Fits and analyzes HMM, SLDS, and LDS state-space models on neural time-series data.
npx skills add https://github.com/smestern/sciagent --skill ssm-smestern
Please help me install this Agent Skill. Skill: ssm Source: https://github.com/smestern/sciagent/tree/main/docs/domains/computational-neuro/skills/ssm Command: npx skills add https://github.com/smestern/sciagent --skill ssm-smestern
This Skill enables users to fit and analyze state-space models (HMM, SLDS, LDS) on neural time-series data, addressing the need for advanced modeling and analysis in computational neuroscience.
Fit an HMM to neural data using the ssm skill with the following command: fit_hmm neural_data
High-intent search queries and answers about installing and using this skill.
You can fit state-space models to neural time-series data by providing your neural data to the fitting command. The skill supports fitting HMM, SLDS, and LDS models using the ssm package.
Yes, you can fit Hidden Markov Models to neural data with missing values. The skill handles missing data via masks and supports multiple initializations to ensure robust model fitting.
To compare state-space models with different numbers of states, use the model comparison feature. This evaluates model performance across varying state counts to help identify the optimal configuration.
You extract latent states from an HMM fitted on neural time-series using the latent state extraction feature. This outputs the hidden dynamic states for further computational neuroscience analysis.
Yes, fitting LDS and SLDS models requires the ssm package. This dependency is necessary for the underlying model fitting and analysis operations on neural time-series data.
Use SLDS versus HMM for neural dynamics analysis depending on the required complexity. SLDS combines continuous and discrete latent dynamics, while HMM models discrete state transitions in neural time-series data.
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