A unifying perspective on neural manifolds and circuits for cognition
Two different perspectives have informed efforts to explain the link between the brain and
behaviour. One approach seeks to identify neural circuit elements that carry out specific …
behaviour. One approach seeks to identify neural circuit elements that carry out specific …
Simulation intelligence: Towards a new generation of scientific methods
The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …
computing, where a motif is an algorithmic method that captures a pattern of computation …
Training deep neural density estimators to identify mechanistic models of neural dynamics
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of
underlying causes. However, determining which model parameters agree with complex and …
underlying causes. However, determining which model parameters agree with complex and …
Connecting connectomes to physiology
With the advent of volumetric EM techniques, large connectomic datasets are being created,
providing neuroscience researchers with knowledge about the full connectivity of neural …
providing neuroscience researchers with knowledge about the full connectivity of neural …
Neural learning rules for generating flexible predictions and computing the successor representation
The predictive nature of the hippocampus is thought to be useful for memory-guided
cognitive behaviors. Inspired by the reinforcement learning literature, this notion has been …
cognitive behaviors. Inspired by the reinforcement learning literature, this notion has been …
Inference on the macroscopic dynamics of spiking neurons
The process of inference on networks of spiking neurons is essential to decipher the
underlying mechanisms of brain computation and function. In this study, we conduct …
underlying mechanisms of brain computation and function. In this study, we conduct …
Interactions between circuit architecture and plasticity in a closed-loop cerebellar system
Determining the sites and directions of plasticity underlying changes in neural activity and
behavior is critical for understanding mechanisms of learning. Identifying such plasticity from …
behavior is critical for understanding mechanisms of learning. Identifying such plasticity from …
pyABC: Efficient and robust easy-to-use approximate Bayesian computation
The Python package pyABC provides a framework for approximate Bayesian computation
(ABC), a likelihood-free parameter inference method popular in many research areas. At its …
(ABC), a likelihood-free parameter inference method popular in many research areas. At its …
Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis
A key aspect of neuroscience research is the development of powerful, general-purpose
data analyses that process large datasets. Unfortunately, modern data analyses have a …
data analyses that process large datasets. Unfortunately, modern data analyses have a …
Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics
While multicompartment models have long been used to study the biophysics of neurons, it
is still challenging to infer the parameters of such models from data including uncertainty …
is still challenging to infer the parameters of such models from data including uncertainty …