An accurate and flexible analog emulation of AdEx neuron dynamics in
silicon
- URL: http://arxiv.org/abs/2209.09280v1
- Date: Mon, 19 Sep 2022 18:08:23 GMT
- Title: An accurate and flexible analog emulation of AdEx neuron dynamics in
silicon
- Authors: Sebastian Billaudelle, Johannes Weis, Philipp Dauer, Johannes Schemmel
- Abstract summary: This manuscript presents the analog neuron circuits of the mixed-signal accelerated neuromorphic system BrainScaleS-2.
They are capable of flexibly and accurately emulating the adaptive exponential integrate-and-fire model equations in combination with both current- and conductance-based synapses.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Analog neuromorphic hardware promises fast brain emulation on the one hand
and an efficient implementation of novel, brain-inspired computing paradigms on
the other. Bridging this spectrum requires flexibly configurable circuits with
reliable and reproducible dynamics fostered by an accurate implementation of
the targeted neuron and synapse models. This manuscript presents the analog
neuron circuits of the mixed-signal accelerated neuromorphic system
BrainScaleS-2. They are capable of flexibly and accurately emulating the
adaptive exponential leaky integrate-and-fire model equations in combination
with both current- and conductance-based synapses, as demonstrated by precisely
replicating a wide range of complex neuronal dynamics and firing patterns.
Related papers
- Integrating programmable plasticity in experiment descriptions for analog neuromorphic hardware [0.9217021281095907]
The BrainScaleS-2 neuromorphic architecture has been designed to support "hybrid" plasticity.
observables that are expensive in numerical simulation, such as per-synapse correlation measurements, are implemented directly in the synapse circuits.
We introduce an integrated framework for describing spiking neural network experiments and plasticity rules in a unified high-level experiment description language.
arXiv Detail & Related papers (2024-12-04T08:46:06Z) - Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equations [1.5467259918426441]
We propose a framework for developing computational models of biological neural systems.
We employ a system of coupled differential equations with differentiable drift and diffusion functions.
We show that these hybrid models achieve competitive performance in predicting stimulus-evoked neural and behavioral responses.
arXiv Detail & Related papers (2024-12-01T09:36:03Z) - NeoHebbian Synapses to Accelerate Online Training of Neuromorphic Hardware [0.03377254151446239]
A novel neoHebbian artificial synapse utilizing ReRAM devices has been proposed and experimentally validated.
System-level simulations, accounting for various device and system-level non-idealities, confirm that these synapses offer a robust solution for the fast, compact, and energy-efficient implementation of advanced learning rules in neuromorphic hardware.
arXiv Detail & Related papers (2024-11-27T12:06:15Z) - A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures [73.65190161312555]
ARCANA is a spiking neural network simulator designed to account for the properties of mixed-signal neuromorphic circuits.
We show how the results obtained provide a reliable estimate of the behavior of the spiking neural network trained in software.
arXiv Detail & Related papers (2024-09-23T11:16:46Z) - Single Neuromorphic Memristor closely Emulates Multiple Synaptic
Mechanisms for Energy Efficient Neural Networks [71.79257685917058]
We demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions.
These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation.
arXiv Detail & Related papers (2024-02-26T15:01:54Z) - WaLiN-GUI: a graphical and auditory tool for neuron-based encoding [73.88751967207419]
Neuromorphic computing relies on spike-based, energy-efficient communication.
We develop a tool to identify suitable configurations for neuron-based encoding of sample-based data into spike trains.
The WaLiN-GUI is provided open source and with documentation.
arXiv Detail & Related papers (2023-10-25T20:34:08Z) - The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron.
Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters.
We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
arXiv Detail & Related papers (2023-06-14T13:34:13Z) - Mapping and Validating a Point Neuron Model on Intel's Neuromorphic
Hardware Loihi [77.34726150561087]
We investigate the potential of Intel's fifth generation neuromorphic chip - Loihi'
Loihi is based on the novel idea of Spiking Neural Networks (SNNs) emulating the neurons in the brain.
We find that Loihi replicates classical simulations very efficiently and scales notably well in terms of both time and energy performance as the networks get larger.
arXiv Detail & Related papers (2021-09-22T16:52:51Z) - Flexible Transmitter Network [84.90891046882213]
Current neural networks are mostly built upon the MP model, which usually formulates the neuron as executing an activation function on the real-valued weighted aggregation of signals received from other neurons.
We propose the Flexible Transmitter (FT) model, a novel bio-plausible neuron model with flexible synaptic plasticity.
We present the Flexible Transmitter Network (FTNet), which is built on the most common fully-connected feed-forward architecture.
arXiv Detail & Related papers (2020-04-08T06:55:12Z) - Synaptic Integration of Spatiotemporal Features with a Dynamic
Neuromorphic Processor [0.1529342790344802]
We show that a single point-neuron with dynamic synapses in the DYNAP-SENAP can respond selectively to presynaptic spikes with a particulartemporal structure.
This structure enables, for instance, visual feature tuning of single neurons.
arXiv Detail & Related papers (2020-02-12T11:26:35Z) - Structural plasticity on an accelerated analog neuromorphic hardware
system [0.46180371154032884]
We present a strategy to achieve structural plasticity by constantly rewiring the pre- and gpostsynaptic partners.
We implemented this algorithm on the analog neuromorphic system BrainScaleS-2.
We evaluated our implementation in a simple supervised learning scenario, showing its ability to optimize the network topology.
arXiv Detail & Related papers (2019-12-27T10:15:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.