Real-time Decoding
After the techniques of multielectrode recordings were introduced, the task of real-time decoding of information from large neuronal ensembles became feasible. If, as Georgopoulos showed, just a few primary motor neurons could accurately predict hand motion in two planes, reconstruction of the movement of an entire limb should be possible with enough simultaneous recordings. In parallel, with the introduction of an enormous Neuroscience boost from DARPA, several lab groups used millions of dollars to make brain-machine interfaces. Of these groups, two were successful in experiments showing that animals could control external interfaces with models based on their neural activity, and that once control was shifted from the hand to the brain-model, animals could learn to control it better. These two groups are led by John Donoghue and Miguel Nicolelis, and both are involved in towards human trials with their methods.
John Donoghue formed the company Cyberkinetics to facilitate commercialization of brain-machine interfaces. They bought the Utah array from Richard Normann. Along with colleagues Hatsopoulos, Paninski, Fellows and Serruya, they first showed that neural ensembles could be used to control external interfaces by having a monkey control a cursor on a computer screen with its mind (2002).
Miguel Nicolelis worked with John Chapin, Johan Wessberg, Mark Laubach, Jose Carmena, Mikhail Lebedev, Antonio Pereira, Jr., Sidarta Ribeiro and other colleagues showed that activity of large neural ensembles can predict arm position. This work made possible creation of brain-machine interfaces - electronic devices that read arm movement intentions and translate them into movements of artificial actuators. Carmena et al. (2003) programmed the neural coding in a brain-machine interface allowed a monkey to control reaching and grasping movements by a robotic arm, and Lebedev et al. (2005) argued that brain networks reorganize to create a new representation of the robotic appendage in addition to the representation of the animal's own limbs.
In addition to the studies by Nicolelis and Donoghue, the groups of Andrew Schwartz and Richard Andersen are developing decoding algorithms that reconstruct behavioral parameters from neuronal ensemble activity. For example Andrew Schwartz uses population vector algorithms that he previously developed with Apostolos Georgopoulos.
Demonstrations of decoding of neuronal ensemble activity can be subdivided into two major classes: off-line decoding and on-line (real time) decoding. In the off-line decoding, investigators apply different algorithms to previously recorded data. Time considerations are usually not an issue in these studies: a sophisticated decoding algorithm can run for many hours on a computer cluster to reconstruct a 10-minute data piece. On-line algorithms decode (and, importantly, predict) behavioral parameters in real time. Moreover, the subject may receive a feedback about the results of decoding — the so-called closed loop mode as opposed to the open loop mode in which the subject does not receive any feedback.
Interestingly, as Hebb predicted, individual neurons in the population can contribute information about different parameters. For example, Miguel Nicolelis and colleagues reported that individual neurons simultaneously encoded arm position, velocity and hand gripping force when the monkeys performed reaching and grasping movements. Mikhail Lebedev, Steven Wise and their colleagues reported prefrontal cortex neurons that simultaneously encoded spatial locations that the monkeys attended to and those that they stored in short-term memory. Both attended and remembered locations could be decoded when these neurons were considered as population.
To address the question of how many neurons are needed to obtain an accurate read-out from the population activity, Mark Laubach in Nicolelis lab used neuron-dropping analysis. In this analysis, he measured neuronal read-out quality as a function of the number of neurons in the population. Read-out quality increased with the number of neurons—initially very notably, but then substantially larger neuronal quantities were needed to improve the read-out.
Read more about this topic: Neural Ensemble