In the years to come, personal memories of COVID-19 the pandemic is likely to remain etched in our minds with precision and clarity, distinct from other memories of 2020. The process that makes it possible has eluded scientists for many decades, but research led by the University of Bristol made a breakthrough in understanding how memories can be so distinct and lasting without getting confused.
The study, published in Nature Communications, describes a recently discovered learning mechanism in the brain that has been shown to stabilize memories and reduce interference between them. His findings also provide new insights into how humans form expectations and make accurate predictions about what might happen in the future.
Memories are created when the connections between the nerve cells that send and receive signals from the brain are made stronger. This process has long been associated with changes in the connections that excite nearby nerve cells in the hippocampus, a region of the brain crucial for memory formation.
These excitatory connections must be balanced with inhibitory connections, which dampen the activity of nerve cells, for healthy brain function. The role of changes in the strength of the inhibitory connection had not previously been considered, and researchers found that the inhibitory connections between nerve cells, known as neurons, can be strengthened in a similar way.
Working together with computational neuroscientists at Imperial College London, the researchers showed how this enables stabilization of memory representations.
Their findings discover for the first time how two different types of inhibitory connections (from parvalbumin and somatostatin-expressing neurons) can also vary and increase their strength, just like excitatory connections. Furthermore, computational modeling has shown that this inhibitory learning allows the hippocampus to stabilize changes in the strength of the excitatory connection, which prevents the interfering information from disrupting memories.
First author, Dr. Matt Udakis, research associate at the School of Physiology, Pharmacology and Neuroscience, said, “We were all really excited when we discovered that these two types of inhibitory neurons could alter their connections and participate in learning.
“It provides an explanation for what we all know to be true; that memories do not disappear as soon as we encounter a new experience. These new findings will help us understand why this is so.
“Computer modeling has given us important new insights into how inhibitory learning allows memories to be stable over time and not be susceptible to interference. This is really important as it was previously unclear how separate memories can remain accurate and robust. “
The research was funded by UKRI’s Biotechnology and Biological Sciences Research Council, which awarded the teams additional funding to develop this research and test their predictions from these results by measuring the stability of memory representations.
Senior author Professor Jack Mellor, professor of neuroscience at the Center for Synaptic Plasticity, said, “Memories form the basis of our expectations about future events and allow us to make more accurate predictions. What the brain constantly does is match our expectations to reality, find out where the discrepancies occur, and use this information to determine what we need to learn.
“We believe what we have discovered plays a crucial role in evaluating how accurate our predictions are and therefore what new information is important. In today’s climate, our ability to manage our expectations and make accurate predictions has never been more important.
“This is also a great example of how research at the interface of two different disciplines can provide exciting science with truly new insights. The memory researchers within Bristol Neuroscience form one of the largest memory-focused research communities in the UK spanning a broad range of skills and approaches. It was a great opportunity to work together and start answering these big questions, which neuroscientists have been grappling with for decades and have far-reaching implications. “
Reference: “Interneuron Specific Plasticity to Parvalbumin and Somatostatin Inhibitory Synapses on CA1 Pyramidal Neurons Model Hippocampal Output” by Matt Udakis, Victor Pedrosa, Sophie E. L. Chamberlain, Claudia Clopath, and Jack R. Mellor, September 2, 2020, Nature Communications.
DOI: 10.1038 / s41467-020-18074-8