Scientists at University of Southern California (USC) have developed a prosthetic implant to help a disabled brain encode memories from being forgotten.
The prosthesis, which includes a small array of electrodes implanted into the brain, gives new hope for dementia and brain-damaged patients who are suffering from memory loss.
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According to researchers, the new implant has been tested successfully in animals and is now being evaluated on human brains.
The new implant has been designed at USC and tested at Wake Forest Baptist Medical Center in Winston-Salem, as part of their decade-long collaboration.
USC noted that: "When your brain receives the sensory input, it creates a memory in the form of a complex electrical signal that travels through multiple regions of the hippocampus, the memory centre of the brain.
"At each region, the signal is re-encoded until it reaches the final region as a wholly different signal that is sent off for long-term storage."
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By GlobalDataThe device relies on a new algorithm that mimics the electrical signalling used by the brain to translate short-term into permanent memories.
In this development, researchers have collected the neural data used to construct the models and algorithms.
The implant is designed to bypass a damaged hippocampal section and provide the next region with the correctly translated memory.
Although there is no way of ‘reading’ a memory, the electrical signalling makes it possible to bypass a damaged region of the brain and decode the content or meaning of a memory from its electrical signal.
USC project leader Ted Berger said: "It’s like being able to translate from Spanish to French without being able to understand either language."
The new technology includes a small collection of electrodes implanted directly into the brain to treat chronic seizures.
The device has been tested on nine patients in more than 100 trials and researchers read the electrical signals created during memory formation at two regions of the hippocampus.
Researchers then fed those signals into the model and examined how the signals generated from the first region of the hippocampus were translated into signals generated by the second region of the hippocampus.
Results from these trials were then used to refine the electrical algorithm until it could predict with 90% accuracy how the signals would be translated.
