ArticleFrontiers in neuroscience2011
Tunable neuromimetic integrated system for emulating cortical neuron models.
Article in Frontiers in neuroscience, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Advancing neuroengineering with Neuromorphic Twins.Nature communications · 2026Review
- Piecewise quadratic neuron model: A tool for close-to-biology spiking neuronal network simulation on dedicated hardware.Frontiers in neuroscience · 2022Article
- Optimal solid state neurons.Nature communications · 2019Article
- Optimized Real-Time Biomimetic Neural Network on FPGA for Bio-hybridization.Frontiers in neuroscience · 2019Article
- Microfluidic Neurons, a New Way in Neuromorphic Engineering?Micromachines · 2016Article
- Qualitative-Modeling-Based Silicon Neurons and Their Networks.Frontiers in neuroscience · 2016Review
- Six networks on a universal neuromorphic computing substrate.Frontiers in neuroscience · 2013Article
- Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments.Frontiers in neuroscience · 2013Article
- Neuromimetic Event-Based Detection for Closed-Loop Tactile Feedback Control of Upper Limb Prostheses.IEEE transactions on hapticsArticle
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Authors and funding
6 authors.
Funding
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Abstract
Nowadays, many software solutions are currently available for simulating neuron models. Less conventional than software-based systems, hardware-based solutions generally combine digital and analog forms of computation. In previous work, we designed several neuromimetic chips, including the Galway chip that we used for this paper. These silicon neurons are based on the Hodgkin-Huxley formalism and they are optimized for reproducing a large variety of neuron behaviors thanks to tunable parameters. Due to process variation and device mismatch in analog chips, we use a full-custom fitting method in voltage-clamp mode to tune our neuromimetic integrated circuits. By comparing them with experimental electrophysiological data of these cells, we show that the circuits can reproduce the main firing features of cortical cell types. In this paper, we present the experimental measurements of our system which mimic the four most prominent biological cells: fast spiking, regular spiking, intrinsically bursting, and low-threshold spiking neurons into analog neuromimetic integrated circuit dedicated to cortical neuron simulations. This hardware and software platform will allow to improve the hybrid technique, also called "dynamic-clamp," that consists of connecting artificial and biological neurons to study the function of neuronal circuits.
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