Paper Info
Title
Signal-independent timescale analysis (SITA) and its application for neural coding during reaching and walking.
Abstract
What are the relevant timescales of neural encoding in the brain? This question is commonly investigated with respect to well-defined stimuli or actions. However, neurons often encode multiple signals, including hidden or internal, which are not experimentally controlled, and thus excluded from such analysis. Here we consider all rate modulations as the signal, and define the rate-modulations signal-to-noise ratio (RM-SNR) as the ratio between the variance of the rate and the variance of the neuronal noise. As the bin-width increases, RM-SNR increases while the update rate decreases. This tradeoff is captured by the ratio of RM-SNR to bin-width, and its variations with the bin-width reveal the timescales of neural activity. Theoretical analysis and simulations elucidate how the interactions between the recovery properties of the unit and the spectral content of the encoded signals shape this ratio and determine the timescales of neural coding. The resulting signal-independent timescale analysis (SITA) is applied to investigate timescales of neural activity recorded from the motor cortex of monkeys during: (i) reaching experiments with Brain-Machine Interface (BMI), and (ii) locomotion experiments at different speeds. Interestingly, the timescales during BMI experiments did not change significantly with the control mode or training. During locomotion, the analysis identified units whose timescale varied consistently with the experimentally controlled speed of walking, though the specific timescale reflected also the recovery properties of the unit. Thus, the proposed method, SITA, characterizes the timescales of neural encoding and how they are affected by the motor task, while accounting for all rate modulations.
Year
DOI
Venue
2014
10.3389/fncom.2014.00091
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Keywords
Field
DocType
timescales,rate modulation,doubly stochastic Poisson processes,rate coding,brain-machine interface,signal to noise ratio
Neuroscience,Computer science,Neural coding,Signal-to-noise ratio,Brain–computer interface,Neuronal noise,Motor cortex,Artificial intelligence,Stimulus (physiology),Machine learning,Modulation (music),Encoding (memory)
Journal
Volume
ISSN
Citations 
8
1662-5188
0
PageRank 
References 
Authors
0.34
5
3
Name
Order
Citations
PageRank
Miriam Zacksenhouse15210.42
Mikhail A. Lebedev2305.07
Miguel A. L. Nicolelis315034.62