A processed image representative of the types of images used in this study. Natural landscapes were transformed into binary images, ones made of black and white pixels, that were decomposed into different textures defined by specific statistics. (Image: Eugenio Piasini)
The human brain uses more energy than any other organ in the body, requiring as much as 20% of the body’s total energy. While this may sound like a lot, the amount of energy would be even higher if the brain were not equipped with an efficient way to represent only the most essential information within the vast, constant stream of stimuli taken in by the five senses. The hypothesis for how this works, known as efficient coding, was first proposed in the 1960s by vision scientist Horace Barlow.
Now, new research from the Scuola Internazionale Superiore di Studi Avanzati (SISSA) and the University of Pennsylvania provides evidence of efficient visual information coding in the rodent brain, adding support to this theory and its role in sensory perception. Published in eLife, these results also pave the way for experiments that can help understand how the brain works and can aid in developing novel artificial intelligence (AI) systems based on similar principles.
According to information theory—the study of how information is quantified, stored, and communicated—an efficient sensory system should only allocate resources to how it represents, or encodes, the features of the environment that are the most informative. For visual information, this means encoding only the most useful features that our eyes detect while surveying the world around us.
Vijay Balasubramanian, a computational neuroscientist at Penn, has been working on this topic for the past decade. “We analyzed thousands of images of natural landscapes by transforming them into binary images, made up of black and white pixels, and decomposing them into different textures defined by specific statistics,” he says. “We noticed that different kinds of textures have different variability in nature, and human subjects are better at recognizing those which vary the most. It is as if our brains assign resources where they are most necessary.”
As part of a major University-wide investment in science, engineering, and medicine, the Innovation in Data Engineering and Science Initiative aims to help Penn become a leader in developing data-driven approaches that can transform scientific discovery, engineering research, and technological innovation.
From smartphones and fitness trackers to social media posts and COVID-19 cases, the past few years have seen an explosion in the amount and types of data that are generated daily. To help make sense of these large, complex datasets, the field of data science has grown, providing methodologies, tools, and perspectives across a wide range of academic disciplines.
As part of its $750 million investment in science, engineering, and medicine, the University has committed to supporting the future needs of this field. To this end, the Innovation in Data Engineering and Science (IDEAS) initiative will help Penn become a leader in developing data-driven approaches that can transform scientific discovery, engineering research, and technological innovation.
“The IDEAS initiative is game-changing for our University,” says President Amy Gutmann. “This new investment allows us to boost our interdisciplinary efforts across campus, recruit phenomenal additional team members, and generate an even more sound foundation for discovery, experimentation, and design. This initiative is a clear statement that Penn is committed to taking data science head-on.”
“One of the unique things about data science and data engineering is that it’s a very horizontal technology, one that is going to be impacting every department on campus,” says George Pappas, Electrical and Systems Engineering Department chair. “When you have a horizontal technology in a competitive area, we have to figure out specific areas where Penn can become a worldwide leader.”
To do this, IDEAS aims to recruit new faculty across three research areas: artificial intelligence (AI) to transform scientific discovery, trustworthy AI for autonomous systems, and understanding connections between the human brain and AI.
In the area of neuroscience and how the human brain is similar to AI and machine learning approaches, research from PIK Professor Konrad Kording and Dani Bassett’sComplex Systems lab exemplifies the types of cross-disciplinary efforts that are essential for addressing complex questions. By recruiting additional faculty in this area, IDEAS will help Penn make strides in bio-inspired computing and in future life-changing discoveries that could address cognitive disorders and nervous system diseases.
Dani S. Bassett, J. Peter Skirkanich Professor in Bioengineering and in Electrical and Systems Engineering
Bassett runs the Complex Systems lab which tackles problems at the intersection of science, engineering, and medicine using systems-level approaches, exploring fields such as curiosity, dynamic networks in neuroscience, and psychiatric disease. They are a pioneer in the emerging field of network science which combines mathematics, physics, biology and systems engineering to better understand how the overall shape of connections between individual neurons influences cognitive traits.
Jason Burdick, Ph.D.
Jason A. Burdick, Robert D. Bent Professor in Bioengineering
Burdick runs the Polymeric Biomaterials Laboratory which develops polymer networks for fundamental and applied studies with biomedical applications with a specific emphasis on tissue regeneration and drug delivery. The specific targets of his research include: scaffolding for cartilage regeneration, controlling stem cell differentiation through material signals, electrospinning and 3D printing for scaffold fabrication, and injectable hydrogels for therapies after a heart attack.
