DANGER PERCEPTION DANGER PERCEPTION Consider the visual system of the frog. The frog possesses sets of nerve cells just behind the retina whose function is to discriminate only the four following events: 1) a moving object which activates the frog's field of vision, 2) a moving object which triggers the field of vision and stops, 3) the general level of lighting in the field of vision which decreases suddenly, 4) a small, dark object round in form which enters the field of vision and moves around in an erratic manner.
The first three events put the frog into a state of alert. The first case can be interpreted as the priming arrival of an intruder. The second case involves the intruder stopping and the danger becoming real. The third case can be interpreted as the arrival of a predator which is overshadowing the frog. All three cases give rise to the "escape" response. The last case suggests an insect is close and it causes an attack by the frog regardless of whether or not there is really prey there. The responses of the frog, flight or attack, are triggered entirely visually. So, the visual neurons of the frog are "wired-up" in order that, when they receive a picture of the frog's environment from its eyes, that information is processed into one of the four predetermined preparation - like possibilities. This information is then sent to the appropiate modules of the brain, in order to produce a response. This feature of being able to extract certain simple features from perhaps a very complex image is commonly referred to as pattern recognition. It is a crucial feature of the brain which allows it to make sense of a very complex and ever changing world. (Herbert Simon).
The leech circles repeatedly through two main behaviors regulated by serotonine: one phase of hunger behavior (low serotonine) and the second satisfaction and quiet behavior during digestion (high serotonine).
The mouse in front of a pedal connected to its own pleasure center through an electrode, prefers to starve (food is present) instead of quitting pleasure.
Now combine these three possibilities (the frog's, the leech' and the mouse's) with a general infantile playing or playful attitude that makes various experiments, like a dolphin. Modelize this situation in terms of a neural net. To try to simplify things, we can think of a simple model in which the network is made up of two screens - the nodes on the first (input) layer of the network are represented as light bulbs which are arranged in a regular pattern on the first screen. Similarly, the nodes of the third (output) layer can be represented as a regular array of light bulbs on the second screen. There is no screen for the hidden layer - that is why it is termed "hidden"! Instead we can think of a black box which connects the first screen to the second. Of course, the magic of how the black box functions depends on the network connections between hidden nodes which are inside. When a node is firing, we show this by lighting its bulb. Imagine the picture. One screen is the input, the second screen is the output. In between is the A-consciousness which may process inputs into outputs. In this model, the nodes representing artificial neurons are arranged into layers. The signal representing an input pattern is fed into the first layer. The nodes in this layer are connected to another layer (sometimes called the "hidden layer". The firing of nodes on the input layer is conveyed via these connections to this hidden layer. Finally, the activity on the nodes in this layer feeds onto the final output layer, where the pattern of firing of the output nodes defines the response of the network to the given input pattern.
Now the network starts functioning in the following way: a given pattern of lit bulbs is set up on the first screen. This then feeds into the black box (the hidden layer, the A-consciousness) and results in a new pattern of lit bulbs on the second screen. This might seem a rather pointless exercise in flashing lights except for the following crucial observation. It is possible to play with the contents of the black box (adjust the strengths of all these internode connections) so that the system can produce any desired pattern on the second screen for a very wide range of input patterns. For example, if the input pattern is a triangle, the output pattern can be trained to be a triangle. If an input pattern now contains both a triangle and a circle, the output can be still arranged to be a triangle. Similarly, we may add a variety of other shapes to the network input pattern and teach the net to rcognize triangles. If there is no triangle in the input, the network can be made to recognize the lack, for example, with a zero. If there is a triangle, the response is a 1.
Now like the frog, the inputs are dangerous conditions with different degrees of danger. The output may be zero (escape) or one (attack). In principle, by using a large network with many nodes in the hidden layer, it is possible to manage so that it spots triangles in the input pattern, independently of noise.
Another way of looking at this is that: the network can classify all pictures into one of two sets - those containing triangles (or danger signals) and those which do not. The neural net is capable both of recognizing and of classifying patterns. This is also accomplished with an A-consciousness - like Soar machine (Alan Newell)
Furthermore, we are not restricted to spotting triangles, we could simultaneously arrange for the network to spot squares, diamonds or whatever we wanted. We could be more ambitious and ask a response with a circle whenever we present it with a picture which contains triangles and squares but not diamonds.
Of course such a network may be used to start with associations between objects. For example, whenever the network is presented with a picture of a danger, the output may be an escape. Hopefully, you are beginning to see the power of this machine at doing rather complex pattern recognition, classification and association tasks. It is no coincidence, of course, that these are the types of task that the brain - adding emotions - is exceptionally good at.
Now " the identification of objects cannot occur without the use of personal knowledge", including, possibly, some innate knowledge, which may escape computational modeling.
Anticipation of gathering more sensory information.
The stimulus triggers a motor response in which the living being directs its sense organs (attention) to the location of the stimulus. This motor response has the effect of collecting more afferent information about the circumstances surrounding a stimulus detected by the somatosensory system
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2.feb.1999
Pulsar tecla de vuelta
Glosario de Carlos von der Becke, where some specialized words are explained.