1,210 research outputs found
Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data
Understanding the neural implementation of complex human behaviors is one of
the major goals in neuroscience. To this end, it is crucial to find a true
representation of the neural data, which is challenging due to the high
complexity of behaviors and the low signal-to-ratio (SNR) of the signals. Here,
we propose a novel unsupervised learning framework, Neural Latent Aligner
(NLA), to find well-constrained, behaviorally relevant neural representations
of complex behaviors. The key idea is to align representations across repeated
trials to learn cross-trial consistent information. Furthermore, we propose a
novel, fully differentiable time warping model (TWM) to resolve the temporal
misalignment of trials. When applied to intracranial electrocorticography
(ECoG) of natural speaking, our model learns better representations for
decoding behaviors than the baseline models, especially in lower dimensional
space. The TWM is empirically validated by measuring behavioral coherence
between aligned trials. The proposed framework learns more cross-trial
consistent representations than the baselines, and when visualized, the
manifold reveals shared neural trajectories across trials.Comment: Accepted at ICML 202
Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience.
Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here, we describe a software toolbox-called seqNMF-with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral datas. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs
Feature-based time-series analysis
This work presents an introduction to feature-based time-series analysis. The
time series as a data type is first described, along with an overview of the
interdisciplinary time-series analysis literature. I then summarize the range
of feature-based representations for time series that have been developed to
aid interpretable insights into time-series structure. Particular emphasis is
given to emerging research that facilitates wide comparison of feature-based
representations that allow us to understand the properties of a time-series
dataset that make it suited to a particular feature-based representation or
analysis algorithm. The future of time-series analysis is likely to embrace
approaches that exploit machine learning methods to partially automate human
learning to aid understanding of the complex dynamical patterns in the time
series we measure from the world.Comment: 28 pages, 9 figure
Robot Learning from Human Demonstration: Interpretation, Adaptation, and Interaction
Robot Learning from Demonstration (LfD) is a research area that focuses on how robots can learn new skills by observing how people perform various activities. As humans, we have a remarkable ability to imitate other human’s behaviors and adapt to new situations. Endowing robots with these critical capabilities is a significant but very challenging problem considering the complexity and variation of human activities in highly dynamic environments.
This research focuses on how robots can learn new skills by interpreting human activities, adapting the learned skills to new situations, and naturally interacting with humans. This dissertation begins with a discussion of challenges in each of these three problems. A new unified representation approach is introduced to enable robots to simultaneously interpret the high-level semantic meanings and generalize the low-level trajectories of a broad range of human activities. An adaptive framework based on feature space decomposition is then presented for robots to not only reproduce skills, but also autonomously and efficiently adjust the learned skills to new environments that are significantly different from demonstrations. To achieve natural Human Robot Interaction (HRI), this dissertation presents a Recurrent Neural Network based deep perceptual control approach, which is capable of integrating multi-modal perception sequences with actions for robots to interact with humans in long-term tasks.
Overall, by combining the above approaches, an autonomous system is created for robots to acquire important skills that can be applied to human-centered applications. Finally, this dissertation concludes with a discussion of future directions that could accelerate the upcoming technological revolution of robot learning from human demonstration
Time-Warp–Invariant Neuronal Processing
A biophysical mechanism acting in auditory neurons allows the brain to process the high variability of speaking rates in natural speech in a time-warp-invariant manner
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