How easy is it to falsify memories? New research at the Weizmann Institute shows that a bit of social pressure may be all that is needed. The study, which appeared recently in Science, reveals a unique pattern of brain activity when false memories are formed – one that hints at a surprising connection between our social selves and memory.

The experiment, conducted by Prof. Yadin Dudai and research student Micah Edelson of the Institute’s Neurobiology Department, together with Prof. Raymond Dolan and Dr. Tali Sharot of University College London, took place in four stages. In the first, volunteers watched a documentary film in small groups. Three days later, they returned to the lab individually to take a memory test, answering questions about the film. They were also asked how confident they were about their answers.

They were later invited back to the lab to retake the test while being scanned in a functional magnetic resonance imager (fMRI) that revealed their brain activity. This time, the subjects were also given a “lifeline”: the supposed answers of the others in their film-viewing group (along with social-media-style photos). Planted among these were false answers to questions the volunteers had previously answered correctly and confidently. After seeing these “planted” responses, the participants conformed to the group, giving incorrect answers nearly 70% of the time.  

But were they simply conforming to perceived social demands, or had their memory of the film actually undergone a change? To find out, the researchers invited the subjects back to the lab to take the memory test once again, telling them that the answers they had previously been fed were not those of their fellow film watchers but random computer generations. In some cases the responses reverted back to the original, correct ones; but close to half remained erroneous, implying that the subjects were relying on false memories implanted in the earlier session.

An analysis of the fMRI data showed differences in brain activity between the persistent false memories and the temporary errors of social compliance. The most outstanding feature of the false memories was a strong co-activation and connectivity between two brain areas: the hippocampus and the amygdala. The hippocampus is known to play a role in long-term memory formation, while the amygdala, sometimes known as the emotion center of the brain, plays a role in social interaction. The scientists think that the amygdala may act as a gateway connecting the social and the memory processing parts of the brain; its “stamp” may be needed for some types of memories, to give them approval before they get uploaded to the memory bank. Thus social reinforcement could act on the amygdala, persuading our brains to replace a strong memory with a false one.

 

Prof. Yadin Dudai’s research is supported by the Norman and Helen Asher Center for Human Brain Imaging, which he heads; the Nella and Leon Benoziyo Center for Neurological Diseases; the Carl and Micaela Einhorn-Dominic Institute of Brain Research, which he heads; the Marc Besen and the Pratt Foundation, Australia; Lisa Mierins Smith, Canada; the Abe and Kathryn Selsky Memorial Research Project; and Miel de Botton, UK. Prof. Dudai is the incumbent of the Sara and Michael Sela Professorial Chair of Neurobiology.
 

 

To reveal some of the ground rules for neuron activity, the scientists used a mathematical model similar to one commonly used in physics, where it was developed to study the behavior of large numbers of magnets in magnetic fields. An equivalent model is also used in statistics and in machine learning. In all these domains, complex behavior arises from pair-wise interactions between elements – attraction and repulsion in magnets, on and off states in binary variables, firing and silence in neurons. When the scientists first applied the model to small networks, it fit perfectly. Even for large networks, the experimental data seemed to fit fairly well, except for a few points that fell off the scale. Upon closer inspection, however, they realized that the data points that didn’t conform to the model belonged to the most frequently occurring activity patterns, and these reflected more complex grammar. In particular, they reflected dependencies between cells that could not be explained by pair relations alone. As a result, their model was good at predicting the rare “phrases” but less accurate when it came to the common ones. But just as we must learn to say: “I am hungry,” before we progress to ordering a full meal with wine, side dishes and dessert in a restaurant, Schneidman and his team realized that they couldn’t ignore the everyday expressions used in brain communication if they wanted to get a feel for its language. Their challenge was to find a way to deal with the common patterns and the rare ones at the same time.

Surprisingly, making a small change in the mathematical formula led to a very simple and accurate way to infer the grammar of this complex system. The original physics equation encodes the interactions between magnets with the numbers 1 and -1. Instead of representing a silent neuron with a -1 – as though it were a negative magnetic pole – they used a 0. While this may seem to be a mere “accounting” issue, it has a profound effect on the formula’s terms in one special case: that in which the elements in the network are only rarely active. This is exactly the situation in the brain: Most neurons are silent most of the time. Suddenly the common phrases could be interpreted, revealing fundamental interactions between neurons. Differences among the various phrases also enabled the researchers to infer different rules from each of the common patterns.

In fact, from an incredibly complex network of possible interactions, the researchers obtained a picture of basic neural communication that is decidedly sparse, yet extremely accurate. “We could assemble a basic grammar of millions of activity patterns from about 500 common phrases that rely on two-, three- and four-way connections – provided we knew which examples to choose,” says Schneidman. “The grammar of neuron networks is eminently learnable.” He thinks it may be learnable precisely because it seems to work like language. The common, constantly repeated phrases may be the way that neurons gain their communications skills and continue to understand one another.

With this new insight into the nature of neural communication, the researchers were able to decode the visual information carried by large groups of retinal cells. Schneidman believes that this new approach could enable us to obtain a detailed picture of the workings of large groups of neurons in different parts of the brain. This might, eventually, lead the way to reading the information encoded in such networks and point to new approaches to treating neurological disorders.

 

Dr. Elad Schneidman's research is supported by the Jeanne and Joseph Nissim Foundation for Life Sciences Research; the Peter and Patricia Gruber Award; and the J&R Foundation.
 
 
 

To investigate learning in unfavorable situations, Dr. Rony Paz of the Institute’s Neurobiology Department, together with his student Jennifer Resnik, had volunteers learn that some tones led to an offensive outcome (e.g., a very bad odor), whereas other tones were followed by pleasant a outcome or by nothing at all. The volunteers were later tested for their perceptual thresholds – that is, how well they were able to distinguish either the “bad” or “good” tones from other, similar tones.

As expected from previous studies, in the neutral or positive conditions, the volunteers became better with practice at discriminating between tones. But, surprisingly, when they found themselves exposed to a negative, possibly disturbing stimulus, their performance worsened.

The differences in learning were in fact very basic differences in perception. After learning that a stimulus is associated with a highly unpleasant experience, the subjects could not distinguish it from other, similar stimuli, even though they could do so beforehand or in normal conditions. In other words, no matter how well they usually learned new things, the subjects receiving the “aversive reinforcement” experienced the two tones as the same.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Long-term memory is a slippery thing. Just how slippery was demonstrated a few years ago by Weizmann Institute scientists, who erased entire memories in rats just by blocking a certain protein in the brain. In other words, memory – even the part we imagine to contain neatly packed files – is in reality a dynamic piece of equipment that must be actively maintained to work. Now these scientists have shown, in research that appeared in Science, that manipulating that same protein can enhance memory.

The protein – PKMzeta – is produced in the brain in response to learning, and it acts on the synapses – the active contact points between neurons. It continues to operate there long after the memory has been formed, suggesting that its function is tied not to learning (that is, absorbing information) but to keeping what is learned available in the long-term memory. In 2007, Prof. Yadin Dudai and research student Reut Shema of the Neurobiology Department, together with Prof. Todd Sacktor of SUNY Downstate Medical Center, New York, trained rats to avoid a specific taste and then blocked the activity of PKMzeta in their brains. While the control rats still had a strong aversion to the taste even months after the training, those in which the activity of the protein was briefly blocked had no such qualms, appearing to have forgotten what they had learned.