Computer Engineering ePosters

Understanding the computational principles of the brain and replicating them on neuromorphic hardware and modern deep learning architectures is crucial for advancing neuro-inspired AI (NeuroAI). Here, we develop an experimentally-constrained biophysical network model of neocortical circuit motifs, focusing on layers…

The use of electromyography (EMG) for translating muscle activity into precise movements has become a pivotal technique to control myoelectric prosthetic devices, significantly improving the quality of life for amputees. Traditional EMG control of prosthetics relies on techniques that decode forearm muscle activity…

The rise of spiking neural networks (SNNs) offers a promising alternative to traditional activation-based deep neural networks, advancing the pursuit of more sustainable and ethical computing architectures. Despite its potential, neuromorphic event-based computing, which promises to fully harness the benefits of…

Despite being inspired by biological neuronal networks (BNNs), artificial neural networks (ANNs) have fundamental differences from their biological counterparts. While ANNs excel at solving complex problems across various domains, BNNs outperform them in terms of energy efficiency and processing highly complex…

The success of Artificial Neural Networks (ANNs) trained with error-backpropagation algorithms relies on the combination of offline training, vast amounts of data, and energy-intensive simulations on dedicated hardware accelerators. In contrast, biological neurons can learn quickly in a self-supervised manner by…

Spiking neural networks (SNNs) inherently rely on the timing of signals for representing and processing information. Transmission delays play an important role in shaping these temporal characteristics. Recent work [1, 2] has demonstrated the substantial advantages of learning these delays along with synaptic weights,…

Analog neuromorphic circuits emulate the dynamic properties of biological neural systems through their physics [1], sharing similarities and constraints with them [2,3]. This alignment can be leveraged for fundamental research to validate or challenge theories of neural computation as well as for applied research to…

Since the popularization of GPUs for machine learning (ML) workloads, several dedicated accelerator chips have emerged, offering architectures optimized for common ML operations and requirements. The types of tasks targeted by such platforms however - often sparse and highly parallel - are not confined to the realm of…

Well designed behavioral experiments are vital to understand the complexities of neural coding, particularly as it becomes evident that such coding may be context-specific, highly distributed, and multiplexed. Traditional commercial solutions for behavioral setups, although available, often lack flexibility and…