Performance of biologically grounded models of the early visual system on standard object recognition tasks

Spike-VisNet: A novel framework for visual recognition with FocusLayer-STDP learning

Current vision-inspired spiking neural networks (SNNs) face key challenges due to their model structures typically focusing on single mechanisms and neglecting the integration of multiple biological features. These limitations, coupled with limited synaptic plasticity, hinder their ability to implement biologically realistic visual processing. To address these issues, we propose Spike-VisNet, a novel retina-inspired framework designed to enhance visual recognition capabilities. This framework simulates both the functional and layered structure of the retina. To further enhance this architecture, we integrate the FocusLayer-STDP learning rule, allowing Spike-VisNet to dynamically adjust synaptic weights in response to varying visual stimuli. This rule combines channel attention, inhibition mechanisms, and competitive mechanisms with spike-timing-dependent plasticity (STDP), significantly improving synaptic adaptability and visual recognition performance. Comprehensive evaluations on benchmark datasets demonstrate that Spike-VisNet outperforms other STDP-based SNNs, achieving precision scores of 98.6% on MNIST, 93.29% on ETH-80, and 86.27% on CIFAR-10. These results highlight its effectiveness and robustness, showcasing Spike-VisNet's potential to simulate human visual processing and its applicability to complex real-world visual challenges.

Neuroscientific insights about computer vision models: a concise review

The development of biologically-inspired computational models has been the focus of study ever since the artificial neuron was introduced by McCulloch and Pitts in 1943. However, a scrutiny of literature reveals that most attempts to replicate the highly efficient and complex biological visual system have been futile or have met with limited success. The recent state-of the-art computer vision models, such as pre-trained deep neural networks and vision transformers, may not be biologically inspired per se. Nevertheless, certain aspects of biological vision are still found embedded, knowingly or unknowingly, in the architecture and functioning of these models. This paper explores several principles related to visual neuroscience and the biological visual pathway that resonate, in some manner, in the architectural design and functioning of contemporary computer vision models. The findings of this survey can provide useful insights for building futuristic bio-inspired computer vision models. The survey is conducted from a historical perspective, tracing the biological connections of computer vision models starting with the basic artificial neuron to modern technologies such as deep convolutional neural network (CNN) and spiking neural networks (SNN). One spotlight of the survey is a discussion on biologically plausible neural networks and bio-inspired unsupervised learning mechanisms adapted for computer vision tasks in recent times.

A large-scale neurocomputational model of spatial cognition integrating memory with vision

We introduce a large-scale neurocomputational model of spatial cognition called 'Spacecog', which integrates recent findings from mechanistic models of visual and spatial perception. As a high-level cognitive ability, spatial cognition requires the processing of behaviourally relevant features in complex environments and, importantly, the updating of this information during processes of eye and body movement. The Spacecog model achieves this by interfacing spatial memory and imagery with mechanisms of object localisation, saccade execution, and attention through coordinate transformations in parietal areas of the brain. We evaluate the model in a realistic virtual environment where our neurocognitive model steers an agent to perform complex visuospatial tasks. Our modelling approach opens up new possibilities in the assessment of neuropsychological data and human spatial cognition.