SPIKING NEURAL NETWORK ARCHITECTURE DESIGN AND PERFORMANCE EXPLORATION TOWARDS THE DESIGN OF A SCALABLE NEURO-INSPIRED SYSTEM FOR COMPLEX COGNITION APPLICATIONS

SPIKING NEURAL NETWORK ARCHITECTURE DESIGN AND PERFORMANCE EXPLORATION TOWARDS THE DESIGN OF A SCALABLE NEURO-INSPIRED SYSTEM FOR COMPLEX COGNITION APPLICATIONS

Abstract:
Spiking Neural Networks (SNNs) have gained significant attention in recent years due to their ability to model the dynamic behavior of biological neural networks more accurately. This abstract presents an overview of the design and performance exploration of SNN architectures, aiming to develop a scalable neuro-inspired system capable of handling complex cognition applications.

The proposed research focuses on addressing the limitations of traditional neural network architectures by leveraging the temporal dynamics of spiking neurons. SNNs mimic the behavior of biological neurons by encoding information in the form of precise spike timings, allowing for more efficient and biologically plausible computation.

To design a scalable neuro-inspired system, the research investigates various aspects of SNN architecture. This includes exploring different neuron models, such as integrate-and-fire and leaky integrate-and-fire, and investigating synapse models that capture the spike-timing-dependent plasticity (STDP) observed in biological systems. Additionally, the research explores network topologies, such as layered architectures and recurrent connections, to model complex cognition tasks effectively.

Performance exploration of the SNN architecture involves evaluating its capability to handle complex cognition applications. This includes tasks such as pattern recognition, speech processing, and decision-making. The research investigates the trade-offs between model accuracy, computational efficiency, and scalability to ensure the proposed neuro-inspired system can handle real-world applications.

Furthermore, the research aims to develop efficient training algorithms for SNNs. Conventional gradient-based methods used in traditional neural networks face challenges in training SNNs due to the non-differentiable nature of spike events. The investigation encompasses novel learning algorithms, such as spike-based backpropagation and surrogate gradient methods, to enable effective training of SNN architectures.

The outcome of this research will contribute to the development of a scalable neuro-inspired system that can handle complex cognition tasks efficiently. The proposed SNN architecture, combined with optimized training algorithms, has the potential to revolutionize various domains, including artificial intelligence, robotics, and brain-computer interfaces, by enabling more biologically realistic and efficient computation.

Keywords: Spiking Neural Networks, Neuro-Inspired Systems, Complex Cognition, Scalable Architecture, Spike-Timing-Dependent Plasticity, Training Algorithms.

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