Theoretical Neuron Networks

At the mention of  “neuroscience,” what comes to mind is often the image of brain scans, EEGs (brainwave readers), and a flurry of lab coats dissecting and analyzing brain tissue.  Of course, this is a crucial part of neuroscience, responsible for the discovery of pathways in the brain, the sources of bodily functions, neuron classes… the list continues.  But when it comes to understanding how the connections between neurons can lead to complex processes, this work often leaves the wet lab, and at Fordham, finds itself in the computer simulations in the systems neurobiology department.

To understand how neurons connect and interact within the brain, professor of neurophysics at Lincoln Center, Dr. Badr Albanna, is using computer simulations to emulate potential physical neuron circuits, as opposed to directly searching for and studying them in test subjects such as mice.  A graduate of University of California Berkeley, his background and PhD in physics has lent him to become interested in theoretical neuroscience.  His work now centers on creating a network that is able to match within a sample with delay, where a program can consistently choose a given object out of a random assortment of objects, displayed in different contexts and at different times.  For instance, in a sequential display of a set of toys, a mouse can be conditioned to always choose the “correct” toy.  The same goes for humans.  But for a computer simulating a number of neurons much less than that which we possess in our biological brains, building the circuitry required to detect a unique object in a series of displays across different time frames is difficult yet rewarding, as it offers insights into potential methods our brains function.  Currently, the project is working on creating a repository that acts as short-term memory, as well as a reliable method for recognizing and separating objects from their backgrounds.

The research is crucial for the development in a sector of artificial intelligence that has often been neglected, according to Dr. Albanna.  Artificial intelligence today is still based largely on the concept that neurons in the brain are binary: they are either sending signals or not.  Simulations that involve newly discovered neuron types that possess individual properties similar to that of circuits can lay the foundations for the next generation of artificial intelligence, especially one that is more adept at displaying and/or performing behaviors.  For instance, when it comes to auditory faculties, our ears can automatically parse out unwanted background noise and focus on a specific set of frequencies.  This is difficult even with large amounts of artificial learning in artificial intelligence networks, and is one potential area where the research results can be applied to.

For Dr. Albanna, the joy in the research lies simply in inspiring others to learn more about the ways in which our brains function.  He hopes that his work will raise more awareness about neuroscience in general and theoretical networks in particular.  As Dr. Albanna prepares to transition to the University of Pittsburgh next year, his work at Fordham will be sure to excite generations of neuroscience majors to come.

By Cary Wang, FCLC ‘23


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