This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the LSM. With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression. The proposed learning rule is local such that each synaptic weight update is based only upon the firing activities of the corresponding presynaptic and postsynaptic neurons without incurring global communications across the neural network. Compared with the backpropagation-based learning, the locality of computation in the proposed approach lends itself to efficient parallel VLSI implementation. We use subsets of the TI46 speech corpus to benchmark the bioinspired digital LSM. To reduce the complexity of the spiking neural network model without performance degradation for speech recognition, we study the impacts of synaptic models on the fading memory of the reservoir and hence the network performance. Moreover, we examine the tradeoffs between synaptic weight resolution, reservoir size, and recognition performance and present techniques to further reduce the overhead of hardware implementation. Our simulation results show that in terms of isolated word recognition evaluated using the TI46 speech corpus, the proposed digital LSM rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the LSM or neural networks.
Growth capability of neurons is an essential factor in axon regeneration. To better understand how microenvironments influence axon growth, methods that allow spatial control of cellular microenvironments and easy quantification of axon growth are critically needed. Here, we present a microchip capable of physically guiding the growth directions of axons while providing physical and fluidic isolation from neuronal somata/dendrites that enables localized biomolecular treatments and linear axon growth. The microchip allows axons to grow in straight lines inside the axon compartments even after the isolation; therefore, significantly facilitating the axon length quantification process. We further developed an image processing algorithm that automatically quantifies axon growth. The effect of localized extracellular matrix components and brain-derived neurotropic factor treatments on axon growth was investigated. Results show that biomolecules may have substantially different effects on axon growth depending on where they act. For example, while chondroitin sulfate proteoglycan causes axon retraction when added to the axons, it promotes axon growth when applied to the somata. The newly developed microchip overcomes limitations of conventional axon growth research methods that lack localized control of biomolecular environments and are often performed at a significantly lower cell density for only a short period of time due to difficulty in monitoring of axonal growth. This microchip may serve as a powerful tool for investigating factors that promote axon growth and regeneration.
Accurate microvascular morphometric information has significant implications in several fields, including the quantification of angiogenesis in cancer research, understanding the immune response for neural prosthetics, and predicting the nature of blood flow as it relates to stroke. We report imaging of the whole mouse brain microvascular system at resolutions sufficient to perform accurate morphometry. Imaging was performed using Knife-Edge Scanning Microscopy (KESM) and is the first example of this technique that can be directly applied to clinical research. We are able to achieve ≈ 0.7μm resolution laterally with 1μm depth resolution using serial sectioning. No alignment was necessary and contrast was sufficient to allow segmentation and measurement of vessels.
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