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Connectionism. Introduction

for understanding the human mind or even to its various aspects have been used in different time metaphors, and in particular a new metaphor, that of the computer, has become increasingly in recent years in the study of psychological processes, it has been affirmed at the same time the spread of cognitivism as a new research paradigm in psychology and with the vision of man as a processor of information that has often brought with them. The computer, information processing, too, thanks to his now classical distinction between hardware and software offers a number of ideas that seem to help us understand something more human split between mind and body, or between mind and brain. However, despite the increasing number of Descartes's mind-body distinction, and despite the century-old tradition that this idea can boast, it is possible to find critical voices outside the chorus, we warn against too simplistic application of this dualism (think of the psychology or psychosomatic medicine, or to stay in an area closer to neuroscience, such as the ideas developed by Antonio Damasio, 1994), and in fact you can find alternatives to the metaphors of the computer that allow us to go beyond the idea of \u200b\u200ba cognitive system to be studied as something totally different brain networks from which it is substantiated. For example, the first computer perform various logic functions for which they were developed using the same physical structure that constituted them (von Neumann, 1966), reading the same von Neumann discovered how it was largely for opportunistic reasons, and that is due dell'inaffidabilità materials of which the first computers were made, which was reached as to the expedient of distinction between hardware, understood as the physical structure of a computer, software, understood broadly as the various features that a given hardware is capable of manage.

There is a paradigm that allows us to study in particular to safeguard the important bond that you can see from the structure of our neural networks and brain functioning of our minds: this is the connectionist paradigm, which has the His "manifesto" in the book of Rumelhart & McClelland (1986) "Parallel distributed processing: Explorations in the microstructure of cognition". Through this paradigm is possible in some way taking advantage of its formal rigor and accuracy of mathematical models, to be both attentive to the psychological side of the phenomena under study (perception, memory, language, reasoning, etc..), And finally recepire le indicazioni provenienti da vari studi più propriamente "biologici", di tipo neuropsicologico e di neuroimmagine.

Secondo questo paradigma l'elaborazione delle informazioni avviene nel nostro sistema cognitivo in modo parallelo, distribuito ed interattivo. I modelli connessionisti (o “reti neurali”) sono composti da unità (“neuroni virtuali”) aventi ognuna un certo valore d'attivazione (consistente in un semplice numero reale), e collegate tra loro da connessioni pesate (e cioè in qualche modo regolabili) di tipo eccitatorio o inibitorio (cioè esse rispettivamente possono aumentare o diminuire l'attivazione del neurone a cui si connettono). Quando un certo input, stimulus is administered to a subset of the units that make up the network, enabling them to determine that this input is propagated through the various connections throughout the network, coming to cause a degree of activation in the final subset of output units (output) of the network, this activation would depend on existing connections as well as also by their greater or lesser strength. To ensure that a number of input matches a certain number of precise output you can rely on a set of algorithms designed specifically to train a neural network according to different criteria, or you can also set the connections manually, depending the phenomenon that you want to simulate and the purposes for which the network is built.

The potential of this paradigm consists mainly in its ability to overcome the perspective of too sharp a division between mind and brain, and the insights it offers in favor of a vision and interactions of complex phenomena under investigation. For example, the connectionist approach sheds new light on the historical opposition between nativism and empiricism, since its models are made of native bone onto which the various processes by which a real learning takes place on an empirical (the "training" in preparation applied to this network). Again, this approach leads to new contributions as the historical conflict between elements and molar approach, overall, as the connectionist models replace the localized production of symbols distributed operations, operations which give rise to the emergence of global properties.

in the literature are well known advantages and disadvantages of the different connections than theoretical approach to symbolic simulation and classical theory of cognitive processes. Regarding the advantages, neural networks are robust and flexible: the destruction of some units of a neural network or the presence of a ambiguous input causes a partial decline in network performance, and conversely a model built according to a purely simbolico che venga danneggiato o testato con input ambigui di solito fallisce completamente il suo obiettivo. Ancora, le reti neurali hanno delle capacità spontanee di generalizzazione assenti nei modelli classici, per cui ad esempio in compiti di categorizzazione riescono a gestire meglio le eccezioni e gli stimoli nuovi. Tra gli svantaggi bisogna certamente considerare invece che i modelli attuali non considerano molte delle differenze tra i diversi tipi di neuroni cerebrali, e non sono in grado ad esempio di simulare una importante proprietà dell'apprendimento umano: mentre le reti necessitano di lunghi cicli di apprendimento per arrivare a eseguire correttamente i loro compiti, all'uomo e a molti animali basta spesso fare esperienza una sola volta di un dato pericolo to learn such a response of avoidance.

biliografici Additional Resources:
Damasio, AR (1994). Descartes' Error: Emotion, Reason, and the Human Brain. New York: Putnam Publishing
Garson, J. (Published 2007, accessed September 2008). Available on http://plato.stanford.edu/entries/connectionism/
von Neumann, J. (1966). Theory of self-reproducing automata. Ed by Arthur W. Burks. USA: University of Illinois.

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Learning Levels

One of the most important topics of neuroscience and learning, is a crucial challenge to understand what algorithms that enable learning. And, of course, if these algorithms are real in the sense of the term Thuringia.

Oovviamente we are not referring only to learn at school, or, more generally, in the human realm. We refer instead to a much broader concept. Now we examine some of the levels at which learning can take place.

Small premise: these are just my personal opinions on the subject, and I will not say anything definitive, or even genuinely scientific, matter.

Learning explicit heteronomous: This indicates the learning they are able to complete living if trained. This covered a large part of education human, but also the training of dogs and horses by the man; education for hunting animals. The transmission, in short, non-innate behavior among different individuals. The culture in the broadest sense, if you like.

Learning implicit self: every living being, when exposed to environmental pressure, changing its behavior to adapt to the environment. This applies to animals with higher intellectual capacity on average, say, mammals and birds, for animals with lower powers, such as fish or insects. Let me give two examples that struck me very much.

zoo in Stuttgart, there are many areas for animals separated by moats, rather than cages. In these ditches live carp. If you get close to observe the animals casting a shadow on fossatto observe that the carp come to the surface, opening her mouth. Evidently they have learned that often the visitors throw them food.

At a still more primitive: the C. Elegans stores during the early stages of development to which the temperature was raised, and always tends to move towards similar temperatures.

The example of C. Elegans is very extreme! In fact, our nematode has a few hundred neurons, so let's see how the behavior of movement must be somehow encoded within this small neural network, using the its connections with the sensory neurons. This brings us to the learning of lower level.

Parametric Learning: This is what I do neural networks, a neural network output is rewarded or not rewarded with a (virtual) and, according to this reward, are reinforced or modified connection, so that the network tends to maximize the reward you get. This is the principle of reinforcement learning . Obviously, for this type of learning is necessary that it provides a higher reward.

What are the great challenges of neuroscience? Let's see some:

- understand the exact mechanisms of learning Parametric;
- What are rewarding instances of the various circuits;
- understand which and how many levels of learning involved and how they are intertwined;
- you can understand how the 'emergency an explicit learning higher level.

understand the difficulties?