ArticleProceedings of the National Academy of Sciences of the United States of America2013
Synthesizing cognition in neuromorphic electronic systems.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Biologically grounded neocortex computational primitives implemented on neuromorphic hardware improve vision transformer performance.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity.Nature communications · 2025Article
- The road to commercial success for neuromorphic technologies.Nature communications · 2025Review
- Multi-gate neuron-like transistors based on ensembles of aligned nanowires on flexible substrates.Nano convergence · 2025Article
- A neuromorphic multi-scale approach for real-time heart rate and state detection.Npj unconventional computing · 2025Article
- The backpropagation algorithm implemented on spiking neuromorphic hardware.Nature communications · 2024Article
- Intelligent computation in cancer gene therapy.Frontiers in genetics · 2024Review
- Neuromorphic learning, working memory, and metaplasticity in nanowire networks.Science advances · 2023Article
- Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.Frontiers in neuroinformatics · 2023Article
- Adaptive cognition implemented with a context-aware and flexible neuron for next-generation artificial intelligence.PNAS nexus · 2022Article
- Synthetic neuromorphic computing in living cells.Nature communications · 2022Article
- Embodied neuromorphic intelligence.Nature communications · 2022Review
- Efficient Spike-Driven Learning With Dendritic Event-Based Processing.Frontiers in neuroscience · 2021Article
- Event-Based Computation for Touch Localization Based on Precise Spike Timing.Frontiers in neuroscience · 2020Article
- Inferring and validating mechanistic models of neural microcircuits based on spike-train data.Nature communications · 2019Article
- Nanosystems, Edge Computing, and the Next Generation Computing Systems.Sensors (Basel, Switzerland) · 2019Review
- A Spiking Neural Network Model of Depth from Defocus for Event-based Neuromorphic Vision.Scientific reports · 2019Article
- Review
- Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks.Neural computation · 2018Article
- CABots and Other Neural Agents.Frontiers in neurorobotics · 2018Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The quest to implement intelligent processing in electronic neuromorphic systems lacks methods for achieving reliable behavioral dynamics on substrates of inherently imprecise and noisy neurons. Here we report a solution to this problem that involves first mapping an unreliable hardware layer of spiking silicon neurons into an abstract computational layer composed of generic reliable subnetworks of model neurons and then composing the target behavioral dynamics as a "soft state machine" running on these reliable subnets. In the first step, the neural networks of the abstract layer are realized on the hardware substrate by mapping the neuron circuit bias voltages to the model parameters. This mapping is obtained by an automatic method in which the electronic circuit biases are calibrated against the model parameters by a series of population activity measurements. The abstract computational layer is formed by configuring neural networks as generic soft winner-take-all subnetworks that provide reliable processing by virtue of their active gain, signal restoration, and multistability. The necessary states and transitions of the desired high-level behavior are then easily embedded in the computational layer by introducing only sparse connections between some neurons of the various subnets. We demonstrate this synthesis method for a neuromorphic sensory agent that performs real-time context-dependent classification of motion patterns observed by a silicon retina.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.