How can the brain be such a good computer? Part of the answer lies in the astonishing number of neurons and synapses that process electrical impulses in parallel. Part of it must be found in the ability of the nervous system to evolve in response to external stimuli and grow, sharpen, and depress synaptic connections. However, we are far from understanding even the basic mechanisms that allow us to think, be aware, recognize patterns, and imagine. The brain can do all this while consuming only around 20 Watts, out-competing any human-made processor in terms of energy-efficiency. This question is of particular interest in a historical era and technological stage where phrases like machine learning and artificial intelligence are more and more widespread, thanks to recent advances produced in the field of computer science. However, brain-inspired computation is today still relying on algorithms that run on traditional silicon-made, digital processors. Instead, the making of brain-like hardware, where the substrate itself can be used for computation and it can dynamically update its electrical pathways, is still challenging. In this work, I tried to employ organic semiconductors that work in electrolytic solutions, called organic mixed ionic-electronic conductors (OMIECs) to build hardware capable of computation. Moreover, by exploiting an electropolymerization technique, I could form conducting connections in response to electrical spikes, in analogy to how synapses evolve when the neuron fires. After demonstrating artificial synapses as a potential building block for neuromorphic chips, I shifted my attention to the implementation of such synapses in fully operational networks. In doing so, I borrowed the mathematical framework of a machine learning approach known as reservoir computing, which allows computation with random (neural) networks. I capitalized my work on demonstrating the possibility of using such networks in-vivo for the recognition and classification of dangerous and healthy heartbeats. This is the first demonstration of machine learning carried out in a biological environment with a biocompatible substrate. The implications of this technology are straightforward: a constant monitoring of biological signals and fluids accompanied by an active recognition of the presence of malign patterns may lead to a timely, targeted and early diagnosis of potentially mortal conditions. Finally, in the attempt to simulate the random neural networks, I faced difficulties in the modeling of the devices with the state-of-the-art approach. Therefore, I tried to explore a new way to describe OMIECs and OMIECs-based devices, starting from thermodynamic axioms. The results of this model shine a light on the mechanism behind the operation of the organic electrochemical transistors, revealing the importance of the entropy of mixing and suggesting new pathways for device optimization for targeted applications.