A NETWORK OF ARTIFICIAL NEURONS LEARNS TO USE
HUMAN LANGUAGE !!!!
A computer
simulation of a cognitive model entirely made up of artificial neurons learns
to communicate through dialogue starting from a state of tabula rasa.
The ANNABELL model is a cognitive architecture entirely made up
of interconnected artificial neurons, able to learn to communicate using human
language starting from a state of 'tabula rasa' only through communication with
a human interlocutor.
A group of researchers
from the University of Sassari (Italy) and the University of Plymouth (UK) has
developed a cognitive model, made up of two million interconnected artificial
neurons, able to learn to communicate using human language starting from a
state of "tabula rasa," only through communication with a human
interlocutor. The model is called ANNABELL (Artificial Neural Network with
Adaptive Behavior Exploited for Language Learning) and it is described in an
article published in the international scientific journal PLOS ONE. This research sheds
light on the neural processes that underlie the development of language.
How does our brain develop the ability
to perform complex cognitive functions, such as those needed for language and
reasoning? This is a question that certainly we are all asking ourselves, to
which the researchers are not yet able to give a complete answer. We know that
in the human brain there are about one hundred billion neurons that communicate
by means of electrical signals. We learned a lot about the mechanisms of
production and transmission of electrical signals among neurons. There are also
experimental techniques, such as functional magnetic resonance imaging, which
allow us to understand which parts of the brain are most active when we are
involved in different cognitive activities. But a detailed knowledge of how a
single neuron works and what are the functions of the various parts of the
brain is not enough to give an answer to the initial question.
We might think that the brain works in a
similar way to a computer: after all, even computers work through electrical
signals. In fact, many researchers have proposed models based on the analogy
brain-is-like-a-computer since the late '60s. However, apart from the
structural differences, there are profound differences between the brain and a
computer, especially in learning and information processing mechanisms.
Computers work through programs developed by human programmers. In these
programs there are coded rules that the computer must follow in handling the
information to perform a given task. However there is no evidence of the
existence of such programs in our brain. In fact, today many researchers
believed that our brain is able to develop higher cognitive skills simply by
interacting with the environment, starting from very little innate knowledge.
The ANNABELL model appears to confirm this perspective.
ANNABELL does not have pre-coded
language knowledge; it learns only through communication with a human
interlocutor, thanks to two fundamental mechanisms, which are also present in
the biological brain: synaptic plasticity and neural gating. Synaptic
plasticity is the ability of the connection between two neurons to increase its
efficiency when the two neurons are often active simultaneously, or nearly
simultaneously. This mechanism is essential for learning and for long-term
memory. Neural gating mechanisms are based on the properties of certain neurons
(called bistable neurons) to behave as switches that can be turned
"on" or "off" by a control signal coming from other
neurons. When turned on, the bistable neurons transmit the signal from a part
of the brain to another, otherwise they block it. The model is able to learn,
due to synaptic plasticity, to control the signals that open and close the
neural gates, so as to control the flow of information among different areas.
The cognitive model has been validated
using a database of about 1500 input sentences, based on literature on early
language development, and has responded by producing a total of about 500
sentences in output, containing nouns, verbs, adjectives, pronouns, and other
word classes, demonstrating the ability to express a wide range of capabilities
in human language processing.
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