# Neuromorphic electronic circuits for building autonomous cognitive systems

arXiv: https://arxiv.org/abs/1403.6428 · HTML: https://mlsanity.com/paper/1403.6428/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2014.

## Built on by (most cited first, 19 of 19)

- [Sparse Spiking Gradient Descent](https://mlsanity.com/paper/2105.08810/) (2021)
- [DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor](https://mlsanity.com/paper/2310.00564/) (2023)
- [Meta-learning Spiking Neural Networks with Surrogate Gradient Descent](https://mlsanity.com/paper/2201.10777/) (2022)
- [Visual Odometry with Neuromorphic Resonator Networks](https://mlsanity.com/paper/2209.02000/) (2022)
- [Gradient-descent hardware-aware training and deployment for mixed-signal Neuromorphic processors](https://mlsanity.com/paper/2303.12167/) (2023)
- [Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware](https://mlsanity.com/paper/2405.01305/) (2024)
- [A feedback control optimizer for online and hardware-aware training of Spiking Neural Networks](https://mlsanity.com/paper/2602.13261/) (2026)
- [Spike-based computation using classical recurrent neural networks](https://mlsanity.com/paper/2306.03623/) (2023)
- [Neuromorphic dreaming: A pathway to efficient learning in artificial agents](https://mlsanity.com/paper/2405.15616/) (2024)
- [Spatiotemporal Pattern Recognition in Single Mixed-Signal VLSI Neurons with Heterogeneous Dynamic Synapses](https://mlsanity.com/paper/2106.05686/) (2021)
- [Demonstration of Superconducting Optoelectronic Single-Photon Synapses](https://mlsanity.com/paper/2204.09665/) (2022)
- [Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing](https://mlsanity.com/paper/2408.15800/) (2024)
- [A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures](https://mlsanity.com/paper/2409.14918/) (2024)
- [TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration](https://mlsanity.com/paper/2410.15854/) (2024)
- [Event-Driven Implementation of a Physical Reservoir Computing Framework for superficial EMG-based Gesture Recognition](https://mlsanity.com/paper/2503.13492/) (2025)
- [Towards efficient keyword spotting using spike-based time difference encoders](https://mlsanity.com/paper/2503.15402/) (2025)
- [Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach](https://mlsanity.com/paper/2509.21346/) (2025)
- [Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks](https://mlsanity.com/paper/2603.24113/) (2026)
- [Neuromorphic Object Detection: An In-Depth Study and Future Directions](https://mlsanity.com/paper/2607.23576/) (2026)

## Abstract (arXiv)

Several analog and digital brain-inspired electronic systems have been recently proposed as dedicated solutions for fast simulations of spiking neural networks. While these architectures are useful for exploring the computational properties of large-scale models of the nervous system, the challenge of building low-power compact physical artifacts that can behave intelligently in the real-world and exhibit cognitive abilities still remains open. In this paper we propose a set of neuromorphic engineering solutions to address this challenge. In particular, we review neuromorphic circuits for emulating neural and synaptic dynamics in real-time and discuss the role of biophysically realistic temporal dynamics in hardware neural processing architectures; we review the challenges of realizing spike-based plasticity mechanisms in real physical systems and present examples of analog electronic circuits that implement them; we describe the computational properties of recurrent neural networks and show how neuromorphic Winner-Take-All circuits can implement working-memory and decision-making mechanisms. We validate the neuromorphic approach proposed with experimental results obtained from our own circuits and systems, and argue how the circuits and networks presented in this work represent a useful set of components for efficiently and elegantly implementing neuromorphic cognition.