How to read neuron-dropping curves?

Methods for decoding information from neuronal signals (Kim et al., 2006; Quiroga and Panzeri, 2009) have attracted much interest in recent years, in a large part due to the rapid development of brain-machine interfaces (BMIs) (Lebedev and Nicolelis, 2006; Lebedev, 2014). BMIs strive to restore brain function to disabled patients or even augment function to healthy individuals by establishing direct communications between the brain and external devices, such as computers and robotic limbs. Accordingly, BMI decoders convert neuronal activity into font characters (Birbaumer et al., 1999), position and velocity of a robot (Carmena et al., 2003; Velliste et al., 2008), sound (Guenther et al., 2009), etc. Large-scale recordings, i.e., simultaneous recordings from as many neurons and as many brain areas as possible, have been suggested as a fundamental approach to improve BMI decoding (Chapin, 2004; Nicolelis and Lebedev, 2009; Schwarz et al., 2014). To this end, the dependency of decoding accuracy on the number of recorded neurons is often quantified as a neuronal dropping curve (NDC) (Wessberg et al., 2000). The term “neuron dropping” refers to the procedure, where neurons are randomly removed from the sample until there is only one neuron left. In this analysis, large neuronal populations usually outperform small ones, the result that accords with the theories of distributed neural processing (Rumelhart et al., 1986). In addition to BMIs based on large-scale recordings, several studies have adapted an alternative approach, where a BMI is driven by a small neuronal population or even a single neuron that plastically adapts to improve BMI performance (Ganguly and Carmena, 2009; Moritz and Fetz, 2011). In such BMIs, a small number of neurons serve as a final common path (Sherrington, 1906) to which inputs from a vast brain network converge. While the utility of large-scale BMIs vs. small-scale BMIs has not been thoroughly investigated, several studies reported that information transfer by BMIs starts to saturate after the population size reaches approximately 50 neurons (Sanchez et al., 2004; Batista et al., 2008; Cunningham et al., 2008; Tehovnik et al., 2013). In one paper, this result was interpreted as mass effect principle, i.e., stabilization of BMI performance after neuronal sample reaches a critical mass (Nicolelis and Lebedev, 2009). However, another recent paper claimed that large-scale recordings cannot improve BMI performance because of the saturation (Tehovnik et al., 2013). This controversy prompted me to clarify here what NDCs show, whether or not they saturate, and how they can be applied to analyze BMI performance.