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- Michael R. DeWeese, Martin Meister
- Network
- 1999

Information theory provides a powerful framework to analyse how neurons represent sensory stimuli or other behavioural variables. A recurring question regards the amount of information conveyed by a specific neuronal response. Here we show that the commonly used definition for this quantity has a serious flaw: the information accumulated during subsequent… (More)

Fitting probabilistic models to data is often difficult, due to the general intractability of the partition function and its derivatives. Here we propose a new parameter estimation technique that does not require computing an intractable normalization factor or sampling from the equilibrium distribution of the model. This is achieved by establishing… (More)

- Jascha Sohl-Dickstein, Peter Battaglino, Michael R. DeWeese
- Physical review letters
- 2011

Fitting probabilistic models to data is often difficult, due to the general intractability of the partition function. We propose a new parameter fitting method, minimum probability flow (MPF), which is applicable to any parametric model. We demonstrate parameter estimation using MPF in two cases: a continuous state space model, and an Ising spin glass. In… (More)

- Chris C. Rodgers, Michael R. DeWeese
- Neuron
- 2014

Animals can selectively respond to a target sound despite simultaneous distractors, just as humans can respond to one voice at a crowded cocktail party. To investigate the underlying neural mechanisms, we recorded single-unit activity in primary auditory cortex (A1) and medial prefrontal cortex (mPFC) of rats selectively responding to a target sound from a… (More)

We present a method for performing Hamiltonian Monte Carlo that largely eliminates sample rejection. In situations that would normally lead to rejection, instead a longer trajectory is computed until a new state is reached that can be accepted. This is achieved using Markov chain transitions that satisfy the fixed point equation, but do not satisfy detailed… (More)

Maximum entropy models are increasingly being used to describe the collective activity of neural populations with measured mean neural activities and pairwise correlations, but the full space of probability distributions consistent with these constraints has not been explored. We provide upper and lower bounds on the entropy for the minimum entropy… (More)

- Jascha Sohl-Dickstein, Santani Teng, +4 authors Nicol S. Harper
- IEEE Trans. Biomed. Engineering
- 2015

OBJECTIVE
We present a device that combines principles of ultrasonic echolocation and spatial hearing to provide human users with environmental cues that are 1) not otherwise available to the human auditory system, and 2) richer in object and spatial information than the more heavily processed sonar cues of other assistive devices. The device consists of a… (More)

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