Biology subjects

Tao, L.

Publications and source records attributed to Tao, L..

4 recordsLinked to original sources

bioRxiv2018

Statistical structure of locomotion and its modulation by odors

Most behaviors such as making tea are not stereotypical but have an obvious structure. However, analytical methods to objectively extract structure from non-stereotyped behaviors are immature. In this study, we analyze the locomotion of fruit flies and show that this non-stereotyped behavior is well-described by a Hierarchical Hidden Markov Model (HHMM). HHMM shows that a flys locomotion can be decomposed into a small number of locomotor features, and odors modulate locomotion by altering the time a fly spends performing different locomotor features. Importantly, although all flies in our dataset use the same set of locomotor features, individual flies vary considerably in how often they employ a given locomotor feature, and how this usage is modulated by odor. This variation is so large that the behavior of individual flies is best understood as being grouped into at least 3-5 distinct clusters, rather than variations around an average fly.

neuroscience↗

bioRxiv2018

A Mechanism for Synaptic Copy between Neural Circuits

The brain has a central, short-term learning module, the hippocampus, which transfers what it has learned to long-term memory in cortex during non-REM sleep. The putative mechanism responsible for this type of memory consolidation invokes hierarchically nested hippocampal ripples (100-250 Hz), thalamo-cortical spindles (7-15 Hz), and cortical slow oscillations (< 1 Hz) to enable transfer. Suppression of, for instance, thalamic spindles has been shown to impair hippocampus-dependent memory consolidation. Cortical oscillations are central to information transfer in neural systems. Significant evidence supports the idea that coincident spike input can allow the neural threshold to be overcome, and spikes to be propagated downstream in a circuit. Thus, an observation of oscillations in neural circuits would be an indication that repeated synchronous spiking is enabling information transfer. However, for memory transfer, in which synaptic weights must be being transferred from one neural circuit (region) to another, what is the mechanism? Here, we present a synaptic transfer mechanism whose structure provides some understanding of the phenomena that have been implicated in memory transfer, including the nested oscillations at various frequencies. The circuit is based on the principle of pulse-gated, graded information transfer between neural populations.\n\nPACS numbers: 87.18.Sn,87.19.lj,87.19.lm,87.19.lq

neuroscience↗

bioRxiv2018

A common goodness-of-fit framework for neural population models using marked point process time-rescaling

A critical component of any statistical modeling procedure is the ability to assess the goodness-of-fit between a model and observed data. For neural spike train models of individual neurons, many goodness-of-fit measures rely on the time-rescaling theorem to assess the statistical properties of rescaled spike times. Recently, there has been increasing interest in statistical models that describe the simultaneous spiking activity of neuron populations, either in a single brain region or across brain regions. Classically, such models have used spike sorted data to describe relationships between the identified neurons, but more recently clusterless modeling methods have been used to describe population activity using a single model. Here we develop a generalization of the time-rescaling theorem that enables comprehensive goodness-of-fit analysis for either of these classes of population models. We use the theory of marked point processes to model population spiking activity, and show that under the correct model, each spike can be rescaled individually to generate a uniformly distributed set of events in time and the space of spike marks. After rescaling, multiple well-established goodness-of-fit procedures and statistical tests are available. We demonstrate the application of these methods both to simulated data and real population spiking in rat hippocampus.

neuroscience↗

bioRxiv2017

A duplex MIPs-based biological-computational cell lineage discovery platform

Cell lineage analysis aims to uncover the developmental history of an organism back to its cell of origin1. Recently, novel in vivo methods and technologies utilizing genome editing enabled important insights into the cell lineages of animals2-8. In contrast, human cell lineage remains restricted to retrospective approaches, which still lack in resolution and cost-efficient solutions. Here we demonstrate a scalable platform for human cell lineage tracing based on Short Tandem Repeats (STRs) targeted by duplex Molecular Inversion Probes (MIPs). With this platform we accurately reproduced a known lineage of DU145 cell lines cells9 and reconstructed lineages of healthy and metastatic single cells from a melanoma patient. The reconstructed trees matched the anatomical and SNV references while adding further refinements. Our platform allowed to faithfully recapitulate lineages of developmental tissue formation in cells from healthy donors. In summary, our lineage discovery platform can profile informative STR somatic mutations efficiently and we provide a solid, high-resolution lineage reconstruction even in challenging low-mutation-rate healthy single cells.

systems biology↗