6, ητs = 7e 4, ητm = 28e 4 , ηVr = 7e 5 and η w = 8e 5, ηb = 15e 6, ηω = 33e 7, η Vr = 8e 5 η Ir = 8e 5). |