Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Connectome-constrained Latent Variable Model of Whole-Brain Neural Activity
Authors: Lu Mi, Richard Xu, Sridhama Prakhya, Albert Lin, Nir Shavit, Aravinthan Samuel, Srinivas C Turaga
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We applied this model to an experimental whole-brain dataset, and found that connectomic constraints enable our LVM to predict the activity of neurons whose activity were withheld significantly better than models unconstrained by a connectome. We explored models with different degrees of biophysical detail, and found that models with realistic conductancebased synapses provide markedly better predictions than current-based synapses for this system. |
| Researcher Affiliation | Academia | Lu Mi1,3, Richard Xu1, Sridhama Prakhya1, Albert Lin2, Nir Shavit3, Aravinthan D.T. Samuel2 , Srinivas C. Turaga1 1 HHMI Janelia Research Campus 2 Harvard University 3 MIT EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Below, we have included some pseudocode describing the architecture of our network. Inference_Network ( calcium_fluor , missing_data_mask , s e n s o r y _ i n p u t ) : calcium_input = c o n c a t e n a t e ( calcium_fluor , missing_data_mask ) conv1_out = r e l u ( conv1 ( calcium_input ) ) up1_out = upsample ( conv1_out ) conv2_out = r e l u ( conv2 ( up1_out ) ) up2_out = upsample ( conv2_out ) sensory_conv_out = r e l u ( conv3 ( s e n s o r y _ i n p u t ) ) merged_calcium_sensory = c o n c a t e n a t e ( up2_out , sensory_conv_out ) mean_latent_neuron_voltage = conv4 ( merged_calc_sensory ) s t d _ l a t e n t _ n e u r o n _ v o l t a g e = s o f t p l u s ( conv5 ( merged_calcim_sensory ) ) s a m p l e _ l a t e n t _ n e u r o n _ v o l t a g e = mean_latent_neuron_voltage + rand_norm * s t d _ l a t e n t _ n e u r o n _ v o l t a g e r e t u r n s a m p l e _ l a t e n t _ n e u r o n _ v o l t a g e Generative_Model ( s a m pl e _ l at e n t _ n e u r on _ v o l t a g e , s e n s o r y _ i n p u t ) : # Equation 1 neuron_voltage_dynamics = leaky_integrator_connectome_dynamics ( s a mp l e _ la t e n t_ n e u r on _ v o l t a g e , s e n s o r y _ i n p u t ) # Equation 5 calcium_concentration_dynamics = l e a k y _ i n t e g r a t o r _ c a l c i u m _ m o d e l ( neuron_voltage_dynamics ) # Equation 6 f l u o r e s c e n c e _ t r a c e = n o n l i n e a r _ a f f i n e _ t r a n s f o r m ( calcium_concentration_dynamcis ) r e t u r n f l u o r e s c e n c e _ t r a c e |
| Open Source Code | Yes | We released our software and datasets (https://github.com/ Turaga Lab/wormvae) for reproducibility. |
| Open Datasets | Yes | We applied the CC-LVM to a calcium imaging dataset in which immobilized, pan-neuronally labeled C.elegans were presented with a panel of chemosensory stimuli (2-butanone, 2,3-pentanedione, and Na Cl) [32]. The connectome constraints we applied utilized the anatomical connectivity data from [30]. |
| Dataset Splits | Yes | We tested this hypothesis by performing neuron holdout evaluations, withholding a single bilateral pair of measured neurons from the model during both training and testing. [...] Another evaluation method we performed was to hold out the data from a handful of individual worms, train the model on the remaining worms, and predict the activity of the neurons of the held-out individuals. For each of the 9 model variants, we trained on 15 worms, and tested the model on 6 withheld worms. |
| Hardware Specification | Yes | We trained each of our LVMs on 1 Quadro RTX 8000. |
| Software Dependencies | No | The paper mentions using "Py Torch" but does not specify a version number. It also mentions "Adam" as an optimizer, but not with a version or as a software dependency. |
| Experiment Setup | Yes | We used an initial learning rate of 3e 4, with a learning rate schedular with a step size of 50, and a gamma of 0.5. We also set a gradient clip value of 1. Each model was trained for 300 epochs in which each epoch is one full pass through all the training data. |