Volume 8(3) 124-126 (2015) - 124 J Comput Sci Syst Biol ISSN: 0974-7230 JCSB, an open access journal Research Article Open Access Licata, J Comput Sci Syst Biol 2015, 8:3 DOI: 10.4172/jcsb.1000179 Short Communication Open Access Keywords: Artifcial neural networks; Feed-forward networks; Recurrent networks; Brain; Mind Introduction Nowadays we are out of the illusion that computers can be good models for human mind. Te human mind is the result of the bio- physical structure of a nervous system in a body which evolved to survive in the environment, in communication with other individuals of same species and in relationship with other species of the ecosystem: its power is due to a very long and hard evolution and we are not able to understand its complexity [1]. (A)Te goal that A.I. should attain is the emulation, through a computer, of some processes of mind in relationship with the environment (the world and other individuals). With respect to this objective I want to underline two obstacles in neural networks strategy: 1) and 2). 1) Neural networks are a strategy to emulate directly the behavior of brain and not the behavior of mind. Tus an important problem that neural network strategy misses is the gap between brain and mind. Tis is the problem of the translation of states of neuronal activation in concrete mental activity. Te mind/brain translation problem will not be overcome until we will not have a clear theory about thought, consciousness, perception and action as cerebral phenomena. Moreover, if this theory wants to be useful to neural network strategy it must be conceived following the neural network philosophy and language. A theory who speaks the language of neural networks should consider thought (i.e. mental representations, planning, consciousness, memory and so on), perception and action not as “states” but as fuxes of states which go through the network (ordered and structured sets of states which go through the network). About these fuxes that we, as thinking brains, perceive in ourselves, we have unclear ideas on their beginning, on their developing and on their ending, but we know that perception can generate them. 2) Artifcial neural networks are very poor imitations of brain. Human brain is a “network” of 100 milliard of neurons in which each neuron is connected to many thousands of other neurons, so, in a brain; there are millions of milliards of connections. Tere are many kinds of structure of neural networks, but the architecture of the most common neural networks consists in a simple three layers structure of artifcial neurons, like the three layers “perceptron” of Figure 1, that henceforth I will call TLP. Discussion Neural networks can be feed-forward or feedback networks. In feed-forward neural networks like TLP the information propagates in only one direction, from input layer to output layer through the hidden layer (that can be more than one), and there are no cycles. Each unit is connected with every unit of the following layer, there are no connections between units of the same layer or with a unit of previous layer, and there are no connections which jump one (or more) layer(s). A feed-forward network simply calculates a function of input values which depend on the distribution of weights (w) of the incoming connections and on the activation function of the outgoing connection. It has not any internal state diferent with respect to the weights of connections. In feedback networks (also called ‘recurrent networks’) the connections are arbitrary. Te Hopfeld network (Figure 2) is a fully connected graph, typically represented as a matrix of weights; it has bi-directional connections and symmetrical weights [2]. Tere is no input or output specifc layers, all neurons are input and output units; activation levels are only +1 or -1. Tese kinds of network, with very high redundancy of connections, produce associative memory and permit the recovery of missing information. Sometimes human brain behaves as a feed-forward network with layers, but it has also many connections that lead information backward to neurons of “preceding layer”, i.e. the brain is a feedback network in which can be many cycles of neurons. Given that sometimes the activation goes back to neurons which have caused it, feedback networks (and the brain) have an internal status memorized as activation levels of units. In recurrent networks the computation has much less order with respect to feed-forward networks.Artifcial feedback networks can become unstable, chaotic or can fuctuate and it can be very hard to obtain a stable output from a given input; so it is a mystery how our brain, as feedback network, is able to produce its(so good) computation. Te learning process, in a neural network, is commonly understood *Corresponding author: Gaetano Licata, Chair of Logic and Philosophy of Science, Dipartimento di Scienze Umanistiche, University of Palermo, Viale delle Scienze Ed. 12, 90128, Palermo, Italy, Tel: 339-456-8136; E - m a i l : ninnilicata@yahoo.it Received February 23, 2015; Accepted March 11, 2015; Published March 13, 2015 Citation: Licata G (2015) Are Neural Networks Imitations of Mind? J Comput Sci Syst Biol 8: 124-126. doi:10.4172/jcsb.1000179 Copyright: © 2015 Licata G. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract Artifcial neural networks are often understood as a good way to imitate mind through the web structure of neurons in brain, but the very high complexity of human brain prevents to consider neural networks as good models for human mind;anyway neural networks are good devices for computation in parallel. The difference between feed-forward and feedback neural networks is introduced; the Hopfeld network and the multi-layers Perceptron are discussed. In a very weak isomorphism (not similitude) between brain and neural networks, an artifcial form of short term memory and of acknowledgement, in Elman neural networks, is proposed. Are Neural Networks Imitations of Mind? Gaetano Licata* Gaetano Licata, Chair of Logic and Philosophy of Science, Dipartimento di Scienze Umanistiche, University of Palermo, Italy Journal of Computer Science & Systems Biology J o u r n a l o f C o m p u t e r S c i e n c e & S y s t e m s B i o l o g y ISSN: 0974-7230