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Exploiting Heterogeneity in Neuromorphic Systems for Robust Analog Computing
Neuromorphic computing represents a cutting-edge approach to designing computational systems by mimicking the architecture and functionality of biological neurons. One of the persistent challenges in fabricating neuromorphic devices is the cross-device response variability, which is often seen as a limitation. However, biological neurons and synapses are intrinsically heterogeneous, exhibiting a wide spectrum of responses that enhance robustness and adaptability. Inspired by this, recent computational study[1] demonstrated that neural networks composed of heterogeneous neurons—without the need for plasticity—significantly outperform their homogeneous counterparts, particularly in their reliability across a range of temporal tasks.
[1] Golmohammadi et al 2024, https://arxiv.org/abs/2412.05126
[2] Zendrikov et al, 2023 10.1088/2634-4386/ace64c
Keywords: neuromorphic computing, heterogenity
Project Objectives:
Analog Implementation of Heterogeneous Neuron Networks: The first objective is to translate the computational algorithm we developed for networks with static heterogeneous neurons, onto analog hardware (Dynap-SE)[2]. This will provide the foundation for real-world neuromorphic systems that exploit the variability of analog components, paralleling the natural diversity seen in biological neurons.
Extension to Networks with Plastic Synapses: Building on the static network model, the next step is to integrate synaptic plasticity into the system. In this phase, synaptic weights will evolve based on local, unsupervised learning rules driven by neuronal activity at the synapse terminals, simulating the adaptive nature of biological systems.
Adaptation to Non-Stationary Inputs: Finally, we will extend the framework to handle non-stationary inputs, investigating how heterogeneous networks with plastic synapses can dynamically adapt to changing input distributions, further incre
Project Objectives: Analog Implementation of Heterogeneous Neuron Networks: The first objective is to translate the computational algorithm we developed for networks with static heterogeneous neurons, onto analog hardware (Dynap-SE)[2]. This will provide the foundation for real-world neuromorphic systems that exploit the variability of analog components, paralleling the natural diversity seen in biological neurons. Extension to Networks with Plastic Synapses: Building on the static network model, the next step is to integrate synaptic plasticity into the system. In this phase, synaptic weights will evolve based on local, unsupervised learning rules driven by neuronal activity at the synapse terminals, simulating the adaptive nature of biological systems. Adaptation to Non-Stationary Inputs: Finally, we will extend the framework to handle non-stationary inputs, investigating how heterogeneous networks with plastic synapses can dynamically adapt to changing input distributions, further incre
-This finding suggests that embracing the inherent diversity of analog devices, rather than suppressing it, can unlock significant performance benefits. The implications extend well beyond theory, providing a compelling opportunity to harness this diversity for real-world neuromorphic applications.
-This finding suggests that embracing the inherent diversity of analog devices, rather than suppressing it, can unlock significant performance benefits. The implications extend well beyond theory, providing a compelling opportunity to harness this diversity for real-world neuromorphic applications.
Chiara De Luca: chiara.deluca@ini.uzh.ch
Prof. Giacomo Indiveri: giacomo@ini.uzh.ch
Chiara De Luca: chiara.deluca@ini.uzh.ch Prof. Giacomo Indiveri: giacomo@ini.uzh.ch
