The human brain remains the premier exemplar of energy-efficient information processing, performing complex cognitive tasks while consuming approximately 20 Watts of power, roughly the energy needed to power a dim lightbulb. In stark contrast, modern deep learning models and large-scale artificial intelligence (AI) systems require massive computational resources and cooling infrastructure, leading to significant environmental and economic costs. This disparity has sparked a renewed interest in understanding the fundamental biological principles that govern metabolic efficiency. Biological systems utilize a suite of strategies, including event-based communication via spikes, sparse coding, local learning rules, and homeostatic regulation, to minimize energy expenditure without sacrificing performance. As the demand for edge computing and sustainable AI grows, computational neuroscience is uniquely positioned to bridge the gap between these biological insights and the development of the next generation of low-power neuromorphic hardware and algorithmic frameworks.

The primary objective of this Research Topic is to address the unsustainable energy trajectory of modern neural computation. While artificial neural networks have achieved human-level performance in specific domains, they often lack the structural and functional efficiency inherent in biological networks. To achieve true energy-efficient computation, we must move beyond traditional von Neumann architectures and explore "brain-inspired" paradigms.

This Research Topic aims to foster a multidisciplinary dialogue between neuroscientists, computer scientists, and hardware engineers to identify the core components of neural efficiency. We seek to explore how biological constraints, such as metabolic costs, signal noise, and limited connectivity, actually serve as drivers for robust and efficient learning. By integrating biological models of synaptic plasticity with spiking neural networks (SNNs) and neuromorphic engineering, we aim to establish a framework for hardware-software co-design that minimizes energy footprints. Ultimately, this collection intends to showcase how the marriage of biological principles and innovative engineering can pave the way for ubiquitous, sustainable, and intelligent systems.

We welcome articles that explore the intersection of neuroscience and energy-efficient computing. We particularly encourage submissions that utilize computational modeling, theoretical analysis, or neuromorphic implementation to address metabolic and energy constraints in intelligence.

Specific themes of interest include, but are not limited to:
- Models of energy-efficient coding: Including temporal coding, sparse representations, and burst firing.
- Energy-aware learning rules: Investigating the metabolic efficiency of STDP, local learning, and gradient-based approximations in SNNs.
- Neuromorphic hardware implementations: Evaluations of silicon-based architectures inspired by biological neural efficiency.
- The role of circuit motifs: How inhibitory circuits and homeostatic scaling contribute to energy regulation.
- Accuracy-Efficiency Trade-offs: Quantitative analyses of the balance between computational precision, robustness, and metabolic cost.
- Comparative Studies: Benchmarking biological neural efficiency against state-of-the-art deep learning architectures.
- Event-driven computation: New algorithms and frameworks for asynchronous, spike-based processing.

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Energy-Efficient Neural Computation