Over the past seven years, Bar-Ilan has seen a 30% increase in total student enrollment, marking the highest growth rate among the country’s major research universities.
Bar-Ilan University has climbed to second place among Israel’s research universities, surpassing the Hebrew University of Jerusalem and now trailing only Tel Aviv University, according to a new Central Bureau of Statistics (CBS) 2025 Higher Education Report.
The shift marks a major redistribution within Israel’s academic landscape. Over the past seven years, Bar-Ilan has seen a 30% increase in total student enrollment, marking the highest growth rate among the country’s major research universities.
The data highlighted a strengthening of the university’s research capabilities, with nearly half of the recent enrollment surge occurring in graduate and doctoral programs. The university has seen growth across key areas, including engineering, medicine, nanotechnology, cybersecurity, artificial intelligence, and the exact sciences.
Photo of Bar-Ilan University campus, March, 2025. (credit: COURTESY BAR ILAN)
Is strategy the secret tool behind Bar-Ilan’s success?
The university pointed to a long-term strategic plan aimed at broadening accessibility and investing heavily in cutting-edge research infrastructure as the catalysts for this success.
“Through a deep, broad, and consistent strategic process, Bar-Ilan University has achieved an unprecedented milestone of becoming the second largest university in Israel, influencing the lives of hundreds of thousands of young Israelis from all walks of life,” said Prof. Arie Zaban, president of Bar-Ilan University.
The strategic shift has included the introduction of new academic offerings, such as new engineering tracks and a six-year medical program. Furthermore, the university has fostered interdisciplinary collaboration to connect scientific research directly with practical needs in areas like health care and technology.
Can the shallow brain architecture compete with deep learning? (Video)
Neural network learning techniques stem from the dynamics of the brain. However, these two scenarios, brain learning and deep learning, are intrinsically different. One of the most prominent differences is the number of layers each one possesses. Deep learning architectures typically consist of numerous layers that can be increased to hundreds, enabling efficient learning of complex classification tasks. Contrastingly, the brain consists of very few layers, yet despite its shallow architecture and noisy and slow dynamics, it can efficiently perform complex classification tasks.
The key question driving new research is the possible mechanism underlying the brain’s efficient shallow learning — one that enables it to perform classification tasks with the same accuracy as deep learning. In an article just published in Physica A, researchers from Bar-Ilan University in Israel show how such shallow learning mechanisms can compete with deep learning. “Instead of a deep architecture, like a skyscraper, the brain consists of a wide shallow architecture, more like a very wide building with only very few floors,” said Prof. Ido Kanter, of Bar-Ilan’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the research.
“The capability to correctly classify objects increases where the architecture becomes deeper, with more layers. In contrast, the brain’s shallow mechanism indicates that a wider network better classifies objects,” said Ronit Gross, an undergraduate student and one of the key contributors to this work. “Wider and higher architectures represent two complementary mechanisms,” she added. Nevertheless, the realization of very wide shallow architectures, imitating the brain’s dynamics, requires a shift in the properties of advanced GPU technology, which is capable of accelerating deep architecture, but fails in the implementation of wide shallow ones.
Newswise — Severe Combined Immunodeficiencies (SCIDs) are a group of debilitating primary immunodeficiency disorders, primarily caused by genetic mutations that disrupt T-cell development. SCID can also affect B-cell and natural killer cell function and counts. Left untreated, SCID proves fatal within the first year of life. The conventional treatment for SCID patients involves allogeneic hematopoietic stem cell transplantation (HSCT), but the challenges of finding compatible donors and potential complications like graft-versus-host disease (GVHD) pose significant hurdles in this approach.
A groundbreaking solution has emerged with the advent of genome editing (GE), particularly using CRISPR-Cas9 technology. This cutting-edge gene therapy research offers hope for many genetic disorders such as SCID. The CRISPR-Cas9 system creates site-specific double-strand breaks in the DNA, allowing for precise gene editing. The repair process can either disrupt a specific gene or correct it, potentially targeting nearly any gene in the genome. This development opens the door to therapeutic interventions for a wide range of genomic diseases.
