Objective and efficient inference for couplings in neuronal networks
Authors: Yu Terada, Tomoyuki Obuchi, Takuya Isomura, Yoshiyuki Kabashima
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here, we apply a recently proposed objective procedure to the spike data obtained from the Hodgkin Huxley type models and in vitro neuronal networks cultured in a circular structure. As a result, we succeed in reconstructing synaptic connections accurately from the evoked activity as well as the spontaneous one. |
| Researcher Affiliation | Academia | 1Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan 2Department of Mathematical and Computer Science Tokyo Institute of Technology Tokyo 152-8550, Japan |
| Pseudocode | No | The paper provides mathematical formulas and descriptions of procedures, but no distinct pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about releasing source code for the described methodology. |
| Open Datasets | Yes | Based on this motivation, this study applies these methods to the data from the Hodgkin Huxley model...Further, we examine the situation where responses of neuronal networks are evoked by external stimuli. We implement this situation both in the Hodgkin Huxley model and in a cultured neuronal network of a previously described design [31] |
| Dataset Splits | No | The paper mentions using 'the whole spike train data' for inference but does not specify explicit train/validation/test splits or cross-validation details for reproducibility. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments (e.g., GPU/CPU models, memory, or cloud resources). |
| Software Dependencies | No | The paper describes mathematical models and inference algorithms, but it does not list specific software dependencies with version numbers (e.g., Python, PyTorch, or specific solvers). |
| Experiment Setup | Yes | Using τ = 3 ms to make the spike trains coarse-grained, we apply the inverse formula to the series and screen relevant couplings with pth = 10−3 |