  One of the challenges of modern neuroscience is creating a "living computer"
based on neural networks grown in vitro. Such an artificial device is supposed
to perform neurocomputational tasks and interact with the environment when
embodied in a robot. Recent studies have identified the most critical
challenge, the search for a neural network architecture to implement
associative learning. This work proposes a model of modular architecture with
spiking neural networks connected by unidirectional couplings. We show that the
model enables training a neuro-robot according to Pavlovian conditioning. The
robot's performance in obstacle avoidance depends on the ratio of the weights
in inter-network couplings. We show that besides STDP, critical factors for
successful learning are synaptic and neuronal competitions. We use the recently
discovered shortest path rule to implement the synaptic competition. This
method is ready for experimental testing. Strong inhibitory couplings implement
the neuronal competition in the subnetwork responsible for the unconditional
response. Empirical testing of this approach requires a technique for growing
neural networks with a given ratio of excitatory and inhibitory neurons not
available yet. An alternative is building a hybrid system with in vitro neural
networks coupled through hardware memristive connections.
