  In the biological nervous system, large neuronal populations work
collaboratively to encode sensory stimuli. These neuronal populations are
characterised by a diverse distribution of tuning curves, ensuring that the
entire range of input stimuli is encoded. Based on these principles, we have
designed a neuromorphic system called a Trainable Analogue Block (TAB), which
encodes given input stimuli using a large population of neurons with a
heterogeneous tuning curve profile. Heterogeneity of tuning curves is achieved
using random device mismatches in VLSI (Very Large Scale Integration) process
and by adding a systematic offset to each hidden neuron. Here, we present
measurement results of a single test cell fabricated in a 65nm technology to
verify the TAB framework. We have mimicked a large population of neurons by
re-using measurement results from the test cell by varying offset. We thus
demonstrate the learning capability of the system for various regression tasks.
The TAB system may pave the way to improve the design of analogue circuits for
commercial applications, by rendering circuits insensitive to random mismatch
that arises due to the manufacturing process.
