Scalable Inference for Neuronal Connectivity from Calcium Imaging
Authors: Alyson K. Fletcher, Sundeep Rangan
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations of the method on realistic neural networks demonstrate good accuracy with computation times that are potentially significantly faster than current approaches based on Markov Chain Monte Carlo methods. and 4 Numerical Example The method was tested using realistic network parameters, as shown in Table 1, similar to those found in neurons networks within a cortical column [24]. |
| Researcher Affiliation | Academia | The provided text does not contain clear institutional affiliations (university names, company names, or email domains) for the authors Alyson K. Fletcher and Sundeep Rangan. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described (no specific repository link, explicit code release statement, or code in supplementary materials). |
| Open Datasets | No | The method was tested using realistic network parameters, as shown in Table 1, similar to those found in neurons networks within a cortical column [24]. and To stabilize the system, we followed the procedure in [8] where the system is simulated multiple times. This indicates simulated data, not a publicly available dataset. |
| Dataset Splits | No | The paper uses simulated data and describes the iterative EM procedure for parameter estimation, but it does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Table 1 lists 'Parameters for the Ca image simulation', including 'Number of neurons, N 100', 'Connection sparsity 10%', 'Mean firing rate per neuron 10 Hz', 'Simulation time step, 1 ms', 'Total simulation time, T 10 sec (10,000 time steps)', 'Integration time constant, αIF 20 ms', 'Conduction delay, δ 2 time steps = 2 ms', 'Ca time constant, αCA 500 ms', 'Ca frame rate , 1/TF 100 Hz', which provides specific experimental setup details for the simulations. |