  Biological cortical networks are potentially fully recurrent networks without
any distinct output layer, where recognition may instead rely on the
distribution of activity across its neurons. Because such biological networks
can have rich dynamics, they are well-designed to cope with dynamical
interactions of the types that occur in nature, while traditional machine
learning networks may struggle to make sense of such data. Here we connected a
simple model neuronal network (based on the 'linear summation neuron model'
featuring biologically realistic dynamics (LSM), consisting of 10 of excitatory
and 10 inhibitory neurons, randomly connected) to a robot finger with multiple
types of force sensors when interacting with materials of different levels of
compliance. Scope: to explore the performance of the network on classification
accuracy. Therefore, we compared the performance of the network output with
principal component analysis of statistical features of the sensory data as
well as its mechanical properties. Remarkably, even though the LSM was a very
small and untrained network, and merely designed to provide rich internal
network dynamics while the neuron model itself was highly simplified, we found
that the LSM outperformed these other statistical approaches in terms of
accuracy.
