RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environments
… of our Ornstein- Uhlenbeck processes (including applying a … a Rayleigh distribution, see
Methods) to those measured … We can re- parameterise the Ornstein Uhlenbeck process in terms …
Methods) to those measured … We can re- parameterise the Ornstein Uhlenbeck process in terms …
A score-based approach for training schrödinger bridges for data modelling
… to Ornstein–Uhlenbeck processes), the required scores can be estimated from exact samples
and be represented by neural … , we start out by applying the negative time differential to the …
and be represented by neural … , we start out by applying the negative time differential to the …
The Expected Loss of Preconditioned Langevin Dynamics Reveals the Hessian Rank
… , LD reduces to an Ornstein–Uhlenbeck process, which is … metric that is equal to the inverse
of the preconditioner. Additionally… a real-world application of nonlinear deep neural networks. …
of the preconditioner. Additionally… a real-world application of nonlinear deep neural networks. …
Fractional diffusion theory of balanced heterogeneous neural networks
A Wardak, P Gong - Physical Review Research, 2021 - APS
… for the mean first passage time of an IF neuron with random … an Ornstein-Uhlenbeck process
with a slowly varying time constant… In the future it would be interesting to apply our fractional …
with a slowly varying time constant… In the future it would be interesting to apply our fractional …
On the empirical estimator of the boundary in inverse first-exit problems
S Gür, K Pötzelberger - Computational Statistics, 2021 - Springer
… first-passage time problems, eg mathematical finance, in particular credit risk modelling or in
biology, in neural … Alternative priors, such as (discrete) Ornstein–Uhlenbeck processes have …
biology, in neural … Alternative priors, such as (discrete) Ornstein–Uhlenbeck processes have …
Modeling driver's evasive behavior during safety–critical lane changes: Two-dimensional time-to-collision and deep reinforcement learning
… A similar validation method will be adopted in this paper. … Additionally, a neural network
(NN) model with two 256-neuron hidden layers was developed as the benchmark for comparison…
(NN) model with two 256-neuron hidden layers was developed as the benchmark for comparison…
Interacting particle solutions of fokker–planck equations through gradient–log–density estimation
… Similar to the Ornstein Uhlenbeck process (Section 10.1), … Moreover, the ability of neural
networks to automatically … the two dimensional Ornstein-Uhlenbeck process captured by …
networks to automatically … the two dimensional Ornstein-Uhlenbeck process captured by …
Threshold-impeded stochastic production: how noise interacts with disruptive thresholds to affect the production output in fluctuating environments
… application, the authors also examined time-correlated input from an OrnsteinUhlenbeck
process … have established the mean hitting time of an Ornstein-Uhlenbeck process (see Section …
process … have established the mean hitting time of an Ornstein-Uhlenbeck process (see Section …
Solving Linear Inverse Problems using Higher-Order Annealed Langevin Diffusion
N Zilberstein, A Sabharwal… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
… neural networks have become ubiquitous in solving inverse … labeled O refers to the
OrnsteinUhlenbeck process, which has a … We start by applying our framework to the symbol …
OrnsteinUhlenbeck process, which has a … We start by applying our framework to the symbol …
Optimal noise level for coding with tightly balanced networks of spiking neurons in the presence of transmission delays
… variables drawn from the first-passage time distribution, we … at the time of the first spike:
(21) where erfc −1 is the inverse … , one-dimensional scalar signal, our results can also apply to …
(21) where erfc −1 is the inverse … , one-dimensional scalar signal, our results can also apply to …