Fast Sampling-Based Inference in Balanced Neuronal Networks
Authors: Guillaume Hennequin, Laurence Aitchison, Mate Lengyel
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We first show analytically and through simulations that the symmetry of the synaptic weight matrix implied by LS yields critically slow mixing when the posterior is high-dimensional. Next, using methods from control theory, we construct and inspect networks that are optimally fast, and hence orders of magnitude faster than LS, while being far more biologically plausible. |
| Researcher Affiliation | Academia | 1Computational & Biological Learning Lab, Dept. of Engineering, University of Cambridge, UK 2Gatsby Computational Neuroscience Unit, University College London, UK |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Our code will be made freely available from GH s personal webpage. |
| Open Datasets | No | The paper uses a toy covariance matrix Σ drawn from an inverse Wishart distribution, whose parameters are specified (N = 200, σ2 0 = 2, σr = 0.2), but this is a generated dataset within the paper, not an externally accessible public dataset with a link or formal citation. |
| Dataset Splits | No | The paper does not explicitly describe training, validation, and test dataset splits for model training or evaluation, as its focus is on analyzing the dynamics of a sampling system rather than training a model on a fixed dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, libraries, or solver names with version numbers. |
| Experiment Setup | Yes | Parameter values: σξ = σ0 = 1. ... parameters N = 200, σ2 0 = 2 and σr = 0.2. ... We initialized S with random, weak and uncorrelated elements (cf. the end of Sec. 4, with ζ = 0.01), and ran the L-BFGS optimization algorithm using the gradient of Eq. 12 to minimize L(S) (with λL2 = 0.1). ... Parameters: N = 200, NI = 100, σξ = 1, τm = 20 ms. ... The first two networks are of size N = 200, while the optimized E/I network has size N +NI = 300. |