Adaptive visual and auditory map alignment in barn owl superior colliculus and its neuromorphic implementation

From retina to motoneurons: A substrate for visuomotor transformation in salamanders

The transformation of visual input into motor output is essential to approach a target or avoid a predator. In salamanders, visually guided orientation behaviors have been extensively studied during prey capture. However, the neural circuitry involved is not resolved. Using salamander brain preparations, calcium imaging and tracing experiments, we describe a neural substrate through which retinal input is transformed into spinal motor output. We found that retina stimulation evoked responses in reticulospinal neurons of the middle reticular nucleus, known to control steering movements in salamanders. Microinjection of glutamatergic antagonists in the optic tectum (superior colliculus in mammals) decreased the reticulospinal responses. Using tracing, we found that retina projected to the dorsal layers of the contralateral tectum, where the dendrites of neurons projecting to the middle reticular nucleus were located. In slices, stimulation of the tectal dorsal layers evoked glutamatergic responses in deep tectal neurons retrogradely labeled from the middle reticular nucleus. We then examined how tectum activation translated into spinal motor output. Tectum stimulation evoked motoneuronal responses, which were decreased by microinjections of glutamatergic antagonists in the contralateral middle reticular nucleus. Reticulospinal fibers anterogradely labeled from tracer injection in the middle reticular nucleus were preferentially distributed in proximity with the dendrites of ipsilateral motoneurons. Our work establishes a neural substrate linking visual and motor centers in salamanders. This retino‐tecto‐reticulo‐spinal circuitry is well positioned to control orienting behaviors. Our study bridges the gap between the behavioral studies and the neural mechanisms involved in the transformation of visual input into motor output in salamanders.

A Biologically Motivated, Proto-Object-Based Audiovisual Saliency Model

The natural environment and our interaction with it are essentially multisensory, where we may deploy visual, tactile and/or auditory senses to perceive, learn and interact with our environment. Our objective in this study is to develop a scene analysis algorithm using multisensory information, specifically vision and audio. We develop a proto-object-based audiovisual saliency map (AVSM) for the analysis of dynamic natural scenes. A specialized audiovisual camera with 360∘ field of view, capable of locating sound direction, is used to collect spatiotemporally aligned audiovisual data. We demonstrate that the performance of a proto-object-based audiovisual saliency map in detecting and localizing salient objects/events is in agreement with human judgment. In addition, the proto-object-based AVSM that we compute as a linear combination of visual and auditory feature conspicuity maps captures a higher number of valid salient events compared to unisensory saliency maps. Such an algorithm can be useful in surveillance, robotic navigation, video compression and related applications.

Noise-shaping gradient descent-based online adaptation algorithms for digital calibration of analog circuits

Analog circuits that are calibrated using digital-to-analog converters (DACs) use a digital signal processor-based algorithm for real-time adaptation and programming of system parameters. In this paper, we first show that this conventional framework for adaptation yields suboptimal calibration properties because of artifacts introduced by quantization noise. We then propose a novel online stochastic optimization algorithm called noise-shaping or ΣΔ gradient descent, which can shape the quantization noise out of the frequency regions spanning the parameter adaptation trajectories. As a result, the proposed algorithms demonstrate superior parameter search properties compared to floating-point gradient methods and better convergence properties than conventional quantized gradient-methods. In the second part of this paper, we apply the ΣΔ gradient descent algorithm to two examples of real-time digital calibration: 1) balancing and tracking of bias currents, and 2) frequency calibration of a band-pass Gm-C biquad filter biased in weak inversion. For each of these examples, the circuits have been prototyped in a 0.5-μm complementary metal-oxide-semiconductor process, and we demonstrate that the proposed algorithm is able to find the optimal solution even in the presence of spurious local minima, which are introduced by the nonlinear and non-monotonic response of calibration DACs.