Implementing homeostatic plasticity in VLSI networks of spiking neurons

Homeostatic plasticity acts to stabilize firing activity in neural systems, ensuring a homogeneous computational substrate despite the inherent differences among neurons and their continuous change. These types of mechanisms are extremely relevant for any physical implementation of neural systems. They can be used in VLSI pulse-based neural networks to automatically adapt to chronic input changes, device mismatch, as well as slow systematic changes in the circuitpsilas functionality (e.g. due to temperature drifts). In this paper we propose analog circuits for implementing homeostatic plasticity mechanisms in VLSI spiking neural networks, compatible with local spike-based learning mechanisms. We show experimental results where a homeostatic control is implemented as a hybrid SoftWare/HardWare (SW/HW) solution, and present analog circuits for a complete on-chip stand-alone solution, validated by circuit simulations.