  Context-sensitive two-point layer 5 pyramidal cells (L5PCs) were discovered
as long ago as 1999. However, the potential of this discovery to provide useful
neural computation has yet to be demonstrated. Here we show for the first time
how a transformative L5PCs-driven deep neural network (DNN), termed the
multisensory cooperative computing (MCC) architecture, can effectively process
large amounts of heterogeneous real-world audio-visual (AV) data, using far
less energy compared to best available 'point' neuron-driven DNNs. A novel
highly-distributed parallel implementation on a Xilinx UltraScale+ MPSoC device
estimates energy savings up to 245759 $ \times $ 50000 $\mu$J (i.e., 62% less
than the baseline model in a semi-supervised learning setup) where a single
synapse consumes $8e^{-5}\mu$J. In a supervised learning setup, the
energy-saving can potentially reach up to 1250x less (per feedforward
transmission) than the baseline model. The significantly reduced neural
activity in MCC leads to inherently fast learning and resilience against sudden
neural damage. This remarkable performance in pilot experiments demonstrates
the embodied neuromorphic intelligence of our proposed cooperative L5PC that
receives input from diverse neighbouring neurons as context to amplify the
transmission of most salient and relevant information for onward transmission,
from overwhelmingly large multimodal information utilised at the early stages
of on-chip training. Our proposed approach opens new cross-disciplinary avenues
for future on-chip DNN training implementations and posits a radical shift in
current neuromorphic computing paradigms.
