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The importance of single-trial analyses can be simply illustrated by considering a classic perceptual decision-making study by Newsome and colleagues ( Horwitz and Newsome 2001). 2008 Horwitz and Newsome 2001 Jones et al. In such settings, it is critical that the neural data not be averaged across trials, but instead be analyzed on a trial-by-trial basis ( Arieli et al. This is particularly true of behavioral tasks involving perception, decision making, attention, or motor planning. However, if the neural responses are more a reflection of internal processing rather than external stimulus drive, the time course of the neural responses may differ on nominally identical trials. Neural responses are typically studied by averaging noisy spiking activity across multiple experimental trials to obtain firing rates that vary smoothly over time. Motivation for single-trial analysis of neural population activity Finally, to assess how well the proposed methods characterize neural population activity when the underlying time course is known, we performed simulations that revealed that GPFA performed tens of percent better than the best two-stage method. We then show how such methods can be a powerful tool for relating the spiking activity across a neural population to the subject's behavior on a single-trial basis. From the extracted trajectories, we directly observed a convergence in neural state during motor planning, an effect that was shown indirectly by previous studies. The predictive ability was further improved by going to GPFA. By adopting a goodness-of-fit metric that measures how well the activity of each neuron can be predicted by all other recorded neurons, we found that the proposed extensions improved the predictive ability of the two-stage methods. We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution.
We then present a novel method for extracting neural trajectories-Gaussian-process factor analysis (GPFA)-which unifies the smoothing and dimensionality-reduction operations in a common probabilistic framework. We first describe extensions of the two-stage methods that allow the degree of smoothing to be chosen in a principled way and that account for spiking variability, which may vary both across neurons and across time. Current methods for extracting neural trajectories involve a two-stage process: the spike trains are first smoothed over time, then a static dimensionality-reduction technique is applied. Beyond the benefit of visualizing the high-dimensional, noisy spiking activity in a compact form, such trajectories can offer insight into the dynamics of the neural circuitry underlying the recorded activity. 2.0 Upgrade with macOS 10.10 -10.We consider the problem of extracting smooth, low-dimensional neural trajectories that summarize the activity recorded simultaneously from many neurons on individual experimental trials.
#Gaussian software free trial full#
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