Persistent and polarised global actin flow is essential for directionality during cell migration

Cell migration is hypothesized to involve a cycle of behaviours beginning with leading edge extension. However, recent evidence suggests that the leading edge may be dispensable for migration, raising the question of what actually controls cell directionality. Here, we exploit the embryonic migration of Drosophila macrophages to bridge the different temporal scales of the behaviours controlling motility. This approach reveals that edge fluctuations during random motility are not persistent and are weakly correlated with motion. In contrast, flow of the actin network behind the leading edge is highly persistent. Quantification of actin flow structure during migration reveals a stable organization and asymmetry in the cell-wide flowfield that strongly correlates with cell directionality. This organization is regulated by a gradient of actin network compression and destruction, which is controlled by myosin contraction and cofilin-mediated disassembly. It is this stable actin-flow polarity, which integrates rapid fluctuations of the leading edge, that controls inherent cellular persistence

Synaptic Learning Models of Map Separation in the Hippocampus

When rats trained to forage in one environment are exposed to a second, highly similar environment, their hippocampal place code exhibits a partial remapping in the new environment that becomes more complete with repeated exposures (Tanila, Shapiro, and Eichenbaum, 1997; Bostock, Muller, and Kubie, 1991). If the perforant path projection to CA3 functions as a pattern completion mechanism, and the DG projection via the mossy bers performs pattern separation (O'Reilly and McClelland, 1994), then partial remapping can be understood as the combined eect of these two projections. We investigated learning rules that could be responsible for the gradual separation of two maps, and found that, while simple Hebbian learning and Hebbian covariance learning would not produce the separation eect, BCM learning was one rule that would. Key words: Hippocampus, LTP, Cognitive maps, Plasticity 1 Introduction Physiological evidence suggests that place cells in the dentate gyrus, CA3, and CA1 of th..

DETECTING BANDLIMITED AUDIO IN BROADCAST TELEVISION SHOWS

For TV and radio shows containing narrowband speech, Speech-to-text (STT) accuracy on the narrowband audio can be improved by using an acoustic model trained on acoustically matched data. To selectively apply it, one must �rst be able to accurately detect which audio segments are narrowband. The present paper explores two different bandwidth classi�cation approaches: a traditional Gaussian mixture model (GMM) approach and a spline-based classi�er that categorizes audio segments based on their power spectra. We focus on shows found in the DARPA GALE Mandarin training and test sets, where the ratio of wideband to narrowband shows is very large. In this setting, the spline-based classi�er reduces the number of misclassi�ed wideband segments by up to 95 % relative to the GMM-based classi�er for the same number of misclassi�ed narrowband segments. Index Terms — Speech processing, speech recognition, pattern classi�cation, telephon

Low-resource Low-footprint Wake-word Detection using Knowledge Distillation

As virtual assistants have become more diverse and specialized, so has the demand for application or brand-specific wake words. However, the wake-word-specific datasets typically used to train wake-word detectors are costly to create. In this paper, we explore two techniques to leverage acoustic modeling data for large-vocabulary speech recognition to improve a purpose-built wake-word detector: transfer learning and knowledge distillation. We also explore how these techniques interact with time-synchronous training targets to improve detection latency. Experiments are presented on the open-source "Hey Snips" dataset and a more challenging in-house far-field dataset. Using phone-synchronous targets and knowledge distillation from a large acoustic model, we are able to improve accuracy across dataset sizes for both datasets while reducing latency.Comment: Accepted to INTERSPEECH 202