Theoretically Provable Spiking Neural Networks
Authors: Shao-Qun Zhang, Zhi-Hua Zhou
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The aforementioned results, time complexity bound in detail, can be verified by a simulated experiment. We here simulate a 4 × 10, 000 spike sequence from the Iris data sets with a timestamp of 0.001 using Poisson encoding. We employ the one-hidden-layer sc SNN [44] that contains self-connection structure as the conducted SNN model. The number of input channels and hidden neurons are 4 and 10, respectively. The self-connection matrix V is randomly sampled from [0, 1] with bias = 1/3. The above configurations meet the conditions of Theorem 3. Table 2 lists the hyper-parameter values in the conducted sc SNN. For any linear matrix G ∈ R4×10 with ||G||2 < ∞, we define an indicator tc = Ω(n ||G||2 /||V||2). Thus, by exploiting the relation among ϵ, t, and tc, we can verify the explicit polynomial bound, especially the order of magnitude function Ω( ) in Theorem 3. Figure 2 plots the experimental results. |
| Researcher Affiliation | Academia | Shao-Qun Zhang Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210093, China {zhangsq,zhouzh}@lamda.nju.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The simulation experiments employ the codes of Zhang and Zhou [44], and detail the data, settings, and configurations in Pages 7-8. So we believe it is easily to reproduce our experimental results. We will publicash our codes as soon as possible if this paper is accepted. |
| Open Datasets | Yes | We here simulate a 4 × 10, 000 spike sequence from the Iris data sets with a timestamp of 0.001 using Poisson encoding. |
| Dataset Splits | No | The paper mentions using the Iris data set but does not provide specific training, validation, or test dataset splits. It describes generating a |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. It only mentions general settings for the simulation. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) needed to replicate the experiment. |
| Experiment Setup | Yes | Table 2: Hyper-parameter Setting of sc SNNs. Parameters Value Parameters Value Time Step 0.001 Firing Threshold 1 Expect Spike Count (True) 100 Membrane Time τm 0.2 Expect Spike Count (False) 10 Time Constant of Synapse τs 0.008 Encoding Length T 10, 000 Maximum Firing 10 Refractory Period 0.016 Maximum Time 10 |