One promising genome-editing approach, CRISPR-Cas9 Homology-directed repair (HDR)-mediated GE, offers the potential for precise gene insertion. In certain subtypes of SCID, an alternative to HSCT can involve conventional CRISPR-Cas9 HDR-mediated gene insertion, but it carries inherent risks, especially in cases like RAG2-SCID. RAG2 is nuclease involved in DNA cleavage during lymphocyte development, and CRISPR-Cas9 HDR-mediated gene insertion may lead to uncontrolled RAG2 nuclease activity and harmful structural variations.
In response, researchers from Bar-Ilan University in Israel propose a novel replacement strategy, termed GE x HDR 2.0: Find and Replace. This approach, outlined in a paper published today in Nature Communications, combines CRISPR-Cas9-mediated genome editing with recombinant adeno-associated serotype 6 (rAAV6) DNA donor vectors to precisely replace the RAG2 coding sequence while preserving regulatory elements. This strategy can be applied also to other genes with hot spot regions for disease-causing mutations.
Dr. Ayal Hendel, of Bar-Ilan University’s Goodman Faculty of Life Sciences, emphasized, “Our innovation hinges on a crucial insight: to efficiently trigger CRISPR-Cas9 HDR-mediated GE for precise coding sequence replacement, it’s essential to separate the distal homology arm from the cleavage site and align it with the sequence immediately downstream of the segment needing replacement. In this process, elongating the distal homology arm length in the donor is of paramount importance. By preserving endogenous regulatory elements and intronic sequences, our approach faithfully reproduces natural gene expression levels, thus reducing the associated risks of unregulated gene expression. This groundbreaking technique, which involves replacing entire coding sequences or exons while retaining critical regulatory elements, brings hope to patients with RAG2-SCID and holds promise for the treatment of various other genetic disorders.”
Newswise — A new study conducted by researchers at Bar-Ilan University in Israel has shed light on the long-term impact of COVID-19 on the quality of life among different ethnic groups in the country. The study, part of a larger cohort project, highlights a significant discrepancy between Arabs and Druze, and Jews, with the two former groups experiencing a more pronounced decline in quality of life one year after infection.
In this cohort study, researchers regularly followed up with individuals who had been infected with the SARS-CoV-2 virus to assess various aspects of their health. The findings, published in the International Journal of Public Health, demonstrate that the disparity in quality of life between ethnic groups remained even after accounting for socio-economic differences.
“We embarked on this study to investigate the long-term effects of COVID-19 on minority groups in Israel given existing health inequalities in the country,” explains the study’s lead author Prof. Michael Edelstein, of the Azrieli Faculty of Medicine of Bar-Ilan University. Well-being was assessed using the EQ-5D quality of life instrument measuring five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. “Our results revealed that, while pre-COVID quality of life among Jews, Arabs, and Druze in our study was initially comparable, at the 12-month mark after infection the Arab and Druze participants reported a quality of life 11% lower than their Jewish counterparts,” adds Edelstein.
The study’s findings carry important implications for understanding the enduring impact of COVID-19 beyond the acute phase of the pandemic. The research suggests that certain populations may be more susceptible to long-term symptoms and a diminished quality of life, exacerbating pre-existing health disparities. These findings not only have implications for Israel, but also provide valuable insights for global efforts to address the long-term consequences of the COVID-19 pandemic.
“The significance of our research lies in the ability to shed light on the ongoing impact of COVID-19, even as the disease transitions from a public health emergency to a persistent health concern,” emphasizes Prof. Edelstein. “By understanding how the virus affects different communities, we can work towards developing targeted interventions and support systems to mitigate the long-term effects on quality of life.”
Dr. Jelte Elsinga, from Amsterdam University Medical Centre in Holland, led the analysis. The study was partially funded by a donation from the Harvey Goodstein Charitable Foundation.
