Real-time estimation and biofeedback of single-neuron firing rates using local field potentials

Abstract

The long-term stability and low-frequency composition of local field potentials (LFPs) offer important advantages for robust and efficient neuroprostheses. However, cortical LFPs recorded by multi-electrode arrays are often assumed to contain only redundant information arising from the activity of large neuronal populations. Here we show that multichannel LFPs in monkey motor cortex each contain a slightly different mixture of distinctive slow potentials that accompany neuronal firing. As a result, the firing rates of individual neurons can be estimated with surprising accuracy. We implemented this method in a real-time biofeedback brain–machine interface, and found that monkeys could learn to modulate the activity of arbitrary neurons using feedback derived solely from LFPs. These findings provide a principled method for monitoring individual neurons without long-term recording of action potentials.

A network engineering perspective on probing and perturbing cognition with neurofeedback

“…Although prior work demonstrated that neurofeedback is effective in modulating dynamics of (1) individual neurons at submillimeter resolution and fast time scales, 75,76 (2) millimeter-scale neuronal populations through oscillatory rhythms, 58 and (3) macroscale functional domains at slow time scales, 59,68 modulating the way in which brain regions functionally interact is now becoming a tractable frontier. Using statistical models to measure functional connectivity and to construct an effective feedback signal for modulation, several studies demonstrated that subjects can directly perturb functional interactions between brain regions with fMRI- [77][78][79][80][81] and EEG-based neurofeedback.…”

Artefactual origin of biphasic cortical spike-LFP correlation

“…In particular, the relationship between LFP and the spiking of nearby neurons can lead to important insights into the cortical function, e.g., (Gray and Singer 1989; Arieli et al 1995; Destexhe et al 1999; Fries et al 2001; Montemurro et al 2008; Nauhaus et al 2009; Martin and Schroeder 2016; Cui et al 2016), and can be utilized in the design of brain-machine interfaces (BMIs) (Gulati et al 2014; Hall et al 2014). The simplest quantitative measure of the relationship between spiking activity and LFP is the spike-LFP cross-correlation, also known as spike-triggered LFP average (stLFP).…”

A CMOS-based neural implantable optrode for optogenetic stimulation and electrical recording

“…To achieve this, rather than recording individual action potentials (APs), we have decided to tune our electronics to local field potentials (LFPs) of neuron activity. There is some evidence in the literature that recording LFPs are more stable than APs for chronic recordings [11,12], and potentially require less complex post-processing [12].…”