A Neuron as a Signal Processing Device

A neuron is a basic physiological and computational unit of the brain. While much is known about the physiological properties of a neuron, its computational role is poorly understood. Here we propose to view a neuron as a signal processing device that represents the incoming streaming data matrix as a sparse vector of synaptic weights scaled by an outgoing sparse activity vector. Formally, a neuron minimizes a cost function comprising a cumulative squared representation error and regularization terms. We derive an online algorithm that minimizes such cost function by alternating between the minimization with respect to activity and with respect to synaptic weights. The steps of this algorithm reproduce well-known physiological properties of a neuron, such as weighted summation and leaky integration of synaptic inputs, as well as an Oja-like, but parameter-free, synaptic learning rule. Our theoretical framework makes several predictions, some of which can be verified by the existing data, others require further experiments. Such framework should allow modeling the function of neuronal circuits without necessarily measuring all the microscopic biophysical parameters, as well as facilitate the design of neuromorphic electronics.Comment: 2013 Asilomar Conference on Signals, Systems and Computers, see http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=681029

CMOS circuit implementations for neuron models

The mathematical neuron basic cells used as basic cells in popular neural network architectures and algorithms are discussed. The most popular neuron models (without training) used in neural network architectures and algorithms (NNA) are considered, focusing on hardware implementation of neuron models used in NAA, and in emulation of biological systems. Mathematical descriptions and block diagram representations are utilized in an independent approach. Nonoscillatory and oscillatory models are discusse

Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure

Hysteresis based neural oscillators for VLSI implementations

The actual tendency in most of the work that is being done in VLSI neural network research is to use the simplest possible models to perform the desired tasks. This yields to the use of sigmoidal type neurons that have a static input-output relationship. However, in some cases, especially when the research is close to biological neuron systems emulation, such simplifled models are not always valid. In these cases, a neuron model closer to biological neurons is needed, namely the oscillatory neuron [l-41. For these neurons, when they are active, their output is a sequence of pulses. In this paper we present several circuits that can be set in an active state to yield an oscillatory output. The difference between the circuits is based on whether the output has two unique output states (off, or on flring at a specific frequency), or has a continuum between the on and off states so t h a t the output frequency changes sigmoidally between zero and its maximum

Diurnal temperature variations affect development of a herbivorous arthropod pest and its predators

The impact of daily temperature variations on arthropod life history remains woefully understudied compared to the large body of research that has been carried out on the effects of constant temperatures. However, diurnal varying temperature regimes more commonly represent the environment in which most organisms thrive. Such varying temperature regimes have been demonstrated to substantially affect development and reproduction of ectothermic organisms, generally in accordance with Jensen's inequality. In the present study we evaluated the impact of temperature alternations at 4 amplitudes (DTR0, +5, +10 and + 15 degrees C) on the developmental rate of the predatory mites Phytoseiulus persimilis Athias-Henriot and Neoseiulus californicus McGregor (Acari: Phytoseiidae) and their natural prey, the two-spotted spider mite Tetranychus urticae Koch (Acari: Tetranychidae). We have modelled their developmental rates as a function of temperature using both linear and nonlinear models. Diurnally alternating temperatures resulted in a faster development in the lower temperature range as compared to their corresponding mean constant temperatures, whereas the opposite was observed in the higher temperature range. Our results indicate that Jensen's inequality does not suffice to fully explain the differences in developmental rates at constant and alternating temperatures, suggesting additional physiological responses play a role. It is concluded that diurnal temperature range should not be ignored and should be incorporated in predictive models on the phenology of arthropod pests and their natural enemies and their performance in biological control programmes