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Found 8 papers, 1 papers with code
DFORM: Diffeomorphic vector field alignment for assessing dynamics across learned models
Evaluating the dynamics in such networks is key to understanding their learned generative mechanisms.
Strong anti-Hebbian plasticity alters the convexity of network attractor landscapes
We are specifically interested in how the attractor landscapes of such networks become altered as a function of the strength and nature (Hebbian vs. anti-Hebbian) of learning, which may have a bearing on the ability of such rules to mediate large-scale optimization problems.
Astrocytes as a mechanism for meta-plasticity and contextually-guided network function
We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts.
Contextual guidance: An integrated theory for astrocytes function in brain circuits and behavior
The participation of astrocytes in brain computation was formally hypothesized in 1992, coinciding with the discovery that these glial cells display a complex form of Ca2+ excitability.
Representation Learning for Context-Dependent Decision-Making
Humans are capable of adjusting to changing environments flexibly and quickly.
Non-Stationary Representation Learning in Sequential Linear Bandits
In this paper, we study representation learning for multi-task decision-making in non-stationary environments.
Efficient state and parameter estimation for high-dimensional nonlinear system identification with application to MEG brain network modeling
Intuitively, our approach consists of solving for the parameters that generate the most accurate state estimator (Extended Kalman Filter).
Slow manifolds in recurrent networks encode working memory efficiently and robustly
Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation.
