Neurocomputational Theory of Mind: Brain Models and Connectionism

V. Neurocomputacional Theory of Mind
The advances that occur in the field of neuroscience, the feasibility study of brain processes and development of models of mind detached from traditional computer models will allow development of a Neurocomputacional Theory of Mind. The NWT is linked to the abandonment of a principle that is deemed essential in Classical Cognitive Science: mental states are computational states and computing involves the manipulation of symbols. The adoption of this principle led to the development of the "computer metaphor" and the idea that cognitive processes are computational processes in the sense that we talk about when we refer to computing what makes a conventional computer.
The crisis of classical computing systems and Classical Cognitive Science associated with them resulted in immediate need to develop new computing systemstions. These computer systems are connectionist systems. Connectionist systems are inspired by the designs and structures that make up the brain, now the image has been reversed, is the mastermind who provides a suitable model for understanding the operation of computer systems. The difference between a classical computer system and a connectionist system is that the latter does not require the manipulation of symbols. The fundamental difference between symbolic models and connectionist models is that while the former rely on the formal properties of a symbolic system to simulate cognitive systems, the latter are based on designs that attempt to copy the structure of the brain (design systems cerebriform). Models or designs of connectionist structures are varied. In reality for every design problem could arise from a different model, which is why this section is to explain what those elements common to most connectionist systems as connectionist models have influenced the computational picture of mind

A connectionist system is, therefore, a network of processors with a design type PDP, whose basic elements are so simple units similar to neurons that are known as network nodes. Each node is connected to other nodes forming a network through which signals are sent to each other and themselves. The activation value of each node depends on the exciting and inhibitory inputs from the network it is connected. The values of activation may be of type 0 or 1, where 0 indicates the unit is off and 1 indicates that the unit is activated. The value of activation is often called a 'force'. The total input received by a node determines its activation state, and this may depend on the input threshold has to reach in order to activate the node to which you connect. The activation of a node is represented by a relationship that is directly proportional to the strength of the input it receives. The strength of connection between two nodes is called weight. .