Mathematical models are an important tool for neuroscientists. During the last 30 years
many papers have appeared on single neuron description and specifically on stochastic …

We consider the effect of quantum diffusion on the dynamics of the inflaton during a period of
ultra-slow-roll inflation. We extend the stochastic-δ Script N formalism to the ultra-slow-roll …

The authors evaluated 4 sequential sampling models for 2-choice decisions--the Wiener
diffusion, Ornstein-Uhlenbeck (OU) diffusion, accumulator, and Poisson counter models--by …

Decision-makers must often balance the desire to accumulate information with the costs of
protracted deliberation. Optimal, reward-maximizing decision-making can require dynamic …

A large class of statistical decision models for performance in simple information processing
tasks can be described by linear, first-order, stochastic differential equations (SDEs), whose …

Diffusion models of decision making, in which successive samples of noisy evidence are
accumulated to decision criteria, provide a theoretical solution to von Neumann's problem of …

We calculate the mass distribution of Primordial Black Holes (PBHs) produced during metric
preheating. After inflation, the oscillations of the inflaton at the bottom of its potential source a …

The drift-diffusion model (DDM) is a model of sequential sampling with diffusion signals,
where the decision maker accumulates evidence until the process hits either an upper or …

The role of stochastic diffusion processes for modeling purposes is discussed. Special
emphasis is put on neuronal firing problems and on the description of popula tion dynamics …

Optimality studies and studies of decision-making in monkeys have been used to support a
model in which the decision boundaries used to evaluate evidence collapse over time. This …