Analog VLSI and neural systems
Analog VLSI and neural systems
Analog VLSI: Circuits and Principles
Analog VLSI: Circuits and Principles
Synergies Between Intrinsic and Synaptic Plasticity Mechanisms
Neural Computation
A hardware/software framework for real-time spiking systems
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Silicon synaptic adaptation mechanisms for homeostasis and contrast gain control
IEEE Transactions on Neural Networks
Synchrony detection and amplification by silicon neurons with STDP synapses
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Model of biological ANN based on homeostatic neurons
NNECFSIC'12 Proceedings of the 12th WSEAS international conference on Neural networks, fuzzy systems, evolutionary computing & automation
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Synaptic homeostasis is a mechanism present in biological neural systems that acts to maintain an homogeneous and stable computational substrate, in face of intrinsic inhomogeneities among neurons, and of their continuous changes due to learning processes and variations in the statistics of the input signals. In hardware spike-based neural networks homeostasis could be useful for solving issues such as mismatch and temperature drifts. Here we present a synaptic circuit that supports both spike-based learning and homeostatic mechanisms, and show how it can be used in conjunction with a software control algorithm to model global synaptic scaling homeostatic mechanism.