# Brain-inspired computing: We need a master plan

arXiv: https://arxiv.org/abs/2104.14517 · HTML: https://mlsanity.com/paper/2104.14517/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 52 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)](https://mlsanity.com/paper/1708.04198/) (2017)
- [Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices](https://mlsanity.com/paper/1603.07341/) (2016)
- [Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain](https://mlsanity.com/paper/1805.08932/) (2018)
- [Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System](https://mlsanity.com/paper/1703.01909/) (2017)
- [Multiscale Co-Design Analysis of Energy, Latency, Area, and Accuracy of a ReRAM Analog Neural Training Accelerator](https://mlsanity.com/paper/1707.09952/) (2017)
- [Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing](https://mlsanity.com/paper/2004.14942/) (2020)
- [A neuromorphic hardware architecture using the Neural Engineering Framework for pattern recognition](https://mlsanity.com/paper/1507.05695/) (2015)

## Built on by (most cited first, 30 of 51)

- [Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline](https://mlsanity.com/paper/2304.06793/) (2023)
- [Large Language Models and the Reverse Turing Test](https://mlsanity.com/paper/2207.14382/) (2022)
- [Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time](https://mlsanity.com/paper/2112.11231/) (2021)
- [SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms](https://mlsanity.com/paper/2406.03287/) (2024)
- [SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking](https://mlsanity.com/paper/2407.04752/) (2024)
- [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)
- [Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment](https://mlsanity.com/paper/2111.06862/) (2021)
- [Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference](https://mlsanity.com/paper/2206.06047/) (2022)
- [Controlling Chaos Using Edge Computing Hardware](https://mlsanity.com/paper/2406.12876/) (2024)
- [Genes in Intelligent Agents](https://mlsanity.com/paper/2306.10225/) (2023)
- [Multiplexed gradient descent: Fast online training of modern datasets on hardware neural networks without backpropagation](https://mlsanity.com/paper/2303.03986/) (2023)
- [HyperSense: Hyperdimensional Intelligent Sensing for Energy-Efficient Sparse Data Processing](https://mlsanity.com/paper/2401.10267/) (2024)
- [Intelligent Computing: The Latest Advances, Challenges and Future](https://mlsanity.com/paper/2211.11281/) (2022)
- [Leveraging dendritic properties to advance machine learning and neuro-inspired computing](https://mlsanity.com/paper/2306.08007/) (2023)
- [A Review of Neuroscience-Inspired Machine Learning](https://mlsanity.com/paper/2403.18929/) (2024)
- [Context-sensitive neocortical neurons transform the effectiveness and efficiency of neural information processing](https://mlsanity.com/paper/2207.07338/) (2022)
- [Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics](https://mlsanity.com/paper/2209.10481/) (2022)
- [Human Body Digital Twin: A Master Plan](https://mlsanity.com/paper/2307.09225/) (2023)
- [Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Data](https://mlsanity.com/paper/2402.03319/) (2024)
- [A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network](https://mlsanity.com/paper/2403.03739/) (2024)
- [A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission](https://mlsanity.com/paper/2402.02043/) (2024)
- [Measurement-driven neural-network training for integrated magnetic tunnel junction arrays](https://mlsanity.com/paper/2312.06446/) (2023)
- [Conflict-free joint decision by lag and zero-lag synchronization in laser network](https://mlsanity.com/paper/2307.15373/) (2023)
- [A differentiable brain simulator bridging brain simulation and brain-inspired computing](https://mlsanity.com/paper/2311.05106/) (2023)
- [Blending Optimal Control and Biologically Plausible Learning for Noise-Robust Physical Neural Networks](https://mlsanity.com/paper/2502.19053/) (2025)
- [Function regression using the forward forward training and inferring paradigm](https://mlsanity.com/paper/2510.06762/) (2025)
- [Energy-Efficient and Dequantization-Free Q-LLMs: A Spiking Neural Network Approach to Salient Value Mitigation](https://mlsanity.com/paper/2510.19498/) (2025)
- [Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent](https://mlsanity.com/paper/2606.03935/) (2026)
- [Vision for Bosnia and Herzegovina in Artificial Intelligence Age: Global Trends, Potential Opportunities, Selected Use-cases and Realistic Goals](https://mlsanity.com/paper/2209.03990/) (2022)
- [Intelligence-Endogenous Management Platform for Computing and Network Convergence](https://mlsanity.com/paper/2308.03450/) (2023)

## Abstract (arXiv)

New computing technologies inspired by the brain promise fundamentally different ways to process information with extreme energy efficiency and the ability to handle the avalanche of unstructured and noisy data that we are generating at an ever-increasing rate. To realise this promise requires a brave and coordinated plan to bring together disparate research communities and to provide them with the funding, focus and support needed. We have done this in the past with digital technologies; we are in the process of doing it with quantum technologies; can we now do it for brain-inspired computing?