Summary: Researchers have developed a theoretical model that clarifies the complex mechanisms of synaptic plasticity — the process that enables the brain to learn and form lasting memories. The model emphasizes the vital role of interactions among neighboring synapses, showing how these local relationships help produce rapid learning and the consolidation of long-term memory traces.
By modeling cooperative dynamics between excitatory and inhibitory synapses, the study explains how interactions among nearby contact sites influence both the strength and the stability of neural connections. This work provides a unified perspective on synaptic co-dependency and sheds light on the micro-level network dynamics that support learning and memory formation.
Key Facts:
- Synaptic plasticity underpins learning: The model builds on the established idea that the brain learns and remembers by adjusting the strength of synaptic connections in response to neuronal activity.
- Neighbor interactions matter: Synapses do not change in isolation; adjacent synaptic contacts influence one another, affecting how information is encoded and stored.
- A holistic network perspective: By integrating multiple rules of synaptic co-dependency into a single network model, the research offers a refined view of how microscale plasticity mechanisms optimize learning and memory.
Source: University of Basel
Researchers at the University of Basel, together with a collaborator in Austria, have created a model that provides a comprehensive explanation for how the brain can learn quickly and form stable, long-lasting memories.
Their study highlights the central role played by interactions among neighboring contact sites on neurons — the synapses — in enabling brain plasticity, the brain’s capacity to adapt to new experiences and retain information over time.
The principle that connections strengthen when neurons fire together dates back to Donald O. Hebb in 1949. Hebb’s idea — that co-active neurons reinforce their connections — remains a foundational concept for understanding learning and memory. Synaptic plasticity refers to the ability of synaptic connections to increase or decrease their strength, allowing neural circuits to encode new information and discard what is no longer relevant.
“Traditionally, synaptic changes have been viewed as largely local, occurring at individual synapses,” explains Dr. Everton Agnes from the Biozentrum at the University of Basel. “However, synapses that undergo plastic changes also influence multiple neighboring synapses, and those neighborly influences are important for shaping network behavior.”
Because simultaneous experimental observation of many nearby synapses is technically difficult, Dr. Agnes and Prof. Tim Vogels from the Institute of Science and Technology Austria designed a theoretical framework to disentangle these co-dependent processes. Their findings were published in Nature Neuroscience.
Synaptic plasticity: the brain’s mechanism for learning
Learning often feels like repetition reinforcing memory: vocabulary practiced over time becomes easier to recall because the neurons involved build stronger connections. Synaptic plasticity — strengthening or weakening of synaptic contacts — is the biological basis for this process. The brain continuously updates its network of connections to store relevant information and let go of what is unnecessary.
Neurons are connected through both excitatory and inhibitory synapses. Excitatory synapses increase the likelihood of signal transmission, while inhibitory synapses reduce it. The new model demonstrates that these types do not operate independently. Nearby excitatory synapses influence each other’s capacity for long-term potentiation, shaping how memory traces are encoded. At the same time, inhibitory synapses contribute to the long-term stability of those excitatory changes, providing mechanisms that can support rapid learning from a single exposure — a phenomenon sometimes described as one-shot learning.
A holistic view of neuronal network dynamics
The coordinated interplay among neighboring synapses is essential both for fast learning and for the persistence of memories. “By incorporating a broad set of synaptic co-dependency rules into a unified network model, we offer a more holistic view of the processes that underlie brain plasticity,” says Agnes.
The model highlights how local synaptic interactions contribute to the emergence of stable and functionally rich network dynamics at the microscale. These dynamics help explain experimentally observed features such as tuning of response properties and the clustering of co-active synapses on dendrites, as well as network-level behaviors seen in motor and sensory cortical circuits.
About this memory, learning, and synaptic plasticity research news
Author: Angelika Jacobs
Source: University of Basel
Contact: Angelika Jacobs – University of Basel
Image: The image is credited to Neuroscience News
Original Research: Open access. “Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks” by Everton Agnes et al., published in Nature Neuroscience.
Abstract
Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks
Synaptic plasticity shapes the development and ongoing function of the brain. When synapses change their strength, they often influence neighboring synapses as well. This co-dependency is challenging to study experimentally because it requires simultaneous monitoring of multiple nearby connections.
To clarify the mechanisms behind experimentally observed phenomena, the authors introduce a formal framework for synaptic co-dependency across different connection types. The model explains how inhibitory synapses can gate excitatory plasticity, while interactions among neighboring excitatory synapses determine the magnitude of long-term potentiation.
The interplay between excitatory and inhibitory plasticity in the model accounts for both the rapid emergence and the enduring stability of diverse synaptic weight patterns, including orientation selectivity and the dendritic clustering of co-active inputs. In recurrent networks, these co-dependent rules produce rich, stable dynamics with heightened sensitivity to inputs, resembling key features of biological cortical circuits.
Overall, the results point to an essential role for neighborly synaptic interactions during learning, linking micro-level physiological processes to emergent network phenomena that support memory and adaptive behavior.