Dynamic clustering and classification of handwritten digits without prior training. Image credit: Christopher White and Gabriel A. Silva

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…

The transparent nematode Caenorhabditis elegans — a neuroscience rockstar. Image credit: Getty.

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…

I’ll be making use of curated lists to better organize my articles. There are three lists I’ve created at the moment …

‘The Technical Paper Reboot’ — Short adaptations of some of our technical papers, in particular highlighting the limits and boundaries of neuroscience.

‘Articles from Forbes’ — Articles originally published in Forbes that I have written as a regular contributor.

‘Medium Originals’ — Pretty self explanatory. But still (mostly) focused on neuroscience, the brain, machine learning, and related topics.

Feel free to drop me a line with any feedback or thoughts.

Gabe Silva

Image credit: Getty

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…

Identifying the causal connectivity of biological neural networks is key to understanding how the brain works — but it’s hard. Image credit: Getty.

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…

Professor Terry Sejnowski at the Salk Institute for Biological Sciences and the University of California San Diego. Image credit: Terrance J. Sejnowski.

The ‘Thinkers and Innovators’ series explores the science and philosophy of the brain and mind with some of the world’s foremost forward thinking experts. It also explores technologies used for studying and interfacing with the brain, as well as technologies motivated by the brain, such as machine learning and artificial…

Gabriel A. Silva

Professor of Bioengineering and Neurosciences, University of California San Diego

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