[eng] Over the last 15 years, Reservoir Computing (RC) has emerged as an appealing approach in Machine Learning, combining the high computational capabilities of Recurrent Neural Networks with a fast and easy training. By mapping the inputs into a
high-dimensional space of non-linear neurons, this class of algorithms have shown their
utility in a wide range of tasks from speech recognition to time series prediction. With
their popularity on the rise, new works have pointed to the possibility of RC as an existing learning paradigm within the actual brain. Likewise, successful implementation
of biologically based plasticity rules into RC artificial networks has boosted the performance of the original models. Within these nature-inspired approaches, most research
lines focus on improving the performance achieved by previous works on prediction or
classification tasks. In this thesis however, we will address the problem from a different
perspective: instead on focusing on the results of the improved models, we will analyze
the role of plasticity rules on the changes that lead to a better performance. To this
end, we implement synaptic and non-synaptic plasticity rules in a standard Echo State
Network , which is a paradigmatic example of an RC model. Testing on temporal series
prediction tasks, we show evidence that improved performance in all plastic models
may be linked to a decrease in spatio-temporal correlations in the reservoir, as well as
a significant increase on individual neurons ability to separate similar inputs in their
activity space. From the perspective of the reservoir dynamics, optimal performance is
suggested to occur at the edge of instability. This is a hypothesis previously suggested
in literature, but we hope to provide new insight on the matter through the study of
different stages on the plastic learning. Finally, we show that it is possible to combine
different forms of plasticity (namely synaptic and non-synaptic rules) to further improve
the performance on prediction tasks, obtaining better results than those achieved with
single-plasticity models.