  The stringent memory and power constraints required in edge-computing
sensory-processing applications have made event-driven neuromorphic systems a
promising technology. On-chip online learning provides such systems the ability
to learn the statistics of the incoming data and to adapt to their changes.
Implementing online learning on event driven-neuromorphic systems requires (i)
a spike-based learning algorithm that calculates the weight updates using only
local information from streaming data, (ii) mapping these weight updates onto
limited bit precision memory and (iii) doing so in a robust manner that does
not lead to unnecessary updates as the system is reaching its optimal output.
Recent neuroscience studies have shown how dendritic compartments of cortical
neurons can solve these problems in biological neural networks. Inspired by
these studies we propose spike-based learning circuits to implement stochastic
dendritic online learning. The circuits are embedded in a prototype spiking
neural network fabricated using a 180nm process. Following an
algorithm-circuits co-design approach we present circuits and behavioral
simulation results that demonstrate the learning rule features. We validate the
proposed method using behavioral simulations of a single-layer network with
4-bit precision weights applied to the MNIST benchmark and demonstrating
results that reach accuracy levels above 85%.
