2 research outputs found
A Consistent Estimator of Nontrivial Stationary Solutions of Dynamic Neural Fields
Dynamics of neural fields are tools used in neurosciences to understand the activities generated by large ensembles of neurons. They are also used in networks analysis and neuroinformatics in particular to model a continuum of neural networks. They are mathematical models that describe the average behavior of these congregations of neurons, which are often in large numbers, even in small cortexes of the brain. Therefore, change of average activity (potential, connectivity, firing rate, etc.) are described using systems of partial different equations. In their continuous or discrete forms, these systems have a rich array of properties, among which is the existence of nontrivial stationary solutions. In this paper, we propose an estimator for nontrivial solutions of dynamical neural fields with a single layer. The estimator is shown to be consistent and a computational algorithm is proposed to help carry out implementation. An illustrations of this consistency is given based on different inputs functions, different kernels, and different pulse emission rate functions
On the Challenges and Opportunities in Generative AI
The field of deep generative modeling has grown rapidly and consistently over
the years. With the availability of massive amounts of training data coupled
with advances in scalable unsupervised learning paradigms, recent large-scale
generative models show tremendous promise in synthesizing high-resolution
images and text, as well as structured data such as videos and molecules.
However, we argue that current large-scale generative AI models do not
sufficiently address several fundamental issues that hinder their widespread
adoption across domains. In this work, we aim to identify key unresolved
challenges in modern generative AI paradigms that should be tackled to further
enhance their capabilities, versatility, and reliability. By identifying these
challenges, we aim to provide researchers with valuable insights for exploring
fruitful research directions, thereby fostering the development of more robust
and accessible generative AI solutions