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Category: neuroscience

The circuit that lets your brain think and see

Nuttida Rungratsameetaweemana is challenging a story neuroscience has told for decades. According to the conventional account, our eyes collect raw information and relay it through a series of nerves and waystations that lead deep into the brain, eventually reaching the cortex. There, the thinking begins as information is processed and put to use for higher tasks such as reasoning, judgment and decision-making.

Her group’s work is complicating that account. Last year, the team published fMRI scans showing unexpected levels of activity in the earliest visual areas of the cortex, the regions that first receive visual signals. Rather than passively relaying what the eyes take in, those early areas seemed to process the same information differently depending on what the research participant was doing. When asked to sort shapes by one set of rules, a participant’s early visual system behaved one way. When asked to apply a different set of rules to the same shape, it behaved differently.

In a new paper published today in PLOS Biology, Rungratsameetaweemana and her team at Columbia Engineering show how the brain might pull this off. They built a simple neural network that follows many of the rules that govern real brains. Like the brain, their model contained one class of neurons that drive other neurons to fire and another class that suppress firing.

Cognitive flexibility problems may arise months before memory impairment in Alzheimer’s

When most people think about Alzheimer’s disease, memory loss is usually the first thing that comes to mind. Forgetting a loved one’s name, missing appointments or repeatedly misplacing everyday items are often considered early warning signs. But what if the disease begins affecting the brain long before memory problems become noticeable? New research from scientists at Texas A&M Health suggests that another change in brain function may appear even earlier: difficulty adapting when circumstances change.

In a recent study published in Nature Communications, researchers found that animal models with Alzheimer’s-related brain changes developed problems with cognitive flexibility months before they showed signs of memory impairment. Cognitive flexibility refers to the brain’s ability to adjust behavior, learn new rules and adapt when situations change.

“We found that this function was impaired before we could detect deficits in spatial memory,” said neuroscientist Jun Wang, Ph.D., professor in the Texas A&M University Naresh K. Vashisht College of Medicine at Texas A&M Health.

Fear-learning circuit shows how stress disrupts brain’s ability to suppress trauma

Fear is often thought of as a negative emotion but is actually a natural protective response to perceived threats or danger. It helps us survive. When we experience a situation that causes fear, it becomes stored in our brain as a fear memory. These fear memories prevent us from touching a hot stove after being burned or from stepping onto a busy street.

What about fear memories that take over? Post-traumatic stress disorder, or PTSD, is caused by severe acute or chronic stress that disrupts the learning process designed to suppress fear memories. These memories then begin to negatively affect a person’s quality of life.

Typically, our fear memories can be suppressed through extinction learning. The original memory or fear isn’t forgotten, but a new memory is formed and suppresses the original fear memory. However, extinction learning can become tricky in situations that involve traumatic memories.

Trisomic rescue via allele-specific multiple chromosome cleavage using CRISPR-Cas9 in trisomy 21 cells

Human trisomy 21, responsible for Down syndrome, is the most prevalent genetic cause of cognitive impairment and remains a key focus for prenatal and preimplantation diagnosis. However, research directed toward eliminating supernumerary chromosomes from trisomic cells is limited. The present study demonstrates that allele-specific multiple chromosome cleavage by clustered regularly interspaced palindromic repeats Cas9 can achieve trisomy rescue by eliminating the target chromosome from human trisomy 21 induced pluripotent stem cells and fibroblasts. Unlike previously reported allele-nonspecific strategies, we have developed a comprehensive allele-specific (AS) Cas9 target sequence extraction method that efficiently removes the target chromosome. The temporary knockdown of DNA damage response genes increases the chromosome loss rate, while chromosomal rescue reversibly restores gene signatures and ameliorates cellular phenotypes. Additionally, this strategy proves effective in differentiated, nondividing cells. We anticipate that an AS approach will lay the groundwork for more sophisticated medical interventions targeting trisomy 21.

Keywords: CRISPR/Cas; Down syndrome; allele specificity; chromosome cut; chromosome loss; human trisomy 21.

© The Author(s) 2025. Published by Oxford University Press on behalf of National Academy of Sciences.

Consciousness likely not unique to earthlings, paper says

Does consciousness depend on flesh and blood?

The answer is almost certainly no, according to Eric Schwitzgebel, a distinguished professor of philosophy at the University of California, Riverside.

In a new working paper, Schwitzgebel and Jeremy Pober, a former UCR graduate student who is now a postdoctoral researcher at the University of Lisbon, assert that consciousness is likely possible in life forms made of much different stuff. Think of the five-limbed alien with a rock-like exterior in the recent blockbuster movie “Project Hail Mary.”

