A biologically inspired spiking model of visual processing for image feature detection

Neuromorphic computing spiking neural network edge detection model for content based image retrieval

In contemporary times, content-based image retrieval (CBIR) techniques have gained widespread acceptance as a means for end-users to discern and extract specific image content from vast repositories. However, it is noteworthy that a substantial majority of CBIR studies continue to rely on linear methodologies such as gradient-based and derivative-based edge detection techniques. This research explores the integration of bioinspired Spiking Neural Network (SNN) based edge detection within CBIR. We introduce an innovative, computationally efficient SNN-based approach designed explicitly for CBIR applications, outperforming existing SNN models by reducing computational overhead by 2.5 times. The proposed SNN-based edge detection approach is seamlessly incorporated into three distinct CBIR techniques, each employing conventional edge detection methodologies including Sobel, Canny, and image derivatives. Rigorous experimentation and evaluations are carried out utilizing the Corel-10k dataset and crop weed dataset, a widely recognized and frequently adopted benchmark dataset in the realm of image analysis. Importantly, our findings underscore the enhanced performance of CBIR methodologies integrating the proposed SNN-based edge detection approach, with an average increase in mean precision values exceeding 3%. This study conclusively demonstrated the utility of our proposed methodology in optimizing feature extraction, thereby establishing its pivotal role in advancing edge centric CBIR approaches.

The effects of distractors on brightness perception based on a spiking network

Visual perception can be modified by the surrounding context. Particularly, experimental observations have demonstrated that visual perception and primary visual cortical responses could be modified by properties of surrounding distractors. However, the underlying mechanism remains unclear. To simulate primary visual cortical activities in this paper, we design a k-winner-take-all (k-WTA) spiking network whose responses are generated through probabilistic inference. In simulations, images with the same target and various surrounding distractors perform as stimuli. Distractors are designed with multiple varying properties, including the luminance, the sizes and the distances to the target. Simulations for each varying property are performed with other properties fixed. Each property could modify second-layer neural responses and interactions in the network. To the same target in the designed images, the modified network responses could simulate distinguishing brightness perception consistent with experimental observations. Our model provides a possible explanation of how the surrounding distractors modify primary visual cortical responses to induce various brightness perception of the given target.

Spiking Neurons Integrating Visual Stimuli Orientation and Direction Selectivity in a Robotic Context

Visual motion detection is essential for the survival of many species. The phenomenon includes several spatial properties, not fully understood at the level of a neural circuit. This paper proposes a computational model of a visual motion detector that integrates direction and orientation selectivity features. A recent experiment in the Drosophila model highlights that stimulus orientation influences the neural response of direction cells. However, this interaction and the significance at the behavioral level are currently unknown. As such, another objective of this article is to study the effect of merging these two visual processes when contextualized in a neuro-robotic model and an operant conditioning procedure. In this work, the learning task was solved using an artificial spiking neural network, acting as the brain controller for virtual and physical robots, showing a behavior modulation from the integration of both visual processes.

Biologically Inspired Intensity and Depth Image Edge Extraction

In recent years, artificial vision research has moved from focusing on the use of only intensity images to include using depth images, or RGB-D combinations due to the recent development of low-cost depth cameras. However, depth images require a lot of storage and processing requirements. In addition, it is challenging to extract relevant features from depth images in real time. Researchers have sought inspiration from biology in order to overcome these challenges resulting in biologically inspired feature extraction methods. By taking inspiration from nature, it may be possible to reduce redundancy, extract relevant features, and process an image efficiently by emulating biological visual processes. In this paper, we present a depth and intensity image feature extraction approach that has been inspired by biological vision systems. Through the use of biologically inspired spiking neural networks, we emulate functional computational aspects of biological visual systems. The results demonstrate that the proposed bioinspired artificial vision system has increased performance over existing computer vision feature extraction approaches.

Bioinspired Approach to Modeling Retinal Ganglion Cells Using System Identification Techniques

The processing capabilities of biological vision systems are still vastly superior to artificial vision, even though this has been an active area of research for over half a century. Current artificial vision techniques integrate many insights from biology yet they remain far-off the capabilities of animals and humans in terms of speed, power, and performance. A key aspect to modeling the human visual system is the ability to accurately model the behavior and computation within the retina. In particular, we focus on modeling the retinal ganglion cells (RGCs) as they convey the accumulated data of real world images as action potentials onto the visual cortex via the optic nerve. Computational models that approximate the processing that occurs within RGCs can be derived by quantitatively fitting the sets of physiological data using an input-output analysis where the input is a known stimulus and the output is neuronal recordings. Currently, these input-output responses are modeled using computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. In this paper, we illustrate how system identification techniques, which take inspiration from biological systems, can accurately model retinal ganglion cell behavior, and are a viable alternative to traditional linear-nonlinear approaches.