Nanotechnology and nanoengineering are producing significant scientific and technological advances in diverse fields including medicine and physiology. In a broad sense, they can be defined as the science and engineering involved in the design, synthesis, characterization, and application of materials and devices whose smallest functional organization in at least one…

The integration and propagation of information in the brain depend on the interplay between structural and dynamical properties across many scales of organization. This interplay happens in molecular and diffusion interactions in neurons and other neural cells, and at physiological scales in individual cells, networks of cells, and eventually in…

In general, the tremendous success and achievements of the many flavors of machine learning (ML) are based on variations of gradient descent algorithms that minimize some version of a cost or loss function. At their core, existing algorithms take advantage of the stochastic convergence of weights in neural networks, with…

There are many methods and tools available to study complex systems. But understanding how the structural connectivity of a network constrains the dynamics the network is able to support is a difficult question to formulate and answer, and remains a very active and open area of research. In a recent…

Everything the human brain is capable of is the product of a complex symphony of interactions between many distinct signaling events and myriad of individual computations. The brain’s ability to learn, connect abstract concepts, adapt, and imagine, are all the result of this vast combinatorial computational space. …

A number of years ago I wrote a commentary in Frontiers in Computational Neuroscience that I believe is even more important today then back when I originally wrote it. The basic argument, which I will go into more detail below, is that — beginning in grounded experimentally validated ‘axioms’ —…

This (somewhat) technical article is based on the introduction of a recent peer-reviewed published paper from our group, and summarizes some of the work done over the years in the field in an attempt to identify and map the inferred connectivity of biological neuronal networks. The ability to do so…

Constructing generalizable models in neuroscience is challenging because neural systems are typically complex in the sense that they are dynamic — constantly changing — and composed of numerous interacting components that collectively participate to produce emergent behaviors: system properties that are not shared by the building block constituent elements that…