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Found 13 papers, 5 papers with code
Machine-learning optimized measurements of chaotic dynamical systems via the information bottleneck
Deterministic chaos permits a precise notion of a "perfect measurement" as one that, when obtained repeatedly, captures all of the information created by the system's evolution with minimal redundancy.
Human Learning of Hierarchical Graphs
Using a serial response experiment with human participants (n=$100$), we replicate our predictions by detecting a surprisal effect at the finer-level of the hierarchy but not at the coarser-level of the hierarchy.
Intrinsically motivated graph exploration using network theories of human curiosity
The theories view curiosity as an intrinsic motivation to optimize for topological features of subgraphs induced by nodes visited in the environment.
Information decomposition in complex systems via machine learning
Guided by the distributed information bottleneck as a learning objective, the information decomposition identifies the variation in the measurements of the system state most relevant to specified macroscale behavior.
Interpretability with full complexity by constraining feature information
Borrowing from information theory, we use the Distributed Information Bottleneck to find optimal compressions of each feature that maximally preserve information about the output.
Compression supports low-dimensional representations of behavior across neural circuits
Using a large sample of youth ($n=1, 040$), we test predictions in two ways: by measuring the dimensionality of spontaneous activity from sensorimotor to association cortex, and by assessing the representational capacity for 24 behaviors in neural circuits and 20 cognitive variables in recurrent neural networks.
Characterizing information loss in a chaotic double pendulum with the Information Bottleneck
A hallmark of chaotic dynamics is the loss of information with time.
Controlling collective dynamical states of mesoscale brain networks with local perturbations
Oscillatory synchrony is hypothesized to support the flow of information between brain regions, with different phase-locked configurations enabling activation of different effective interactions.
A structurally informed model for modulating functional connectivity
For each individual, we measured the TMS-induced change in FC between the FPS and DMS (the FC network), and the structural coupling between the stimulated area and the FPS and DMS (the structural context network (SCN)).
The Distributed Information Bottleneck reveals the explanatory structure of complex systems
The Distributed Information Bottleneck throttles the downstream complexity of interactions between the components of the input, deconstructing a relationship into meaningful approximations found through deep learning without requiring custom-made datasets or neural network architectures.
Curiosity as filling, compressing, and reconfiguring knowledge networks
We formalize curiosity as the process of building a growing knowledge network to quantitatively investigate information gap theory, compression progress theory, and the conformational change theory of curiosity.
The information content of brain states is explained by structural constraints on state energetics
These activity patterns define global brain states and contain information in accordance with their expected probability of occurrence.
Learning Continuous Chaotic Attractors with a Reservoir Computer
Neural systems are well known for their ability to learn and store information as memories.
