AIMC Topic: Computer Simulation

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Entropy-Weighted Numerical Gradient Optimization Spiking Neural System for Biped Robot Control.

International journal of neural systems
The optimization of robot controller parameters is a crucial task for enhancing robot performance, yet it often presents challenges due to the complexity of multi-objective, multi-dimensional multi-parameter optimization. This paper introduces a nove...

A neural network paradigm for modeling psychometric data and estimating IRT model parameters: Cross estimation network.

Behavior research methods
This paper presents a novel approach known as the cross estimation network (CEN) for fitting the datasets obtained from psychological or educational tests and estimating the parameters of item response theory (IRT) models. The CEN is comprised of two...

Mixing neural networks, continuation and symbolic computation to solve parametric systems of non linear equations.

Neural networks : the official journal of the International Neural Network Society
We consider a square non linear parametric equations system F(P,X) = 0 which is constituted of n non differential equations in the n unknowns {x,…,x} that are the components of X while P={p,…,p} is a set of m parameters that play a role in the defini...

A neurocomputational model of decision and confidence in object recognition task.

Neural networks : the official journal of the International Neural Network Society
How does the brain process natural visual stimuli to make a decision? Imagine driving through fog. An object looms ahead. What do you do? This decision requires not only identifying the object but also choosing an action based on your decision confid...

An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While t...

Learning agile soccer skills for a bipedal robot with deep reinforcement learning.

Science robotics
We investigated whether deep reinforcement learning (deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies. We used deep RL to train a hu...

Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies.

eLife
Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimen...

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction.

PLoS computational biology
In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in opti...

Empirical modeling and prediction of neuronal dynamics.

Biological cybernetics
Mathematical modeling of neuronal dynamics has experienced a fast growth in the last decades thanks to the biophysical formalism introduced by Hodgkin and Huxley in the 1950s. Other types of models (for instance, integrate and fire models), although ...

Expressive power of ReLU and step networks under floating-point operations.

Neural networks : the official journal of the International Neural Network Society
The study of the expressive power of neural networks has investigated the fundamental limits of neural networks. Most existing results assume real-valued inputs and parameters as well as exact operations during the evaluation of neural networks. Howe...