• Publications
  • Influence
Action Potential Energy Efficiency Varies Among Neuron Types in Vertebrates and Invertebrates
The initiation and propagation of action potentials (APs) places high demands on the energetic resources of neural tissue. Each AP forces ATP-driven ion pumps to work harder to restore the ionicExpand
  • 189
  • 15
  • PDF
Information and Efficiency in the Nervous System—A Synthesis
In systems biology, questions concerning the molecular and cellular makeup of an organism are of utmost importance, especially when trying to understand how unreliable components—like geneticExpand
  • 126
  • 10
  • PDF
Balanced Excitatory and Inhibitory Synaptic Currents Promote Efficient Coding and Metabolic Efficiency
A balance between excitatory and inhibitory synaptic currents is thought to be important for several aspects of information processing in cortical neurons in vivo, including gain control, bandwidthExpand
  • 60
  • 6
Adversarial Information Factorization
We propose a novel generative model architecture designed to learn representations for images that factor out a single attribute from the rest of the representation. A single object may have manyExpand
  • 8
  • 4
  • PDF
Consequences of Converting Graded to Action Potentials upon Neural Information Coding and Energy Efficiency
Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied byExpand
  • 33
  • 3
  • PDF
Cognitive Dynamics: From Attractors to Active Inference
This paper combines recent formulations of self-organization and neuronal processing to provide an account of cognitive dynamics from basic principles. We start by showing that inference (andExpand
  • 46
  • 3
Efficient gradient computation for dynamical models
Data assimilation is a fundamental issue that arises across many scales in neuroscience — ranging from the study of single neurons using single electrode recordings to the interaction of thousands ofExpand
  • 27
  • 2
  • PDF
Gradient-based MCMC samplers for dynamic causal modelling
In this technical note, we derive two MCMC (Markov chain Monte Carlo) samplers for dynamic causal models (DCMs). Specifically, we use (a) Hamiltonian MCMC (HMC-E) where sampling is simulated usingExpand
  • 31
  • 1
  • PDF
mpdcm: A toolbox for massively parallel dynamic causal modeling
BACKGROUND Dynamic causal modeling (DCM) for fMRI is an established method for Bayesian system identification and inference on effective brain connectivity. DCM relies on a biophysical model thatExpand
  • 27
  • 1
  • PDF
Annealed Importance Sampling for Neural Mass Models
Neural Mass Models provide a compact description of the dynamical activity of cell populations in neocortical regions. Moreover, models of regional activity can be connected together into networks,Expand
  • 11
  • 1
  • PDF