The Brain’s Hidden Patterns: Uncovering the Secret to Flexibility and Stability

For decades, scientists believed that the brain used a single, shared transmission site for all types of plasticity. However, a groundbreaking study from researchers at the University of Pittsburgh has challenged this assumption, revealing that the brain employs distinct transmission sites to achieve different types of plasticity.

The study, published in Science Advances, offers a deeper understanding of how the brain balances stability with flexibility – a process essential for learning, memory, and mental health. By uncovering the hidden patterns of the brain’s transmission sites, researchers hope to shed light on the underlying mechanisms that govern our thoughts, emotions, and behaviors.

Neurons communicate through synaptic transmission, where one neuron releases chemical messengers called neurotransmitters from a presynaptic terminal. These molecules travel across a microscopic gap called a synaptic cleft and bind to receptors on a neighboring postsynaptic neuron, triggering a response.

Traditionally, scientists believed that spontaneous transmissions (signals that occur randomly) and evoked transmissions (signals triggered by sensory input or experience) originated from one type of canonical synaptic site and relied on shared molecular machinery. However, the research team led by Oliver Schlüter discovered that the brain instead uses separate synaptic transmission sites to carry out regulation of these two types of activity.

The study focused on the primary visual cortex, where cortical visual processing begins. The researchers expected spontaneous and evoked transmissions to follow a similar developmental trajectory, but instead found that they diverged after eye opening.

As the brain began receiving visual input, evoked transmissions continued to strengthen. In contrast, spontaneous transmissions plateaued, suggesting that the brain applies different forms of control to the two signaling modes. To understand why, the researchers applied a chemical that activates otherwise silent receptors on the postsynaptic side, causing spontaneous activity to increase while evoked signals remained unchanged.

This division likely enables the brain to maintain consistent background activity through spontaneous signaling while refining behaviorally relevant pathways through evoked activity. This dual system supports both homeostasis and Hebbian plasticity – the experience-dependent process that strengthens neural connections during learning.

“Our findings reveal a key organizational strategy in the brain,” said Yue Yang, a research associate in the Department of Neuroscience and first author of the study. “By separating these two signaling modes, the brain can remain stable while still being flexible enough to adapt and learn.”

The implications could be broad. Abnormalities in synaptic signaling have been linked to conditions like autism, Alzheimer’s disease, and substance use disorders. A better understanding of how these systems operate in the healthy brain may help researchers identify how they become disrupted in disease.

“Learning how the brain normally separates and regulates different types of signals brings us closer to understanding what might be going wrong in neurological and psychiatric conditions,” said Yang.

Have you ever wondered what happens inside your brain when you listen to music or a steady rhythm? A groundbreaking study from Aarhus University and the University of Oxford has revealed that your brain doesn’t just hear it – it reorganizes itself in real time. The research, led by Dr. Mattia Rosso and Associate Professor Leonardo Bonetti, introduces a novel neuroimaging method called FREQ-NESS, which maps the brain’s internal organization with high spectral and spatial precision.

Using advanced algorithms, FREQ-NESS disentangles overlapping brain networks based on their dominant frequency, allowing scientists to track how each frequency propagates in space across the brain. This development represents a major advance in neuroscience, enabling researchers to study the brain’s large-scale dynamics more accurately.

The traditional view of brainwaves as fixed stations – alpha, beta, gamma – is being challenged by this research. FREQ-NESS reveals that brain activity is organized through frequency-tuned networks, both internally and externally. This understanding opens new possibilities for basic neuroscience, brain-computer interfaces, and clinical diagnostics.

This study contributes to a growing body of research exploring how the brain’s rhythmic structure shapes perception, attention, and consciousness. Professor Leonardo Bonetti notes, “The brain doesn’t just react – it reconfigures. And now we can see it.” This breakthrough could revolutionize how scientists study brain responses to music and beyond.

A large-scale research program is underway to build on this methodology, supported by an international network of neuroscientists. FREQ-NESS may also pave the way for individualized brain mapping, offering new insights into the intricate harmony of the human brain.

The human visual system has long been a source of inspiration for computer vision researchers, who aim to develop machines that can see and understand the world around them with the same level of efficiency and accuracy as humans. While machine vision systems have made significant progress in recent years, they still face major challenges when it comes to processing vast amounts of visual data while consuming minimal power.

One approach to overcoming these hurdles is through neuromorphic computing, which mimics the structure and function of biological neural systems. However, two major challenges persist: achieving color recognition comparable to human vision, and eliminating the need for external power sources to minimize energy consumption.

A recent breakthrough by a research team led by Associate Professor Takashi Ikuno from Tokyo University of Science has addressed these issues with a groundbreaking solution. Their self-powered artificial synapse is capable of distinguishing colors with remarkable precision, making it particularly suitable for edge computing applications where energy efficiency is crucial.

The device integrates two different dye-sensitized solar cells that respond differently to various wavelengths of light, generating its electricity via solar energy conversion. This self-powering capability makes it an attractive solution for industries such as autonomous vehicles, healthcare, and consumer electronics, where visual recognition capabilities are essential but power consumption is limited.

The researchers demonstrated the potential of their device in a physical reservoir computing framework, recognizing different human movements recorded in red, green, and blue with an impressive 82% accuracy. This achievement has significant implications for various industries, including autonomous vehicles, which could utilize these devices to efficiently recognize traffic lights, road signs, and obstacles.

In healthcare, self-powered artificial synapses could power wearable devices that monitor vital signs like blood oxygen levels with minimal battery drain. For consumer electronics, this technology could lead to smartphones and augmented/virtual reality headsets with dramatically improved battery life while maintaining sophisticated visual recognition capabilities.

The realization of low-power machine vision systems with color discrimination capabilities close to those of the human eye is within reach, thanks to this breakthrough research. The potential applications of self-powered artificial synapses are vast, and their impact will be felt across various industries in the years to come.