Modern Bayesian experimental design
Bayesian experimental design (BED) provides a powerful and general framework for
optimizing the design of experiments. However, its deployment often poses substantial …
optimizing the design of experiments. However, its deployment often poses substantial …
Closed-loop and activity-guided optogenetic control
Advances in optical manipulation and observation of neural activity have set the stage for
widespread implementation of closed-loop and activity-guided optical control of neural …
widespread implementation of closed-loop and activity-guided optical control of neural …
Fast online deconvolution of calcium imaging data
Fluorescent calcium indicators are a popular means for observing the spiking activity of
large neuronal populations, but extracting the activity of each neuron from raw fluorescence …
large neuronal populations, but extracting the activity of each neuron from raw fluorescence …
Systematic errors in connectivity inferred from activity in strongly recurrent networks
Understanding the mechanisms of neural computation and learning will require knowledge
of the underlying circuitry. Because it is difficult to directly measure the wiring diagrams of …
of the underlying circuitry. Because it is difficult to directly measure the wiring diagrams of …
Variational Bayesian optimal experimental design
Bayesian optimal experimental design (BOED) is a principled framework for making efficient
use of limited experimental resources. Unfortunately, its applicability is hampered by the …
use of limited experimental resources. Unfortunately, its applicability is hampered by the …
Implicit deep adaptive design: Policy-based experimental design without likelihoods
Abstract We introduce implicit Deep Adaptive Design (iDAD), a new method for performing
adaptive experiments in real-time with implicit models. iDAD amortizes the cost of Bayesian …
adaptive experiments in real-time with implicit models. iDAD amortizes the cost of Bayesian …
Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience
L Paninski, JP Cunningham - Current opinion in neurobiology, 2018 - Elsevier
Highlights•Modern recording technologies are creating data at a scale and complexity that
demand rigorous data analytical approaches.•Neural data science is an essential bridge …
demand rigorous data analytical approaches.•Neural data science is an essential bridge …
Neuroadaptive Bayesian optimization and hypothesis testing
Cognitive neuroscientists are often interested in broad research questions, yet use overly
narrow experimental designs by considering only a small subset of possible experimental …
narrow experimental designs by considering only a small subset of possible experimental …
Fast active set methods for online spike inference from calcium imaging
J Friedrich, L Paninski - Advances in neural information …, 2016 - proceedings.neurips.cc
Fluorescent calcium indicators are a popular means for observing the spiking activity of
large neuronal populations. Unfortunately, extracting the spike train of each neuron from raw …
large neuronal populations. Unfortunately, extracting the spike train of each neuron from raw …
Efficient" shotgun" inference of neural connectivity from highly sub-sampled activity data
Inferring connectivity in neuronal networks remains a key challenge in statistical
neuroscience. The “common input” problem presents a major roadblock: it is difficult to …
neuroscience. The “common input” problem presents a major roadblock: it is difficult to …