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Found 16 papers, 6 papers with code
Modeling Neural Activity with Conditionally Linear Dynamical Systems
Neural population activity exhibits complex, nonlinear dynamics, varying in time, over trials, and across experimental conditions.
Comparing noisy neural population dynamics using optimal transport distances
Biological and artificial neural systems form high-dimensional neural representations that underpin their computational capabilities.
What Representational Similarity Measures Imply about Decodable Information
Neural responses encode information that is useful for a variety of downstream tasks.
Discriminating image representations with principal distortions
This metric may then be used to optimally differentiate a set of models, by finding a pair of "principal distortions" that maximize the variance of the models under this metric.
Tensor Decomposition Meets RKHS: Efficient Algorithms for Smooth and Misaligned Data
We refer to tensors with some infinite-dimensional modes as quasitensors, and the approach of decomposing a tensor with some continuous RKHS modes is referred to as CP-HiFi (hybrid infinite and finite dimensional) tensor decomposition.
Duality of Bures and Shape Distances with Implications for Comparing Neural Representations
A multitude of (dis)similarity measures between neural network representations have been proposed, resulting in a fragmented research landscape.
Soft Matching Distance: A metric on neural representations that captures single-neuron tuning
Common measures of neural representational (dis)similarity are designed to be insensitive to rotations and reflections of the neural activation space.
Estimating Shape Distances on Neural Representations with Limited Samples
Measuring geometric similarity between high-dimensional network representations is a topic of longstanding interest to neuroscience and deep learning.
Representational dissimilarity metric spaces for stochastic neural networks
Quantifying similarity between neural representations -- e. g. hidden layer activation vectors -- is a perennial problem in deep learning and neuroscience research.
Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models
This construction is similar to Bayesian nonparametric mixture models like the Dirichlet process mixture model (DPMM) in that the number of latent events (i. e. clusters) is a random variable, but the point process formulation makes the NSP especially well suited to modeling spatiotemporal data.
Generalized Shape Metrics on Neural Representations
In doing so, we identify relationships between neural representations that are interpretable in terms of anatomical features and model performance.
Statistical Neuroscience in the Single Trial Limit
Individual neurons often produce highly variable responses over nominally identical trials, reflecting a mixture of intrinsic "noise" and systematic changes in the animal's cognitive and behavioral state.
Point process models for sequence detection in high-dimensional neural spike trains
Sparse sequences of neural spikes are posited to underlie aspects of working memory, motor production, and learning.
Universality and individuality in neural dynamics across large populations of recurrent networks
To address these foundational questions, we study populations of thousands of networks, with commonly used RNN architectures, trained to solve neuroscientifically motivated tasks and characterize their nonlinear dynamics.
Fast Convolutive Nonnegative Matrix Factorization Through Coordinate and Block Coordinate Updates
Identifying recurring patterns in high-dimensional time series data is an important problem in many scientific domains.
Line attractor dynamics in recurrent networks for sentiment classification
Recurrent neural networks (RNNs) are a powerful tool for modeling sequential data.