César de la Fuente, Ph.D.
César de la Fuente, Presidential Assistant Professor in Bioengineering and Chemical & Biomedical Engineering in Penn Engineering and in Microbiology and Psychiatry in the Perelman School of Medicine
De la Fuente runs the Machine Biology Group which combines the power of machines and biology to prevent, detect, and treat infectious diseases. He pioneered the development of the first antibiotic designed by a computer with efficacy in animals, designed algorithms for antibiotic discovery, and invented rapid low-cost diagnostics for COVID-19 and other infections.
Carl June, M.D.
Carl H. June, Richard W. Vague Professor in Immunotherapy in the Perelman School of Medicine and member of the Bioengineering Graduate Group
June is the Director for the Center for Cellular Immunotherapies and the Parker Institute for Cancer Therapy and runs the June Lab which develops new forms of T cell based therapies. June’s pioneering research in gene therapy led to the FDA approval for CAR T therapy for treating acute lymphoblastic leukemia (ALL), one of the most common childhood cancers.
Vivek Shenoy, Ph.D.
Vivek Shenoy, Eduardo D. Glandt President’s Distinguished Professor in Bioengineering, Mechanical Engineering and Applied Mechanics (MEAM), and in Materials Science and Engineering (MSE)
Shenoy runs the Theoretical Mechanobiology and Materials Lab which develops theoretical concepts and numerical principles for understanding engineering and biological systems. His analytical methods and multiscale modeling techniques gain insight into a myriad of problems in materials science and biomechanics.
The highly anticipated annual list identifies researchers who demonstrated significant influence in their chosen field or fields through the publication of multiple highly cited papers during the last decade. Their names are drawn from the publications that rank in the top 1% by citations for field and publication year in the Web of Science™ citation index.
Bassett and Burdick were both on the Highly Cited Researchers list in 2019 and 2020.
The methodology that determines the “who’s who” of influential researchers draws on the data and analysis performed by bibliometric experts and data scientists at the Institute for Scientific Information™ at Clarivate. It also uses the tallies to identify the countries and research institutions where these scientific elite are based.
David Pendlebury, Senior Citation Analyst at the Institute for Scientific Information at Clarivate, said: “In the race for knowledge, it is human capital that is fundamental and this list identifies and celebrates exceptional individual researchers who are having a great impact on the research community as measured by the rate at which their work is being cited by others.”
The full 2021 Highly Cited Researchers list and executive summary can be found online here.
Our final Penn Bioengineering seminar of the fall semester will take place this Thursday. Keep an eye on the BE events calendar for upcoming spring seminars.
Speaker: Bijan Pesaran, Ph.D.
Professor
Neural Science
New York University
Date: Thursday, December 16, 2021
Time: 3:30-4:30 PM EST
Zoom – check email for link
Room: Moore 216
Abstract: Neural engineering is enjoying an era of transformative growth. Classical methods that dominated the neurosciences for decades are being replaced by powerful new technologies. In this talk, I will discuss how to engineer electrical and optical interfaces to the primate brain. I will first present work on electrode interfaces that stimulate and record at the surface of and within the brain. I will show how simultaneously measuring and manipulating neurons immediately beneath electrode contacts during behavior delivers ground-truth data. The results have implications for electrode interface design and new generations of implantable biomedical devices. I will then turn to optical neural interfaces. Two-photon fluorescence microscopy images the activity of neurons expressing genetically-encoded calcium indicators and is most often performed in small animal models, such as the mouse, worm and fly. I will present a cellular-resolution robotic imaging platform to investigate the non-human primate brain at scale. I will finish by discussing potential applications of this technology to a range of scientific and clinical goals.
Bijan Pesaran Bio: Bijan Pesaran is interested in understanding large-scale circuits in the primate brain and how to engineer novel brain-based therapies. Bijan completed his undergraduate degree in Physics at the University of Cambridge, UK. After a year in the Theoretical Physics department at Bell Labs Murray Hill, he went on to earn his PhD in Physics at the California Institute of Technology. He then made the switch to neuroscience as a postdoctoral fellow in Biology at Caltech. Bijan has been on the faculty at New York University since 2006. He is currently a Professor of Neural Science in the Center for Neural Science. In 2013, he was a CV Starr Visiting Scholar at the Princeton Neuroscience Institute at Princeton University. Among other honors and awards, Bijan has received a Burroughs-Wellcome Career Award in the Biomedical Sciences, a Sloan Research Fellowship, a McKnight Scholar Award, the National Science Foundation CAREER Award and is a member of the Simons Collaboration for the Global Brain.
