| Name | scikit-neuromsi JSON |
| Version |
1.0.2
JSON |
| download |
| home_page | None |
| Summary | Implementation of multisensory integration models in Python |
| upload_time | 2025-07-12 16:11:37 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | Copyright (c) 2021, Paredes, Renato; Cabral, Juan
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| keywords |
multisensory integration
computational neuroscience
cognitive modelling
behaviour simulation
perception
|
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# Scikit-NeuroMSI
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**Scikit-NeuroMSI** is an open-source Python framework that simplifies the implementation of neurocomputational models of multisensory integration.
## Motivation
Research on the neural mechanisms underlying multisensory integration—where unisensory signals combine to produce distinct multisensory responses—has surged in recent years. Despite this progress, a unified theoretical framework for multisensory integration remains elusive. **Scikit-NeuroMSI** aims to bridge this gap by providing a standardized, flexible, and extensible platform for modeling multisensory integration, fostering the development of theories that connect neural and behavioral responses.
## Features
**Scikit-NeuroMSI** was designed to meet three fundamental requirements in the computational study of multisensory integration:
- **Modeling Standardization**: Standardized interface for implementing and analyzing different types of models. The package currently handles the following model families: Maximum Likelihood Estimation, Bayesian Causal Inference, and Neural Networks.
- **Data Processing Pipeline**: Multidimensional data processing across spatial dimensions (1D to 3D spatial coordinates), temporal sequences, and multiple sensory modalities (e.g., visual, auditory, touch).
- **Analysis Tools**: Integrated tools for parameter sweeping across model configurations, result visualization and export, and statistical analysis of model outputs.
In addition, there is a **core** module with features to facilitate the implementation of new models of multisensory integration.
## Requirements
You need Python 3.10+ to run scikit-neuromsi.
## Installation
Run the following command:
```bash
pip install scikit-neuromsi
```
or clone this repo and then inside the local directory execute:
```bash
pip install -e .
```
## Usage Examples
### Run models of multisensory integration
Simulate responses from existing models of multisensory integration (e.g. audio-visual causal inference network from Cuppini et al. (2017)):
```python
from skneuromsi.neural import Cuppini2017
# Model setup
model_cuppini2017 = Cuppini2017(neurons=90,
position_range=(0, 90))
# Model execution
res = model_cuppini2017.run(auditory_position=35,
visual_position=52)
# Results plot
ax1 = plt.subplot()
res.plot.linep(ax=ax1)
ax1.set_ylabel("neural activity")
ax1.set_xlabel("stimulus location (deg)")
```
### Simulate experimental paradigms
Simulate multisensory integration experiments (e.g. causal inference under spatial disparity - "ventriloquist effect") using the parameter sweep tool:
```python
from skneuromsi.sweep import ParameterSweep
import numpy as np
# Experiment setup
spatial_disparities = np.array([-24, -12, -6, -3, 3, 6, 12, 24])
sp_cuppini2017 = ParameterSweep(model=model_cuppini2017,
target="visual_position",
repeat=1,
range=45 + spatial_disparities)
# Experiment run
res_sp_cuppini2017 = sp_cuppini2017.run(auditory_position=45,
auditory_sigma=4.5,
visual_sigma=3.5)
# Experiment results plot
ax1 = plt.subplot()
res_sp_cuppini2017.plot(kind="unity_report", label="Cuppini 2017", ax=ax1)
ax1.set_xlabel("visual position (deg)")
```
For more detailed examples and advanced usage, refer to the [Scikit-NeuroMSI Documentation](https://scikit-neuromsi.readthedocs.io/).
## Contribute to Scikit-NeuroMSI
We welcome contributions to Scikit-NeuroMSI!
If you're a multisensory integration researcher, we encourage you to integrate your models directly into our package. If you're a software developer, we'd love your help in enhancing the overall functionality of Scikit-NeuroMSI.
For detailed information on how to contribute ideas, report bugs, or improve the codebase, please refer to our [Contribuiting Guidelines](https://github.com/renatoparedes/scikit-neuromsi/blob/main/CONTRIBUTING.md).
## License
Scikit-NeuroMSI is under
[The 3-Clause BSD License](https://github.com/renatoparedes/scikit-neuromsi/blob/main/LICENSE.txt)
This license allows unlimited redistribution for any purpose as long as
its copyright notices and the license’s disclaimers of warranty are maintained.
## How to cite?
If you want to cite Scikit-NeuroMSI, please use the following references:
> Paredes, R., Cabral, J. B., & Series, P. (2025). Scikit-NeuroMSI: A Generalized Framework for Modeling Multisensory Integration. bioRxiv, 2025-05. doi: https://doi.org/10.1101/2025.05.26.656124
>Paredes, R., Series, P., Cabral, J. (2023). Scikit-NeuroMSI: a Python framework for multisensory integration modelling. IX Congreso de Matematica Aplicada, Computacional e Industrial, 9, 545–548.
