Volitional Control of Individual Neurons in the Human Brain

Abstract

Can the human brain, a complex interconnected structure of over 80 billion neurons learn to control itself at the most elemental scalea single neuron. We directly linked the firing rate of a single (direct) neuron to the position of a box on a screen, which participants tried to control. Remarkably, all subjects upregulated the firing rate of the direct neuron in memory structures of their brain. Learning was accompanied by improved performance over trials, simultaneous decorrelation of the direct neuron to local neurons, and direct neuron to beta frequency oscillation phase-locking. Such previously unexplored neuroprosthetic skill learning within memory related brain structures, and associated beta frequency phase-locking implicates the ventral striatum. Our demonstration that humans can volitionally control neuronal activity in mnemonic structures, may provide new ways of probing the function and plasticity of human memory without exogenous stimulation.

Brain–Machine Interfaces: Closed-Loop Control in an Adaptive System

“…Existing recordings in the hippocampus are focused on treating epilepsy (42). Experiments in these patients have found that some people can volitionally control activity in the hippocampus and surrounding areas (43). Offline open-loop decoding of navigation has also been well demonstrated in the hippocampus, but how this brain area might be incorporated into a closed-loop BMI remains an exciting open question.…”

A corollary discharge mediates saccade related inhibition of single units in mnemonic structures of the human brain

“…For offline spike detection, all microelectrode channels were bandpass filtered between 300-3000Hz. Spikes were subsequently detected using local energy measurement threshold crossings, calculated by convolving the raw signal with a kernel with an approximate width of an action potential [41]. All detected spikes were sorted using the open-source, semiautomatic template-matching algorithm OSort [38].…”

“…All detected spikes were sorted using the open-source, semiautomatic template-matching algorithm OSort [38]. Similar to our previous work [39], [41], we classified clusters as putative single neurons using the following criteria (1) minimal/no violation of refractory period, (2) shape of the inter-spike interval distribution, (3) shape of the waveform, (4) separation from other clusters; and, (5) stability of firing rate (assessed by comparing the average firing rate of the neuron to multiple null distributions obtained by randomly sampling 400ms and 5s epochs from each neuron's spike train). Groups that appeared similar to one another were merged.…”