As part of the larger cohort project, multiple papers have already been published and several more are in progress. Moving forward, the research team will continue to explore the role of vaccines in mitigating the long-term impact of COVID-19, as well as investigate the pandemic’s economic consequences on employment and income among the study participants.
Newswise — The earliest artificial neural network, the Perceptron, was introduced approximately 65 years ago and consisted of just one layer. However, to address solutions for more complex classification tasks, more advanced neural network architectures consisting of numerous feedforward (consecutive) layers were later introduced. This is the essential component of the current implementation of deep learning algorithms. It improves the performance of analytical and physical tasks without human intervention, and lies behind everyday automation products such as the emerging technologies for self-driving cars and autonomous chat bots.
The key question driving new research published today in Scientific Reports is whether efficient learning of non-trivial classification tasks can be achieved using brain-inspired shallow feedforward networks, while potentially requiring less computational complexity. “A positive answer questions the need for deep learning architectures, and might direct the development of unique hardware for the efficient and fast implementation of shallow learning,” said Prof. Ido Kanter, of Bar-Ilan’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the research. “Additionally, it would demonstrate how brain-inspired shallow learning has advanced computational capability with reduced complexity and energy consumption.”
“We’ve shown that efficient learning on an artificial shallow architecture can achieve the same classification success rates that previously were achieved by deep learning architectures consisting of many layers and filters, but with less computational complexity,” said Yarden Tzach, a PhD student and contributor to this work. “However, the efficient realization of shallow architectures requires a shift in the properties of advanced GPU technology, and future dedicated hardware developments,” he added.
For years brain dynamics and machine learning development were researched independently, however recently brain dynamics has been revealed as a source for new types of efficient artificial intelligence.
Newswise — The brain is composed of millions of billions of neurons which communicate with each other. Each neuron collects its many inputs and transmits a spike to its connecting neurons. The dynamics of such large and highly interconnected neural networks is the basis of all high order brain functionalities.
In an article published today in the journal Scientific Reports, a group of scientists has experimentally demonstrated that there are frequent periods of silence in which a neuron fails to respond to its inputs. As opposed to elecronic devices, which are fast and reliable, the brain is composed of unreliable neurons. “A logic-gate always gives the same output to the same input, otherwise electronic devices like cellphones and computers, which are composed of many billions of interconnected logic-gates, wouldn’t function well,” said Prof. Ido Kanter, of Bar-Ilan University’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the study. “Comparing the unreliability of the brain to a computer or cellphone: one time your computer answers 1+1=2 and other times 1+1=5, or dialing 7 in your cellphone many times can result in 4 or 9. Silencing periods would appear to be a major disadvantage of the brain, but our latest findings have shown otherwise.”
Contrary to what one might think, Kanter and team have demonstrated that neuronal silencing periods are not a disadvantage representing biological limitations, but rather an advantage for temporal sequence identification. “Assume you would like to remember a phone number, 0765…,” said Yuval Meir, a co-author of the study. “Neurons which were active when the digit 0 was presented might be silenced when the next digit 7 is presented, for example. Consequently, each digit is trained on a different dynamically created sub-network, and this silencing mechanism enables our brain to identify sequences efficiently.”
The brain silencing mechanism is a proposed source for a new AI mechanism, and in addition has been demonstrated as the origin for a new type of cryptosystem for handwriting recognition at automated teller machines (ATMs). This cryptosystem allows the user to write his personal identification number (PIN) on an electronic board rather than clicking a PIN into the ATM. The sequence identification developed by Kanter and team, based on neuronal silencing periods, is not only capable of identifying the correct PIN but also the user’s personal handwriting style and the timing in which each digit of the PIN is written on the board. These added features act as safeguards against stolen cards, even if a thief knows the user’s PIN.
This latest research by Kanter and team shows that it is not always beneficial to improve the unreliablilty of stuttered neurons in the brain, because they have advantages for higher brain functions.
