# Information, Thermodynamics and Life: A Narrative Review
## 3 Applications \"after\" Information: To the Process of Learning
### Neuronal Systems as Forms of Artificial Life  
Once the brain interacts with the environment, it adapts in a constant manner and the form in which the environment is represented is entitled an \"internal model\". The dynamical properties and the connectivity of neurons are the expression for internal models' neurobiological basis. Consequently, the intercommunications between external devices and neural tissues present a fundamental means for the connectivity investigation and the dynamical properties of neural populations. [PERSON] developed and suggested this idea in the 1980s in order to investigate and represent the neuronal populations' dynamical behavior of in the lamprey's brainstem [65]. The maintenance of the brainstem took place in vitro. Two types of artificial device were used for its connection in a closed loop: (a) a small mobile robot and (b) a simulated dynamical system. In both cases, the recorded extracellular signals control the device and its output was interpreted in a form of electrical stimuli, which transferred to the neural system. The objective of the initial study was to evaluate the dynamical dissociation in the preparation of neurons in a configuration in the form single-input/single-output. The dynamical dissociation refers to the number of variables of the state that determine the system's output along with the applied input. Recent report's results pointed out that whilst the particular neural system has considerable dynamical properties, its efficient intricacy, as set up by the dynamical dissociation, is rather temperate. In another study, a more specific situation has been considered, where a robotic device is controlled by the same section of the nervous system in a configuration of two-input/two-output. The input-output information from the neuro-robotic preparation has been adapted to neural network models with different interior dynamics, providing thus the ability to observe for each model its generalization error. In the brain-machine interface context, a computational and experimental framework such as this equips the means in order to investigate neural plasticity as well as internal representation. In neuroscience, information transfer has been the topic of intensive investigation. In its simplest form, neuronal processing consists of an input signal, a processing unit (which is actually the neuron itself) and an output, which is the occurring behavior (as described by [PERSON] and [PERSON] (1953)). Information flow takes place in sequences of symbols, which could correspond to trajectories of stochastic processes [66]. Early works on neuronal information transmission, have found that information is transmitted by \"signal trains\" of discrete action potentials [67]. The first models investigated were simple neuronal connections, where one input pulse produced one output signal. An interesting finding of early works was that the brain was characterized by highly irregular, inter-spike activities, indicating that the spike-signal was noisy [67]. Hence, similar early works attempted to reduce this irregularity by accounting for the average of multiple signals. Although, this reduced the noise it is now known that even slight irregularities in brain signals still carry out messages [68]. The same was hypothesized for molecular signals in the cellular environment, which has been found that the most important differences were those that took place in infinitesimal ranges. Finally, it is possible that irregularities in neural signaling may actually represent the actual information transmission [67,68]. Overall, neuroscience stated two hypotheses, about the nature of neural information processing. The first theory describes the neural code as a \"temporal code\", which takes into account the neural \"spike trains\" (\"Spike trains are a representation of neural activity. In neurophysiological studies, spike trains are obtained by detecting intra- or extracellularly the action potentials, but preserving only the time instant at which they occur.