Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings
Brain networks exist within the confines of resource limitations. As a result, a brain network
must overcome the metabolic costs of growing and sustaining the network within its physical …
must overcome the metabolic costs of growing and sustaining the network within its physical …
SORN: a self-organizing recurrent neural network
Understanding the dynamics of recurrent neural networks is crucial for explaining how the
brain processes information. In the neocortex, a range of different plasticity mechanisms are …
brain processes information. In the neocortex, a range of different plasticity mechanisms are …
Growing brains: Co-emergence of anatomical and functional modularity in recurrent neural networks
Recurrent neural networks (RNNs) trained on compositional tasks can exhibit functional
modularity, in which neurons can be clustered by activity similarity and participation in …
modularity, in which neurons can be clustered by activity similarity and participation in …
[HTML][HTML] Bio-instantiated recurrent neural networks: Integrating neurobiology-based network topology in artificial networks
A Goulas, F Damicelli, CC Hilgetag - Neural Networks, 2021 - Elsevier
Biological neuronal networks (BNNs) are a source of inspiration and analogy making for
researchers that focus on artificial neuronal networks (ANNs). Moreover, neuroscientists …
researchers that focus on artificial neuronal networks (ANNs). Moreover, neuroscientists …
Latent circuit inference from heterogeneous neural responses during cognitive tasks
Higher cortical areas carry a wide range of sensory, cognitive, and motor signals supporting
complex goal-directed behavior. These signals are mixed in heterogeneous responses of …
complex goal-directed behavior. These signals are mixed in heterogeneous responses of …
Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion
Neural networks need the right representations of input data to learn. Here we ask how
gradient-based learning shapes a fundamental property of representations in recurrent …
gradient-based learning shapes a fundamental property of representations in recurrent …
Brain connectivity meets reservoir computing
F Damicelli, CC Hilgetag, A Goulas - PLoS computational biology, 2022 - journals.plos.org
The connectivity of Artificial Neural Networks (ANNs) is different from the one observed in
Biological Neural Networks (BNNs). Can the wiring of actual brains help improve ANNs …
Biological Neural Networks (BNNs). Can the wiring of actual brains help improve ANNs …
Universality and individuality in neural dynamics across large populations of recurrent networks
N Maheswaranathan, A Williams… - Advances in neural …, 2019 - proceedings.neurips.cc
Many recent studies have employed task-based modeling with recurrent neural networks
(RNNs) to infer the computational function of different brain regions. These models are often …
(RNNs) to infer the computational function of different brain regions. These models are often …
A mechanistic multi-area recurrent network model of decision-making
M Kleinman, C Chandrasekaran… - Advances in neural …, 2021 - proceedings.neurips.cc
Recurrent neural networks (RNNs) trained on neuroscience-based tasks have been widely
used as models for cortical areas performing analogous tasks. However, very few tasks …
used as models for cortical areas performing analogous tasks. However, very few tasks …
Engineering recurrent neural networks from task-relevant manifolds and dynamics
E Pollock, M Jazayeri - PLoS computational biology, 2020 - journals.plos.org
Many cognitive processes involve transformations of distributed representations in neural
populations, creating a need for population-level models. Recurrent neural network models …
populations, creating a need for population-level models. Recurrent neural network models …