  Reservoir computing is a machine learning paradigm that transforms the
transient dynamics of high-dimensional nonlinear systems for processing
time-series data. Although reservoir computing was initially proposed to model
information processing in the mammalian cortex, it remains unclear how the
non-random network architecture, such as the modular architecture, in the
cortex integrates with the biophysics of living neurons to characterize the
function of biological neuronal networks (BNNs). Here, we used optogenetics and
fluorescent calcium imaging to record the multicellular responses of cultured
BNNs and employed the reservoir computing framework to decode their
computational capabilities. Micropatterned substrates were used to embed the
modular architecture in the BNNs. We first show that modular BNNs can be used
to classify static input patterns with a linear decoder and that the modularity
of the BNNs positively correlates with the classification accuracy. We then
used a timer task to verify that BNNs possess a short-term memory of ~1 s and
finally show that this property can be exploited for spoken digit
classification. Interestingly, BNN-based reservoirs allow transfer learning,
wherein a network trained on one dataset can be used to classify separate
datasets of the same category. Such classification was not possible when the
input patterns were directly decoded by a linear decoder, suggesting that BNNs
act as a generalization filter to improve reservoir computing performance. Our
findings pave the way toward a mechanistic understanding of information
processing within BNNs and, simultaneously, build future expectations toward
the realization of physical reservoir computing systems based on BNNs.
