7,071 research outputs found
Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
Spiking neural networks (SNN) are artificial computational models that have
been inspired by the brain's ability to naturally encode and process
information in the time domain. The added temporal dimension is believed to
render them more computationally efficient than the conventional artificial
neural networks, though their full computational capabilities are yet to be
explored. Recently, computational memory architectures based on non-volatile
memory crossbar arrays have shown great promise to implement parallel
computations in artificial and spiking neural networks. In this work, we
experimentally demonstrate for the first time, the feasibility to realize
high-performance event-driven in-situ supervised learning systems using
nanoscale and stochastic phase-change synapses. Our SNN is trained to recognize
audio signals of alphabets encoded using spikes in the time domain and to
generate spike trains at precise time instances to represent the pixel
intensities of their corresponding images. Moreover, with a statistical model
capturing the experimental behavior of the devices, we investigate
architectural and systems-level solutions for improving the training and
inference performance of our computational memory-based system. Combining the
computational potential of supervised SNNs with the parallel compute power of
computational memory, the work paves the way for next-generation of efficient
brain-inspired systems
Automating image analysis by annotating landmarks with deep neural networks
Image and video analysis is often a crucial step in the study of animal behavior and kinematics. Often these analyses require that the position of one or more animal landmarks are annotated (marked) in numerous images. The process of annotating landmarks can require a significant amount of time and tedious labor, which motivates the need for algorithms that can automatically annotate landmarks. In the community of scientists that use image and video analysis to study the 3D flight of animals, there has been a trend of developing more automated approaches for annotating landmarks, yet they fall short of being generally applicable. Inspired by the success of Deep Neural Networks (DNNs) on many problems in the field of computer vision, we investigate how suitable DNNs are for accurate and automatic annotation of landmarks in video datasets representative of those collected by scientists studying animals.
Our work shows, through extensive experimentation on videos of hawkmoths, that DNNs are suitable for automatic and accurate landmark localization. In particular, we show that one of our proposed DNNs is more accurate than the current best algorithm for automatic localization of landmarks on hawkmoth videos. Moreover, we demonstrate how these annotations can be used to quantitatively analyze the 3D flight of a hawkmoth. To facilitate the use of DNNs by scientists from many different fields, we provide a self contained explanation of what DNNs are, how they work, and how to apply them to other datasets using the freely available library Caffe and supplemental code that we provide.https://arxiv.org/abs/1702.00583Published versio
A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines
Information in neural networks is represented as weighted connections, or
synapses, between neurons. This poses a problem as the primary computational
bottleneck for neural networks is the vector-matrix multiply when inputs are
multiplied by the neural network weights. Conventional processing architectures
are not well suited for simulating neural networks, often requiring large
amounts of energy and time. Additionally, synapses in biological neural
networks are not binary connections, but exhibit a nonlinear response function
as neurotransmitters are emitted and diffuse between neurons. Inspired by
neuroscience principles, we present a digital neuromorphic architecture, the
Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex
synaptic response functions without requiring additional hardware components.
We consider the paradigm of spiking neurons with temporally coded information
as opposed to non-spiking rate coded neurons used in most neural networks. In
this paradigm we examine liquid state machines applied to speech recognition
and show how a liquid state machine with temporal dynamics maps onto the
STPU-demonstrating the flexibility and efficiency of the STPU for instantiating
neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN
BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections
We introduce an architecture for large-scale image categorization that
enables the end-to-end learning of separate visual features for the different
classes to distinguish. The proposed model consists of a deep CNN shaped like a
tree. The stem of the tree includes a sequence of convolutional layers common
to all classes. The stem then splits into multiple branches implementing
parallel feature extractors, which are ultimately connected to the final
classification layer via learned gated connections. These learned gates
determine for each individual class the subset of features to use. Such a
scheme naturally encourages the learning of a heterogeneous set of specialized
features through the separate branches and it allows each class to use the
subset of features that are optimal for its recognition. We show the generality
of our proposed method by reshaping several popular CNNs from the literature
into our proposed architecture. Our experiments on the CIFAR100, CIFAR10, and
Synth datasets show that in each case our resulting model yields a substantial
improvement in accuracy over the original CNN. Our empirical analysis also
suggests that our scheme acts as a form of beneficial regularization improving
generalization performance.Comment: WACV 201
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