What Makes Memories So Detailed and Enduring?

Summary: Researchers have identified a new mechanism of learning that stabilizes memory and reduces interference between different memories.

Source: University of Bristol

In years to come, our personal memories of the COVID-19 pandemic are likely to be etched in our minds with precision and clarity, distinct from other memories of 2020. The process which makes this possible has eluded scientists for many decades, but research led by the University of Bristol has made a breakthrough in understanding how memories can be so distinct and long-lasting without getting muddled up..

The study, published in Nature Communications, describes a newly discovered mechanism of learning in the brain shown to stabilise memories and reduce interference between them. Its findings also provide new insight into how humans form expectations and make accurate predictions about what could happen in future.

Memories are created when the connections between the nerve cells which send and receive signals from the brain are made stronger. This process has long been associated with changes to connections that excite neighbouring 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 nerve cell activity, for healthy brain function. The role of changes to inhibitory connection strength had not previously been considered and the researchers found that inhibitory connections between nerve cells, known as neurons, can similarly be strengthened.

Working together with computational neuroscientists at Imperial College London, the researchers showed how this allows the stabilisation of memory representations.

Their findings uncover 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. Moreover, computational modelling demonstrated this inhibitory learning enables the hippocampus to stabilise changes to excitatory connection strength, which prevents 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 these two types of inhibitory neurons could alter their connections and partake 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 that is.

“The computer modelling gave us important new insight into how inhibitory learning enables memories to be stable over time and not be susceptible to interference. That’s really important as it has previously been unclear how separate memories can remain precise and robust.”

The research was funded by the UKRI’s Biotechnology and Biological Sciences Research Council, which has awarded the teams further funding to develop this research and test their predictions from these findings by measuring the stability of memory representations.

This shows the hippocampus
The tiny red dots are inhibitory nerve cells within the brain’s hippocampus. The optogenetic tool, shown in green, allows researchers to measure the strength of messages to other nerve cells, using flashes of light. Image is credited to Matt Udakis.

Senior author Professor Jack Mellor, Professor in Neuroscience at the Centre for Synaptic Plasticity, said: “Memories form the basis of our expectations about future events and enable us to make more accurate predictions. What the brain is constantly doing is matching our expectations to reality, finding out where mismatches occur, and using this information to determine what we need to learn.

“We believe what we have discovered plays a crucial role in assessing how accurate our predictions are and therefore what is important new information. In the current 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 deliver exciting science with truly new insights. Memory researchers within Bristol Neuroscience form one of the largest communities of memory-focussed research in the UK spanning a broad range of expertise and approaches. It was a great opportunity to work together and start to answer these big questions, which neuroscientists have been grappling with for decades and have wide-reaching implications.”

About this memory research article

Source:
University of Bristol
Contacts:
Victoria Tagg – University of Bristol
Image Source:
The image is in credited to Matt Udakis.

Original Research: Open access
“Interneuron-specific plasticity at parvalbumin and somatostatin inhibitory synapses onto CA1 pyramidal neurons shapes hippocampal output” by Matt Udakis, Victor Pedrosa, Sophie Chamberlain, Claudia Clopath and Jack Mellor. Nature Communications.


Abstract

Interneuron-specific plasticity at parvalbumin and somatostatin inhibitory synapses onto CA1 pyramidal neurons shapes hippocampal output

The formation and maintenance of spatial representations within hippocampal cell assemblies is strongly dictated by patterns of inhibition from diverse interneuron populations. Although it is known that inhibitory synaptic strength is malleable, induction of long-term plasticity at distinct inhibitory synapses and its regulation of hippocampal network activity is not well understood. Here, we show that inhibitory synapses from parvalbumin and somatostatin expressing interneurons undergo long-term depression and potentiation respectively (PV-iLTD and SST-iLTP) during physiological activity patterns. Both forms of plasticity rely on T-type calcium channel activation to confer synapse specificity but otherwise employ distinct mechanisms. Since parvalbumin and somatostatin interneurons preferentially target perisomatic and distal dendritic regions respectively of CA1 pyramidal cells, PV-iLTD and SST-iLTP coordinate a reprioritisation of excitatory inputs from entorhinal cortex and CA3. Furthermore, circuit-level modelling reveals that PV-iLTD and SST-iLTP cooperate to stabilise place cells while facilitating representation of multiple unique environments within the hippocampal network.

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