# Memory and information processing in neuromorphic systems

arXiv: https://arxiv.org/abs/1506.03264 · HTML: https://mlsanity.com/paper/1506.03264/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2015.

## Built on by (most cited first, 30 of 48)

- [Sparse Spiking Gradient Descent](https://mlsanity.com/paper/2105.08810/) (2021)
- [Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence](https://mlsanity.com/paper/2106.01288/) (2021)
- [Deep Reinforcement Learning with Spiking Q-learning](https://mlsanity.com/paper/2201.09754/) (2022)
- [Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks](https://mlsanity.com/paper/2305.16044/) (2023)
- [Neuromorphic Visual Scene Understanding with Resonator Networks](https://mlsanity.com/paper/2208.12880/) (2022)
- [Fully Spiking Actor Network with Intra-layer Connections for Reinforcement Learning](https://mlsanity.com/paper/2401.05444/) (2024)
- [Spiking neural networks for nonlinear regression](https://mlsanity.com/paper/2210.03515/) (2022)
- [DelGrad: Exact event-based gradients for training delays and weights on spiking neuromorphic hardware](https://mlsanity.com/paper/2404.19165/) (2024)
- [Visual Odometry with Neuromorphic Resonator Networks](https://mlsanity.com/paper/2209.02000/) (2022)
- [Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence](https://mlsanity.com/paper/2411.15243/) (2024)
- [Equilibrium Propagation for Non-Conservative Systems](https://mlsanity.com/paper/2602.03670/) (2026)
- [Quantum Computing and Neuromorphic Computing for Safe, Reliable, and explainable Multi-Agent Reinforcement Learning: Optimal Control in Autonomous Robotics](https://mlsanity.com/paper/2408.03884/) (2024)
- [Spiking Neural Networks: The Future of Brain-Inspired Computing](https://mlsanity.com/paper/2510.27379/) (2025)
- [A feedback control optimizer for online and hardware-aware training of Spiking Neural Networks](https://mlsanity.com/paper/2602.13261/) (2026)
- [Neuromorphic Auditory Perception by Neural Spiketrum](https://mlsanity.com/paper/2309.05430/) (2023)
- [One-Time Soft Alignment Enables Resilient Learning without Weight Transport](https://mlsanity.com/paper/2505.20892/) (2025)
- [Quantum-like Coherence Derived from the Interaction between Chemical Reaction and Its Environment](https://mlsanity.com/paper/2509.01021/) (2025)
- [Random Spiking Neural Networks are Stable and Spectrally Simple](https://mlsanity.com/paper/2511.00904/) (2025)
- [SGEMAS: A Self-Growing Ephemeral Multi-Agent System for Unsupervised Online Anomaly Detection via Entropic Homeostasis](https://mlsanity.com/paper/2512.14708/) (2025)
- [Sharpness Aware Surrogate Training for Spiking Neural Networks](https://mlsanity.com/paper/2603.18039/) (2026)
- [Sharpness-Aware Surrogate Training for On-Sensor Spiking Neural Networks](https://mlsanity.com/paper/2604.09696/) (2026)
- [What is Cognitive Computing? An Architecture and State of The Art](https://mlsanity.com/paper/2301.00882/) (2023)
- [A method for the ethical analysis of brain-inspired AI](https://mlsanity.com/paper/2305.10938/) (2023)
- [Neuromorphic Spintronics](https://mlsanity.com/paper/2409.10290/) (2024)
- [Neuromorphic IoT Architecture for Efficient Water Management: A Smart Village Case Study](https://mlsanity.com/paper/2410.19562/) (2024)
- [Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net](https://mlsanity.com/paper/2411.14585/) (2024)
- [Ultra-Low-Power Spiking Neurons in 7 nm FinFET Technology: A Comparative Analysis of Leaky Integrate-and-Fire, Morris-Lecar, and Axon-Hillock Architectures](https://mlsanity.com/paper/2505.03764/) (2025)
- [Learning with Spike Synchrony in Spiking Neural Networks](https://mlsanity.com/paper/2505.14841/) (2025)
- [Training of Spiking Neural Networks with Expectation-Propagation](https://mlsanity.com/paper/2506.23757/) (2025)
- [From Propagator to Oscillator: The Dual Role of Symmetric Differential Equations in Neural Systems](https://mlsanity.com/paper/2507.22916/) (2025)

## Abstract (arXiv)

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.