Deep neural network representations align well with brain activity in the ventral visual stream. However, the primate visual system has a distinct dorsal processing stream with different functional properties. To test if a model trained to perceive 3D scene geometry aligns better with neural responses in dorsal visual areas, we trained a self-supervised geometry-aware recurrent neural network (GRNN) to predict novel camera views using a 3D feature memory. We compared GRNN to self-supervised baseline models that have been shown to align well with ventral regions using the large-scale fMRI Natural Scenes Dataset (NSD). We found that while the baseline models accounted better for ventral brain regions, GRNN accounted for a greater proportion of variance in dorsal brain regions. Our findings demonstrate the potential for using task-relevant models to probe representational differences across visual streams.

In February of 2020, I participated in the "On Consciousness" podcast with Bernie Baars and David Edelman. We talked about my work at The Neurosciences Institute (NSI) in La Jolla, California on the Darwin series of Brain-Based Devices, as well as my current research in neurorobotics. Unsurprisingly, the conversation turned to consciousness. I happened to mention that a page from my old lab notebook, which is pinned to a bulletin board in my office at UC Irvine, outlines a roadmap towards the creation of a Conscious Artifact. The key steps in this roadmap were laid out by Gerald Edelman, who was the director of the NSI at the time I was a research fellow there.

The new research from the University of Sheffield's Neuroscience Institute examines how the routine use of AI in healthcare could help to relieve the time and economic impact that common neurodegenerative diseases, such as Alzheimer's and Parkinson's, put on the NHS. The main risk factor for many neurological disorders is age, and with populations worldwide living longer than ever before, the number of people with a neurodegenerative disease is expected to hit unprecedented levels. The number of people living with Alzheimer's alone is predicted to treble to 115 million by 2050, posing a real challenge for the health system. The new study, published in the journal Nature Reviews Neurology, highlights how AI technologies, such as machine learning algorithms, can detect neurodegenerative disorders -- which cause part of the brain to die -- before progressive symptoms worsen. This can improve patients' chances of benefitting from successful disease-modifying treatment.

Upon meeting for the first time at a dinner at Stanford earlier this year, Fei-Fei Li and Jennifer Doudna couldn't help but note the remarkable parallels in their experiences as scientists. Stanford's Fei-Fei Li and Jennifer Doudna of UC Berkeley will discuss the ethics of artificial intelligence and CRISPR technology. Both women helped kickstart twin revolutions that are profoundly reshaping society in the 21st century – Li in the field of artificial intelligence (AI) and Doudna in the life sciences. Both revolutions can be traced back to 2012, the year that computer scientists collectively recognized the power of Li's approach to training computer vision algorithms and that Doudna drew attention to a new gene-editing tool known as CRISPR-Cas9 ("CRISPR" for short). Both pioneering scientists are also driven by a growing urgency to raise awareness about the ethical dangers of the technologies they helped create.

The Neurosciences Institute is a non-profit scientific research organization dedicated to learning about the brain for the benefit of humankind. Founded by the late Nobel Laureate Gerald M. Edelman, the Institute focuses its research on the principles underlying how we perceive and act upon the world, how we learn and remember, and how consciousness arises. The Neurosciences Institute is dedicated to increasing knowledge about how the brain works at the most fundamental levels. By that, we mean defining the fundamental principles of anatomy and physiology that enable the brain and nervous system to carry out their myriad functions. Although many facts are known about the nervous system from the molecular to the cellular to the tissue levels and new information is being discovered every day, a generally agreed-upon set of basic overall principles that would explain how we see, how we move, or how we are conscious remains elusive.