Efficient Inference of Flexible Interaction in Spiking-neuron Networks
Authors: Feng Zhou, Yixuan Zhang, Jun Zhu
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the accuracy and efficiency performance of our algorithm on synthetic and real data. For real neural recordings, we show our algorithm can estimate the temporal dynamics of interaction and reveal the interpretable functional connectivity underlying neural spike trains. |
| Researcher Affiliation | Collaboration | Dept. of Comp. Sci. & Tech., BNRist Center, THU-Bosch Joint ML Center, Tsinghua University Data Science Institute, University of Technology Sydney {zhoufeng6288, dcszj}@tsinghua.edu.cn, yixuan.zhang@uts.edu.au |
| Pseudocode | Yes | Algorithm 1: EM inference for SNMHP |
| Open Source Code | Yes | The implementation of our model is publicly available at https://github.com/zhoufeng6288/SNMHawkes Beta. |
| Open Datasets | Yes | Spike Train Data (Blanche, 2005; Apostolopoulou et al., 2019) Several multi-channel silicon electrode arrays are designed to record simultaneously spontaneous neural activity of multiple isolated single units in anesthetized paralyzed cat primary visual cortex areas 17. The spike train dataset contains spike times of 25 simultaneously recorded neurons. and Tim Blanche. The neural data was recorded by Tim Blanche in the laboratory of Nicholas Swindale, University of British Columbia, and downloaded from the NSF-funded CRCNS Data Sharing website., 2005. |
| Dataset Splits | No | We extract the spike times in the time window [0, 300] (time unit: 100ms, the same applies to the following) as the training data (Fig. 2a) and [300, 600] as the test data (App. IV). |
| Hardware Specification | No | The paper mentions 'GPU/DGX Acceleration' in the acknowledgments but does not provide specific details about the hardware (e.g., CPU/GPU models, memory, or processor types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | All hyperparameters are fine tuned to obtain the maximum test Log L: the scaled (shifted) Beta distribution Beta( α = 50, β = 50, shift = 5) with support [0, Tφ = 10] is designed as the basis function; the number of quadrature nodes is set to 1000 and EM iterations to 100. More experimental details, e.g., hyperparameters, are given in the App. IV. and For the synthetic data...the optimal number of basis functions is 4, which are chosen as the ground truth: φ{1,2,3,4} = Beta( α = 50, β = 50, scale = 6, shift = { 2, 1, 0, 1}). By cross validation, the hyperparameter α is chosen to be 0.05. As shown in the experiment of log-likelihood and running time w.r.t. the number of quadrature nodes, the accuracy is not sensitive to the number of quadrature nodes over 100, so the number of quadrature nodes is set to 2000. The number of EM iterations is set to 200... |