The human brain is perhaps the most complex of organs, boasting between 50-100 billion nerve cells or neurons that constantly interact with each other. These neurons ‘carry’ messages through electrochemical processes; meaning, chemicals in our body (charged sodium, potassium and chloride ions) move in and out of these cells and establish an electrical current.
Scientists have, for a long time now, stimulated with different types of inputs individual neurons that have been isolated for study. To have enough statistical power, these experiments typically involved stimulating a single neuron over and over again, to get a general idea of how it responds to different signals. Although these studies have yielded a lot of information, they have their own limitations.
An article by University of Leicester bioengineer Professor Rodrigo Quian Quiroga appeared recently in Nature Reviews Neuroscience. In the article, Prof. Quian Quiroga and co-author Dr. Stefano Panzeri discuss new methodologies that are enabling scientists to better understand how our brain processes information.
Prof. Quian Quiroga explains, “The human brain typically makes decisions based on a single stimulus, by evaluating the activity of a large number of neurons. I don’t get in front of a tiger 100 times to make an average of my neuronal responses and decide if I should run or not. If I see a tiger once, I run”. Traditional studies thus undermine this complexity by only accounting for the responses single neurons.
Moreover, these studies take into account an “average response” obtained by stimulating the neuron numerous times. The brain, on the other hand, acts based on single stimulus presentations. Therefore, the information given by an averaged response can often be insufficient.
Prof. Quian Quiroga and Dr. Panzeri stress, on account of these factors “it is important to shift from a single-neuron, multiple-trial framework to multiple-neuron, single-trial methodologies”. In other words, it is more beneficial to study responses of numerous neurons to a single stimulus.
Prof. Quian Quiroga says, “A major challenge of our days is (thus) to develop the methodologies to record and process the data from hundreds of neurons and developing these is by no means a trivial task”.
He adds, “Our brains are able to create very complex processes – just imagine the perfect harmony with which we move different muscles for normal walking – thousands of neurons are involved in this and to determine the role of each is complicated”.
In his recent review paper, Prof. Quian Quiroga and Dr. Panzeri discuss two complementary approaches that can be used to resolve this, namely ‘decoding’ and ‘information theory’.
‘Decoding’ essentially helps determine what must have caused a particular response (much like “working backwards”). Thus, the response of a neuronal population is used to reconstruct the stimulus or behaviour that caused it in the first place. ‘Information theory’, on the other hand, literally quantifies how much information a number of neurons carry about the stimulus.
Prof. Quian Quiroga explains, “together, the two approaches not only allow scientists to extract more information on how the brain works, but information that is ambiguous at the level of single neurons, can be clearly evaluated when the whole ‘population’ is considered”. The review is an asset for anyone involved in the field, as it carefully considers and evaluates the two statistical approaches, as well as describes potential applications.
As part of his own research, Prof. Quiroga (in collaboration with Prof. Richard Andersen at Caltech) has been studying the ‘decoding’ of movement plans using activity of certain neuronal populations. This ability to predict movement intentions from activity of neurons has application in brain-machine interfaces, especially for development of neural prostheses (electronic and/or mechanical devices that connect to the nervous system and replace functions lost as a result of disease or injury) for paralysed patients.
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