Research progress on 40 Hz sensory stimulation for the treatment of Alzheimer’s disease

Abstract:

Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder characterized by β-amyloid (Aβ) deposition, tau protein hyperphosphorylation, and synaptic dysfunction. In recent years, 40 Hz sensory stimulation—including visual, auditory, and multimodal modalities—has emerged as a novel, non-invasive intervention demonstrating potential efficacy in both animal models and preliminary clinical studies. Preclinical evidence indicates that such stimulation can markedly reduce cerebral Aβ burden (by approximately 37%–53%), inhibit tau protein phosphorylation, enhance neuronal network synchrony and synaptic plasticity, and improve learning and memory performance. Limited human trials suggest that 40 Hz sensory stimulation is safe and well tolerated in patients with mild cognitive impairment (MCI) and early-stage AD, with a slowing trend in cognitive scale score decline following intervention. This review summarizes the mechanisms of action, experimental evidence from animal models, and advances in clinical application of 40 Hz sensory stimulation in AD prevention and treatment. It further explores the potential for multimodal combination therapies integrating sensory stimulation with cognitive training, pharmacological interventions, and lifestyle modifications, and addresses challenges such as optimal timing of intervention and the influence of ambient electromagnetic fields in real-world settings. Current evidence supports 40 Hz sensory stimulation as a feasible, multi-target, and safe adjunctive intervention; however, its efficacy and applicability must be verified through multicenter, randomized controlled trials with long-term follow-up.

Multifunctional Organic Materials, Devices, and Mechanisms for Neuroscience, Neuromorphic Computing, and Bioelectronics

Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks. Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems. However, developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge. Organic computational materials offer affordable, biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching. Here, the review investigates the advancements made in the development of organic neuromorphic devices. This review explores resistive switching mechanisms such as interface-regulated filament growth, molecular-electronic dynamics, nanowire-confined filament growth, and vacancy-assisted ion migration, while proposing methodologies to enhance state retention and conductance adjustment. The survey examines the challenges faced in implementing low-power neuromorphic computing, e.g., reducing device size and improving switching time. The review analyses the potential of these materials in adjustable, flexible, and low-power consumption applications, viz. biohybrid spiking circuits interacting with biological systems, systems that respond to specific events, robotics, intelligent agents, neuromorphic computing, neuromorphic bioelectronics, neuroscience, and other applications, and prospects of this technology.

Keywords: Brain-inspired neuromorphic computing; Neuromorphic bioelectronics; Neuroscience; Organic materials; Resistive switching mechanisms.

© 2025. The Author(s).

Neuromorphic Sentiment Analysis Using Spiking Neural Networks

Over the past decade, the artificial neural networks domain has seen a considerable embracement of deep neural networks among many applications. However, deep neural networks are typically computationally complex and consume high power, hindering their applicability for resource-constrained applications, such as self-driving vehicles, drones, and robotics. Spiking neural networks, often employed to bridge the gap between machine learning and neuroscience fields, are considered a promising solution for resource-constrained applications. Since deploying spiking neural networks on traditional von-Newman architectures requires significant processing time and high power, typically, neuromorphic hardware is created to execute spiking neural networks. The objective of neuromorphic devices is to mimic the distinctive functionalities of the human brain in terms of energy efficiency, computational power, and robust learning. Furthermore, natural language processing, a machine learning technique, has been widely utilized to aid machines in comprehending human language. However, natural language processing techniques cannot also be deployed efficiently on traditional computing platforms. In this research work, we strive to enhance the natural language processing traits/abilities by harnessing and integrating the SNNs traits, as well as deploying the integrated solution on neuromorphic hardware, efficiently and effectively. To facilitate this endeavor, we propose a novel, unique, and efficient sentiment analysis model created using a large-scale SNN model on SpiNNaker neuromorphic hardware that responds to user inputs. SpiNNaker neuromorphic hardware typically can simulate large spiking neural networks in real time and consumes low power. We initially create an artificial neural networks model, and then train the model using an Internet Movie Database (IMDB) dataset. Next, the pre-trained artificial neural networks model is converted into our proposed spiking neural networks model, called a spiking sentiment analysis (SSA) model. Our SSA model using SpiNNaker, called SSA-SpiNNaker, is created in such a way to respond to user inputs with a positive or negative response. Our proposed SSA-SpiNNaker model achieves 100% accuracy and only consumes 3,970 Joules of energy, while processing around 10,000 words and predicting a positive/negative review. Our experimental results and analysis demonstrate that by leveraging the parallel and distributed capabilities of SpiNNaker, our proposed SSA-SpiNNaker model achieves better performance compared to artificial neural networks models. Our investigation into existing works revealed that no similar models exist in the published literature, demonstrating the uniqueness of our proposed model. Our proposed work would offer a synergy between SNNs and NLP within the neuromorphic computing domain, in order to address many challenges in this domain, including computational complexity and power consumption. Our proposed model would not only enhance the capabilities of sentiment analysis but also contribute to the advancement of brain-inspired computing. Our proposed model could be utilized in other resource-constrained and low-power applications, such as robotics, autonomous, and smart systems.

Keywords: SpiNNaker; artificial neural network; natural language processing; neuromorphic computing; sentiment analysis; spiking neural networks.

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