Commentary: Emergence of a stable cortical map for neuroprosthetic control
This highly cited paper by Ganguly and Carmena (2009) reported a case of neuroplasticity associated with the operation of a brain-machine interface (BMI). Neuroplasticity is of great interest to BMI developers because of its causal role in the embodiment of neural prostheses (Lebedev and Nicolelis, 2006; Dobkin, 2007; Koralek et al., 2012; Shenoy and Carmena, 2014; Kraus et al., 2016; Gulati et al., 2017). Ganguly and Carmena reported that small populations of neurons (from 10 to 15) recorded in monkey primary motor cortex (M1) adapted to operating a BMI based on a fixed linear decoder. The decoder was trained once and left unchanged for several weeks. The population activity patterns underwent plastic modifications and stabilized on an optimal “cortical map” that assured accurate performance of center-out movements with a screen cursor. Moreover, monkeys learned to operate shuffled decoders, where the original neuronal weights were randomly reassigned. Here I comment on three issues arising from this paper: (1) the proper way to assess neuronal tuning under BMI control; (2) the constraints imposed on neuronal tuning properties by a fixed decoder; and (3) the problem of measuring changes in tuning when both neuronal activity and cursor trajectories change.