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"description": "# Scikit-NeuroMSI\n\n\n<!-- BODY -->\n\n[](https://github.com/renatoparedes/scikit-neuromsi/actions/workflows/ci.yml)\n[](https://scikit-neuromsi.readthedocs.io/en/latest/?badge=latest)\n[](https://pypi.org/project/scikit-neuromsi/)\n[](https://pypi.org/project/scikit-neuromsi/)\n[](https://coveralls.io/github/renatoparedes/scikit-neuromsi?branch=main)\n[](https://github.com/psf/black)\n[](https://opensource.org/licenses/BSD-3-Clause)\n[](https://github.com/leliel12/diseno_sci_sfw)\n[](https://doi.org/10.1101/2025.05.26.656124)\n[](https://pepy.tech/projects/scikit-neuromsi)\n\n**Scikit-NeuroMSI** is an open-source Python framework that simplifies the implementation of neurocomputational models of multisensory integration.\n\n## Motivation\n\nResearch on the neural mechanisms underlying multisensory integration\u2014where unisensory signals combine to produce distinct multisensory responses\u2014has surged in recent years. Despite this progress, a unified theoretical framework for multisensory integration remains elusive. **Scikit-NeuroMSI** aims to bridge this gap by providing a standardized, flexible, and extensible platform for modeling multisensory integration, fostering the development of theories that connect neural and behavioral responses.\n\n## Features\n\n**Scikit-NeuroMSI** was designed to meet three fundamental requirements in the computational study of multisensory integration:\n\n- **Modeling Standardization**: Standardized interface for implementing and analyzing different types of models. The package currently handles the following model families: Maximum Likelihood Estimation, Bayesian Causal Inference, and Neural Networks.\n\n- **Data Processing Pipeline**: Multidimensional data processing across spatial dimensions (1D to 3D spatial coordinates), temporal sequences, and multiple sensory modalities (e.g., visual, auditory, touch).\n\n- **Analysis Tools**: Integrated tools for parameter sweeping across model configurations, result visualization and export, and statistical analysis of model outputs.\n\nIn addition, there is a **core** module with features to facilitate the implementation of new models of multisensory integration.\n\n## Requirements\n\nYou need Python 3.10+ to run scikit-neuromsi.\n\n## Installation\n\nRun the following command:\n\n```bash\npip install scikit-neuromsi\n```\n\nor clone this repo and then inside the local directory execute:\n\n```bash\npip install -e .\n```\n\n## Usage Examples\n\n### Run models of multisensory integration\n\nSimulate responses from existing models of multisensory integration (e.g. audio-visual causal inference network from Cuppini et al. (2017)):\n\n```python\nfrom skneuromsi.neural import Cuppini2017\n\n# Model setup\nmodel_cuppini2017 = Cuppini2017(neurons=90, \n position_range=(0, 90))\n\n# Model execution\nres = model_cuppini2017.run(auditory_position=35, \n visual_position=52)\n\n# Results plot\nax1 = plt.subplot()\nres.plot.linep(ax=ax1)\nax1.set_ylabel(\"neural activity\")\nax1.set_xlabel(\"stimulus location (deg)\")\n```\n\n\n### Simulate experimental paradigms\n\nSimulate multisensory integration experiments (e.g. causal inference under spatial disparity - \"ventriloquist effect\") using the parameter sweep tool:\n\n```python\nfrom skneuromsi.sweep import ParameterSweep\nimport numpy as np\n\n# Experiment setup\nspatial_disparities = np.array([-24, -12, -6, -3, 3, 6, 12, 24])\n\nsp_cuppini2017 = ParameterSweep(model=model_cuppini2017,\n target=\"visual_position\",\n repeat=1,\n range=45 + spatial_disparities)\n\n# Experiment run\nres_sp_cuppini2017 = sp_cuppini2017.run(auditory_position=45,\n auditory_sigma=4.5,\n visual_sigma=3.5)\n\n# Experiment results plot\nax1 = plt.subplot()\nres_sp_cuppini2017.plot(kind=\"unity_report\", label=\"Cuppini 2017\", ax=ax1)\nax1.set_xlabel(\"visual position (deg)\")\n```\n\n\nFor more detailed examples and advanced usage, refer to the [Scikit-NeuroMSI Documentation](https://scikit-neuromsi.readthedocs.io/).\n\n## Contribute to Scikit-NeuroMSI\n\nWe welcome contributions to Scikit-NeuroMSI! \n\nIf you're a multisensory integration researcher, we encourage you to integrate your models directly into our package. If you're a software developer, we'd love your help in enhancing the overall functionality of Scikit-NeuroMSI. \n\nFor detailed information on how to contribute ideas, report bugs, or improve the codebase, please refer to our [Contribuiting Guidelines](https://github.com/renatoparedes/scikit-neuromsi/blob/main/CONTRIBUTING.md).\n\n## License\n\nScikit-NeuroMSI is under\n[The 3-Clause BSD License](https://github.com/renatoparedes/scikit-neuromsi/blob/main/LICENSE.txt)\n\nThis license allows unlimited redistribution for any purpose as long as\nits copyright notices and the license\u2019s disclaimers of warranty are maintained.\n\n## How to cite?\n\nIf you want to cite Scikit-NeuroMSI, please use the following references:\n\n> Paredes, R., Cabral, J. B., & Series, P. (2025). Scikit-NeuroMSI: A Generalized Framework for Modeling Multisensory Integration. bioRxiv, 2025-05. doi: https://doi.org/10.1101/2025.05.26.656124\n\n>Paredes, R., Series, P., Cabral, J. (2023). Scikit-NeuroMSI: a Python framework for multisensory integration modelling. IX Congreso de Matematica Aplicada, Computacional e Industrial, 9, 545\u2013548.\n",
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