Single-Neuron Stability during Repeated Reaching in Macaque Premotor Cortex

By C. Chestek, Aaron P. Batista, G. Santhanam, Byron M. Yu, A. Afshar, John P. Cunningham, V. Gilja, Stephen I. Ryu, Mark M. Churchland, Krishna V. Shenoy. 2007

174 citations recorded by the source corpus.

Some movements that animals and humans make are highly stereotyped, repeated with little variation. The patterns of neural activity associated with repeats of a movement may be highly similar, or the same movement may arise from different patterns of neural activity, if the brain exploits redundancies in the neural projections to muscles. We examined the stability of the relationship between neural activity and behavior. We asked whether the variability in neural activity that we observed during repeated reaching was consistent with a noisy but stable relationship, or with a changing relationship, between neural activity and behavior. Monkeys performed highly similar reaches under tight behavioral control, while many neurons in the dorsal aspect of premotor cortex and the primary motor cortex were simultaneously monitored for several hours. Neural activity was predominantly stable over time in all measured properties: firing rate, directional tuning, and contribution to a decoding model that predicted kinematics from neural activity. The small changes in neural activity that we did observe could be accounted for primarily by subtle changes in behavior. We conclude that the relationship between neural activity and practiced behavior is reasonably stable, at least on timescales of minutes up to 48 h. This finding has significant implications for the design of neural prosthetic systems because it suggests that device recalibration need not be overly frequent, It also has implications for studies of neural plasticity because a stable baseline permits identification of nonstationary shifts.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Natural continual learning: success is a journey, not (just) a destination (2021, background): “In the RNNs, we found that the single-unit representations of a given digit changed during learning of the task involving that digit but stabilized after learning, consistent with work in several distinct biological circuits [6, 11, 16, 20, 34].”
Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex. (2017, unknown): “Many studies have noted stability in motor cortex activity, but some have identified subtle shifts in tuning over days (Chestek et al., 2007; Ganguly and Carmena, 2009; Huber et al., 2012; PadoaSchioppa et al., 2004; Peters et al., 2014; Rokni et al., 2007; Stevenson et al., 2011).”
Variance and invariance of neuronal long-term representations (2017, unknown): “Here it has direct implications for the design and prospective long-‐term performance of brain-‐machine-‐interfaces (BMIs) for the control of motor prosthetics [60,64,70].”
Computation Through Neural Population Dynamics (2020, background): “…the dynamics themselves; (d) movement begins; (e) sensory feedback is isolated in null-dimensions before influencing output-potent feedback corrections; and ( f ) these dynamics are largely stable across time for a given behavior (Chestek et al. 2007, Flint et al. 2016, Gallego et al. 2020).”
Long-term stability of cortical population dynamics underlying consistent behavior (2020, unknown): “…time, prior studies disagree on the stability of movement information in single neurons over short periods of time (from one to four days), with some claiming inherent instability of tuning properties 29 and others claiming stability well within the error introduced by measurement noise 11,17,30 .”
Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex (2011, methodology): “A number of papers have attempted to track single units across a few days [30, 35–37].”
Automated long-term recording and analysis of neural activity in behaving animals (2016, background): “…stability of movement-related neural dynamics from such experiments have produced conflicting results, with some studies reporting stability (Chestek et al., 2007; Fraser and Schwartz, 2012; Ganguly and Carmena, 2009; Greenberg and Wilson, 2004), while others finding significant…”