Uploaded April 2022 | Updated September 2026, 1 week ago
Jeremy Schwartz and Seth Alter, MIT
In this tutorial, Jeremy Schwartz will walk us through the features and capabilities of ThreeDWorld (threedworld.org/), a high-fidelity, multi-modal platform for interactive physical simulation. Next, Seth Alter will conduct a tutorial lab session. The repository is available here - github.com/alters-mit/tdw_bcs_demo/tree/main - (please note that it is not needed to run the code in advance — only requirement is to have Python 3.6 or higher installed).
Jeremy Schwartz and Seth Alter, MIT
In this tutorial, Jeremy Schwartz will walk us through the features and capabilities of ThreeDWorld (threedworld.org/), a high-fidelity, multi-modal platform for interactive physical simulation. Next, Seth Alter will conduct a tutorial lab session. The repository is available here - github.com/alters-mit/tdw_bcs_demo/tree/main - (please note that it is not needed to run the code in advance — only requirement is to have Python 3.6 or higher installed).


![Continuous-time deconvolutional regression: A method for studying continuous dynamics in naturali...
[full title] Continuous-time deconvolutional regression: A method for studying continuous dynamics in naturalistic data
Cory Shain, MIT
Abstract: Naturalistic experiments are of growing interest to neuroscientists and cognitive scientists. Naturalistic data can be hard to analyze because critical events can occur at irregular intervals, and measured responses to those events can overlap and interact in complex ways. For example, words come quickly enough during naturalistic reading and listening that the brain responses to words likely overlap in time, and inherent variability in word durations can make these responses difficult to identify from data. In this tutorial, I will present continuous-time deconvolutional regression (CDR), a new approach to analyzing naturalistic time series. CDR uses machine learning to estimate impulse response functions from data, but, unlike established methods like finite impulse response modeling, these functions are defined in continuous time. CDR can therefore directly estimate event-related responses in a range of naturalistic experiment types, including fMRI, EEG/MEG, and behavioral measures. The tutorial will demonstrate how to define, fit, and evaluate CDR models, how to test hypotheses in the CDR framework, how to visualize patterns with CDR, and how CDR can be used to relax a range of assumptions about time series data. These steps can be run from the command line using an open-source Python library, with no need for users to write any code.
The sample data+models for the CDR tutorial available here: https://www.dropbox.com/sh/yz4l5745nxz8h7w/AACWgDwfKjhkXlK0L5eM-d25a?dl=0. The files data.zip and models.zip need to be downloaded and extracted.
CDR can be installed with either Anaconda or python+pip. To install with pip, run: pip install https://github.com/coryshain/cdr/archive/refs/tags/v0.5.3.tar.gz
Conda installation instructions are available in the readme at https://github.com/coryshain/cdr. Continuous-time deconvolutional regression: A method for studying continuous dynamics in naturali...](https://i.ytimg.com/vi/fNktSKmckBE/mqdefault.jpg)







![Efficient representation, learning, and planning through abstraction: clustering cognitive spaces...
[full title] Efficient representation, learning, and planning through abstraction: clustering cognitive spaces into submaps
Ila Fiete, MIT
Abstract: Episodic memory involves fragmenting the continuous stream of experience into discrete episodes. Not coincidentally, the hippocampus, which plays a central role in both episodic memory and spatial navigation, represents large spatial environments in a fragmented way even when explored in a continuous trajectory. In non-spatial and non-memory contexts too, humans report sudden contextual re-anchoring or re-orientation when reading garden path sentences (“Time flies like an arrow, fruit flies like a banana.) or watching a movie with viewpoint changes. In this talk, I will describe a theory for the online and real-time generation of fragmented representations and contextual re-anchoring from continuous experience that resemble those obtained by principled but offline and computationally complex information-based algorithms. The resulting fragmentations closely match those observed from neural recordings in animals navigating through complex environments. I will discuss the utility of map fragmentation, as a form of state abstraction that enables representation fidelity, flexible and rapid learning through reuse of existing fragments, and many-fold improvements in the ability to plan and navigate through complex environments relative to more global representations. Efficient representation, learning, and planning through abstraction: clustering cognitive spaces...](https://i.ytimg.com/vi/gfgoLjhrh7k/mqdefault.jpg)