When Nathan Francis Mossell graduated in 1882, he became the first African American to earn a medical degree from Penn. He soon became a prominent African American physician, the first to be elected to the Philadelphia County Medical Society. He helped found the Frederick Douglass Memorial Hospital and Training School, which treated Black patients and helped train the next generation of Black doctors and nurses.
“Dr. Mossell was truly inspiring. He had to fight for everything, yet never reneged on his principles. He pretty much started a hospital and was a major champion for the advancement of equality for African Americans,” Kording said. “In my research, where I study how intelligence works, I am inspired by scholars like him who combine many different insights. He was a wonderful man, and I will be proud to carry his name.”
Cathy and Marc Lasry Professor Vijay Balasubramanian at Penn’s BioPond.
A study published in PLOS Computational Biology describes a new model for how the olfactory system discerns unique odors. Researchers from the University of Pennsylvania found that a simplified, statistics-based model can explain how individual odors can be perceived as more or less similar from others depending on the context. This model provides a starting point for generating new hypotheses and conducting experiments that can help researchers better understand the olfactory system, a complex, crucial part of the brain.
The sense of smell, while crucial for things like taste and hazard avoidance, is not as well studied as other senses. Study co-author Vijay Balasubramanian, a theoretical physicist with an interest in how living systems process information, says that olfaction is a prime example of a complex information-processing system found in nature, as there are far more types of volatile molecules—on the scale of tens or hundreds of thousands—than there are receptor types in the nose to detect them, on the scale of tens to hundreds depending on the species.
“Every molecule can bind to many receptors, and every receptor can bind to many molecules, so you get this combinatorial mishmash, with the nose encoding smells in a way that involves many receptor types to collectively tell you what a smell is,” says Balasubramanian. “And because there are many fewer receptor types than molecular species, you basically have to compress a very high dimensional olfactory space into a much lower dimensional space of neural responses.”
The prestigious APS Fellowship Program signifies recognition by one’s professional peers. Each year, no more than one half of one percent of the APS membership is recognized with this distinct honor. Bassett’s election and groundbreaking work in biological physics and network science will be recognized through presentation of a certificate at the APS March Meeting.
Bassett is a pioneer in the field of network neuroscience, an emerging subfield which incorporates elements of mathematics, physics, biology and systems engineering to better understand how the overall shape of connections between individual neurons influences cognitive traits. They lead the Complex Systems lab which tackles problems at the intersection of science, engineering, and medicine using systems-level approaches, exploring fields such as curiosity, dynamic networks in neuroscience, and psychiatric disease.
Bassett recently collaborated with Penn artist-in-residence Rebecca Kamen and other scholars on an interdisciplinary art exhibit on the creative process in art and science at the Katzen Art Center at American University. They have also published research modeling different types of curiosity and exploring gender-based citation bias in neuroscience publishing.
“I’m thrilled and humbled to receive this honor from the American Physical Society,” says Bassett. “I am indebted to the many fantastic mentees, colleagues, and mentors that have made my time in science such an exciting adventure. Thank you.”
Jennifer E. Phillips-Cremins (upper left) and members of her lab.
Each year, the National Institutes of Health (NIH) recognizes exceptionally creative scientists through its High-Risk, High-Reward Research Program. The four awards granted by this program are designed to support researchers whose “out of the box” and “trailblazing” ideas have the potential for broad impact.
Jennifer E. Phillips-Cremins, Associate Professor and Dean’s Faculty Fellow in Penn Engineering’s Department of Bioengineering and the Perelman School of Medicine’s Department of Genetics, is one such researcher. As a recipient of an NIH Director’s Pioneer Award, she will receive $3.5 million over five years to support her work on the role that the physical folding of chromatin plays in the encoding of neural circuit and synapse properties contributing to long-term memory.
Phillips-Cremins’ award is one of 106 grants made through the High-Risk, High-Reward program this year, though she is only one of 10 to receive the Pioneer Award, which is the program’s largest funding opportunity.
“The science put forward by this cohort is exceptionally novel and creative and is sure to push at the boundaries of what is known,” said NIH Director Francis S. Collins.
