Wide dynamic range weights and biologically realistic synaptic dynamics for spike-based learning circuits
- Conference Paper
Spike-based neuromorphic learning circuits typically represent their synaptic weights as voltages, and convert them into post-synaptic currents so that they can be integrated by their afferent silicon neuron. This voltage-to-current conversion is often done using a single transistor. This results in an exponential (for weak-inversion) or quadratic (for strong inversion) non-linear transformation which severely restricts the type of learning algorithms that can be implemented. To overcome this problem we propose a range of solutions that perform a linear transformation fro m weight voltage to synaptic current, simplifying the implementation of a spike-based learning rules. We demonstrate the application of these conversion circuits using current-mode integrators that produce alpha-functions with biologically realistic temporal dynamics and amplitudes that are linearly proportional to the synaptic weights. The circuits proposed are low-power, and can be integrated in a wide range of spike-based learning framework s that have been recently proposed. We describe the advantages and disadvantages of the various solutions proposed and validate them with circuit simulation results. Show more
Book title2016 IEEE International Symposium on Circuits and Systems (ISCAS)
Pages / Article No.
Organisational unit03453 - Douglas, Rodney J.
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