- IIT's PyPhi: Analyse data using IIT's Phi at different temporal scales. The main contribution is in temporal_emergence.py, where various coarse graining methods are implemented. These enable IIT analysis and comparison across temporal scales, in the search for temporal emergence (Hoel et al., 2013; Hoel et al., 2016).
- GLMCC: find functional connectivity between pairs of neurons in terms of Post Synaptic Potentials (PSPs).
- Steinmetz et al. 2019 dataset: Pre-process, visualise and analyse spike-sorted data from the Steinmetz dataset.
- Hoel, E. P., Albantakis, L., & Tononi, G. (2013). Quantifying causal emergence shows that macro can beat micro. Proceedings of the National Academy of Sciences, 110(49), 19790-19795.
- Hoel, E. P., Albantakis, L., Marshall, W., & Tononi, G. (2016). Can the macro beat the micro? Integrated information across spatiotemporal scales. Neuroscience of Consciousness, 2016(1).
- Kobayashi, R., Kurita, S., Kurth, A., Kitano, K., Mizuseki, K., Diesmann, M., ... & Shinomoto, S. (2019). Reconstructing neuronal circuitry from parallel spike trains. Nature communications, 10(1), 1-13.
- Mayner, W. G., Marshall, W., Albantakis, L., Findlay, G., Marchman, R., & Tononi, G. (2018). PyPhi: A toolbox for integrated information theory. PLoS computational biology, 14(7), e1006343.
- Steinmetz, N. A., Zatka-Haas, P., Carandini, M., & Harris, K. D. (2019). Distributed coding of choice, action and engagement across the mouse brain. Nature, 576(7786), 266-273.