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Found 10 papers, 4 papers with code
$Γ$-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data
Natural systems with emergent behaviors often organize along low-dimensional subsets of high-dimensional spaces.
Shaping dynamical neural computations using spatiotemporal constraints
Here, we discuss how neural systems use dynamics for computation, and claim that the biological constraints that shape brain networks may be leveraged to improve the implementation of artificial neural networks.
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.
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.
Variability in higher order structure of noise added to weighted networks
We address this problem by examining the higher-order structure of noisy, weak edges added to model networks.
Teaching Recurrent Neural Networks to Modify Chaotic Memories by Example
Here we demonstrate that a recurrent neural network (RNN) can learn to modify its representation of complex information using only examples, and we explain the associated learning mechanism with new theory.
Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks
Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well-understood.
Linear Dynamics & Control of Brain Networks
The brain is an intricately structured organ responsible for the rich emergent dynamics that support the complex cognitive functions we enjoy as humans.