Phillips-Cremins’ research is in the general field of epigenetics, the molecular and structural modifications that allow the genome — an identical copy of which is found in each cell — to express genes differently at different times and in different parts of the body. Within this field, her lab focuses on higher-order folding patterns of the DNA sequence, which bring distant sets of genes and regulatory elements into close proximity with one another as they are compressed inside the cell’s nucleus.
Previous work from the Cremins lab has investigated severe genome misfolding patterns common across a class of genetic neurological disorders, including fragile X syndrome, Huntington’s disease, ALS and Friedreich’s ataxia.
With the support of the Pioneer Award, she and the members of her lab will extend that research to a more fundamental question of neuroscience: how memory is encoded over decades, despite the rapid turnover of the relevant proteins and RNA sequences within the brain’s synapses.
“Our long-term goals are to understand how, when and why pathologic genome misfolding leads to synaptic dysfunction by way of disrupted gene expression,” said Phillips-Cremins, “as well as to engineer the genome’s structure-function relationship to reverse pathologic synaptic defects in debilitating neurological diseases.”
A collaborative study finds that deeper regions of the brain encode visual information more slowly, enabling the brain to identify fast-moving objects and images more accurately and persistently.
by Erica K. Brockmeier
Busy pedestrian crossing at Hong Kong
New research from the University of Pennsylvania, the Scuola Internazionale Superiore de Studi Avanzati (SISSA), and KU Leuven details the time scales of visual information processing across different regions of the brain. Using state-of-the-art experimental and analytical techniques, the researchers found that deeper regions of the brain encode visual information slowly and persistently, which provides a mechanism for explaining how the brain accurately identifies fast-moving objects and images. The findings were published in Nature Communications.
Understanding how the brain works is a major research challenge, with many theories and models developed to explain how complex information is processed and represented. One area of particular interest is vision, a major component of neural activity. In humans, for example, there is evidence that around half of the neurons in the cortex are related to vision.
Researchers are eager to understand how the visual cortex can process and retain information about objects in motion in a way that allows people to take in dynamic scenes while still retaining information about and recognizing the objects around them.
“One of the biggest challenges of all the sensory systems is to maintain a consistent representation of our surroundings, despite the constant changes taking place around us. The same holds true for the visual system,” says Davide Zoccolan, director of SISSA’s Visual Neuroscience Laboratory. “Just look around us: objects, animals, people, all on the move. We ourselves are moving. This triggers rapid fluctuations in the signals acquired by the retina, and until now it was unclear whether the same type of variations apply to the deeper layers of the visual cortex, where information is integrated and processed. If this was the case, we would live in tremendous confusion.”
Experiments using static stimuli, such as photographs, have found that information from the sensory periphery are processed in the visual cortex according to a finely tuned hierarchy. Deeper regions of the brain then translate this information about visual scenes into more complex shapes, objects, and concepts. But how this process works in more dynamic, real-world settings is not well understood.
To shed light on this, the researchers analyzed neural activity patterns in multiple visual cortical areas in rodents while they were being shown dynamic visual stimuli. “We used three distinct datasets: one from SISSA, one from a group in KU Leuven led by Hans Op de Beeck and one from the Allen Institute for Brain Science in Seattle,” says Zoccolan. “The visual stimuli used in each were of different types. In SISSA, we created dedicated video clips showing objects moving at different speeds. The other datasets were acquired using various kinds of clips, including from films.”
Next, the researchers analyzed the signals registered in different areas of the visual cortex through a combination of sophisticated algorithms and models developed by Penn’s Eugenio Pasini and Vijay Balasubramanian. To do this, the researchers developed a theoretical framework to help connect the images in the movies to the activity of specific neurons in order to determine how neural signals evolve over different time scales.
“The art in this science was figuring out an analysis method to show that the processing of visual images is getting slower as you go deeper and deeper in the brain,” says Balasubramanian. “Different levels of the brain process information over different time scales; some things could be more stable, some quicker. It’s very hard to tell if the time scales across the brain are changing, so our contribution was to devise a method for doing this.”
Vijay Balasubramanian is the Cathy and Marc Lasry Professor in the Department of Physics and Astronomy in the School of Arts & Sciences and a member of the Penn Bioengineering Graduate Group at the University of Pennsylvania.
The Lindback Awards, announced annually, are the most prestigious teaching awards that full-time faculty members at the University can receive.
Meaney is the Solomon R. Pollack Professor in Bioengineering and Senior Associate Dean of Penn Engineering and his research areas span from traumatic brain injury to brain network theory. He received his M.S. and Ph.D. in Bioengineering and Biomedical Engineering from Penn Engineering.
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