Imagine two mailmen walking down a street together. One mailman loves dogs. The other mailman has been bitten by every dog in the neighborhood; He has an irrational fear of dogs he cannot seem to control and is constantly afraid of seeing dogs on his mail route. Suddenly, a German shepherd comes walking towards them down the sidewalk from the opposite direction.
The other man starts to sweat. The pupils of his eyes dilate, his heart beats faster, his breathing becomes more rapid and shallow, and his fists clench as his sympathetic nervous system becomes aroused. If this dynamic continues to its conclusion, the second mailman may get bit yet again, thereby fulfilling his expectations.
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Jazayeri had previously shown that humans performing this task tend to bias their responses toward the middle of the range. Here, they found that animals do the same. For example, if animals believed the interval would be short, and were given an interval of milliseconds, the interval they produced was a little shorter than milliseconds. Conversely, if they believed it would be longer, and were given the same millisecond interval, they produced an interval a bit longer than milliseconds.
Once they had established that the animals relied on their prior beliefs, the researchers set out to find how the brain encodes prior beliefs to guide behavior. They recorded activity from about 1, neurons in a region of the frontal cortex, which they have previously shown is involved in timing.
To make sense of these signals, the researchers analyzed the evolution of neural activity across the entire population over time, and found that prior beliefs bias behavioral responses by warping the neural representation of time toward the middle of the expected range. Researchers believe that prior experiences change the strength of connections between neurons. The strength of these connections, also known as synapses, determines how neurons act upon one another and constrains the patterns of activity that a network of interconnected neurons can generate.
The finding that prior experiences warp the patterns of neural activity provides a window onto how experience alters synaptic connections. As an independent test of these ideas, the researchers developed a computer model consisting of a network of neurons that could perform the same ready-set-go task. Using techniques borrowed from machine learning, they were able to modify the synaptic connections and create a model that behaved like the animals. These models are extremely valuable as they provide a substrate for the detailed analysis of the underlying mechanisms, a procedure that is known as "reverse-engineering.
The model also had a warped representation of time according to prior experience. The researchers used the computer model to further dissect the underlying mechanisms using perturbation experiments that are currently impossible to do in the brain. Using this approach, they were able to show that unwarping the neural representations removes the bias in the behavior. This important finding validated the critical role of warping in Bayesian integration of prior knowledge.
The researchers now plan to study how the brain builds up and slowly fine-tunes the synaptic connections that encode prior beliefs as an animal is learning to perform the timing task. Previous item Next item.
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