# Spatiotemporal Pattern Recognition in Single Mixed-Signal VLSI Neurons with Heterogeneous Dynamic Synapses

arXiv: https://arxiv.org/abs/2106.05686 · HTML: https://mlsanity.com/paper/2106.05686/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)](https://mlsanity.com/paper/1708.04198/) (2017)
- [Fast and energy-efficient neuromorphic deep learning with first-spike times](https://mlsanity.com/paper/1912.11443/) (2019)
- [Brain-inspired computing: We need a master plan](https://mlsanity.com/paper/2104.14517/) (2021)
- [Low-Power Neuromorphic Hardware for Signal Processing Applications](https://mlsanity.com/paper/1901.03690/) (2019)
- [Neuromorphic electronic circuits for building autonomous cognitive systems](https://mlsanity.com/paper/1403.6428/) (2014)
- [Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor](https://mlsanity.com/paper/1911.05521/) (2019)
- [The importance of space and time in neuromorphic cognitive agents](https://mlsanity.com/paper/1902.09791/) (2019)
- [Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors](https://mlsanity.com/paper/2102.06408/) (2021)
- [Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout](https://mlsanity.com/paper/1708.05356/) (2017)
- [Introducing "Neuromorphic Computing and Engineering"](https://mlsanity.com/paper/2106.01329/) (2021)
- [Synaptic Delays for Temporal Feature Detection in Dynamic Neuromorphic Processors](https://mlsanity.com/paper/1906.12282/) (2019)
- [Dynamic Reliability Management in Neuromorphic Computing](https://mlsanity.com/paper/2105.02038/) (2021)
- [Synaptic Integration of Spatiotemporal Features with a Dynamic Neuromorphic Processor](https://mlsanity.com/paper/2002.04924/) (2020)

## Abstract (arXiv)

Mixed-signal neuromorphic processors with brain-like organization and device physics offer an ultra-low-power alternative to the unsustainable developments of conventional deep learning and computing. However, realizing the potential of such neuromorphic hardware requires efficient use of its heterogeneous, analog neurosynaptic circuitry with neurocomputational methods for sparse, spike-timing-based encoding and processing. Here, we investigate the use of balanced excitatory-inhibitory disynaptic lateral connections as a resource-efficient mechanism for implementing a thalamocortically inspired Spatiotemporal Correlator (STC) neural network without using dedicated delay mechanisms. We present hardware-in-the-loop experiments with a DYNAP-SE neuromorphic processor, in which receptive fields of heterogeneous coincidence-detection neurons in an STC network with four lateral afferent connections per column were mapped by random input-sampling. Furthermore, we demonstrate how such a neuron was tuned to detect a particular spatiotemporal feature by discrete address-reprogramming of the analog synaptic circuits. The energy dissipation of the disynaptic connections is one order of magnitude lower per lateral connection (0.65 nJ vs 9.6 nJ per spike) than in the former delay-based hardware implementation of the STC.