Neural Network-Based Analog-to-Digital Converters

In this chapter, we present an overview of the recent advances in analog-to-digital converter (ADC) neural networks. Biological neural networks consist of natural binarization reflected by the neurosynaptic processes. This natural analog-to-binary conversion ability of neurons can be modeled to emulate analog-to-digital conversion using a set of nonlinear circuit elements and existing artificial neural network models. Since one neuron during processing consumes on average only about half nanowatts of power, neurons can perform highly energy-efficient operations, including pattern recognition. Analog-to-digital conversion itself is an example of simple pattern recognition where input analog signal can be presented in one of the 2N different patterns for N bits. The classical configuration of neural network-based ADC is Hopfield neural network ADC. Improved designs, such as modified Hopfield network ADC, T-model neural ADC, and multilevel neurons-based neural ADC, will be discussed. In addition, the latest architecture designs of neural ADC such as hybrid complementary metal-oxide semiconductor (CMOS)-memristor Hopfield ADC are covered at the end of this chapter

Method and apparatus for predicting the direction of movement in machine vision

A computer-simulated cortical network is presented. The network is capable of computing the visibility of shifts in the direction of movement. Additionally, the network can compute the following: (1) the magnitude of the position difference between the test and background patterns; (2) localized contrast differences at different spatial scales analyzed by computing temporal gradients of the difference and sum of the outputs of paired even- and odd-symmetric bandpass filters convolved with the input pattern; and (3) the direction of a test pattern moved relative to a textured background. The direction of movement of an object in the field of view of a robotic vision system is detected in accordance with nonlinear Gabor function algorithms. The movement of objects relative to their background is used to infer the 3-dimensional structure and motion of object surfaces

Time-encoding analog-to-digital converters : bridging the analog gap to advanced digital CMOS : part 1: basic principles

The scaling of CMOS technology deep into the nanometer range has created challenges for the design of highperformance analog ICs. The shrinking supply voltage and presence of mismatch and noise restrain the dynamic range, causing analog circuits to be large in area and have a high power consumption in spite of the process scaling. Analog circuits based on time encoding [1], [2] and hybrid analog/digital signal processing [3] have been developed to overcome these issues. Realizing analog circuit functionality with highly digital circuits results in more scalable design solutions that can achieve excellent performance. This article reviews the basic principles of time encoding applied, in particular, to analog-to-digital converters (ADCs) based on voltage-controlled oscillators (VCOs), one of the most successful time-encoding techniques to date

Size constancy in bat biosonar?

Perception and encoding of object size is an important feature of sensory systems. In the visual system object size is encoded by the visual angle (visual aperture) on the retina, but the aperture depends on the distance of the object. As object distance is not unambiguously encoded in the visual system, higher computational mechanisms are needed. This phenomenon is termed "size constancy". It is assumed to reflect an automatic re-scaling of visual aperture with perceived object distance. Recently, it was found that in echolocating bats, the 'sonar aperture', i.e., the range of angles from which sound is reflected from an object back to the bat, is unambiguously perceived and neurally encoded. Moreover, it is well known that object distance is accurately perceived and explicitly encoded in bat sonar. Here, we addressed size constancy in bat biosonar, recruiting virtual-object techniques. Bats of the species Phyllostomus discolor learned to discriminate two simple virtual objects that only differed in sonar aperture. Upon successful discrimination, test trials were randomly interspersed using virtual objects that differed in both aperture and distance. It was tested whether the bats spontaneously assigned absolute width information to these objects by combining distance and aperture. The results showed that while the isolated perceptual cues encoding object width, aperture, and distance were all perceptually well resolved by the bats, the animals did not assign absolute width information to the test objects. This lack of sonar size constancy may result from the bats relying on different modalities to extract size information at different distances. Alternatively, it is conceivable that familiarity with a behaviorally relevant, conspicuous object is required for sonar size constancy, as it has been argued for visual size constancy. Based on the current data, it appears that size constancy is not necessarily an essential feature of sonar perception in bats