4.1.8 Random Neuron

1. Random Neuron Functionality

Sometimes a little randomness is a good thing. When an ant is out searching for food and it is not picking up any scents on its antenna then it will start a random search to try and find food. Bacteria and other microorganisms do the same kind of thing. Random behavior comes into play quite often when an organism is searching for something or has exhausted its repertoire of normal options for dealing with a situation. The random neuron was created to handle the modeling of such behaviors. The random neuron uses three random variable equations to set its intrinsic current. These three equations control the amplitude of the intrinsic current that is generated, the duration between bursts, and the duration of bursts. A random number is generated between 0 and 100 and it is fed into these equations to find the final values that are used. This allows the user to configure the system using the mapping functions that were used earlier for the sensory and motor neurons. So it would be possible to setup a neuron that randomly fired an intrinsic current generated from a bell curve, with a burst duration generated by a linear function, and an inter-burst duration that was constant. One question that might pop to mind is "why make things this complex?" Why not simply have a random number generator set the value of the intrinsic current at each time step? One big reason is that building a random neuron that operated in that way could jump all over the place from time step to time step. On cycle 0 it could be at 10 na, cycle 1 it could jump to -5 na, and then on the next cycle jump up to 15 na. But it would not stay in any one place long enough for that setting to propagate through to the rest of the system. It would almost be like a white noise generator which would ultimately have no meaning. The second important reason is that this would limit the system to using only uniform random variables. But uniform random variables are not always the best system to solve a problem. Say, for instance, that the random neuron was going to be used to control the turning of an insect for random searching. Normally, the insect would only want to make small course changes. It would only want to make a 120^o turn very rarely. It would want the ability to make a large turn like that, but not often. This type of behavior can be simulated very nicely with an inverted bell curve. The large bulk of the random numbers in the middle of the curve would then generate a modest intrinsic current, but numbers on the edges of the curve would generate large intrinsic currents. Also, using random variable equations for the burst and the inter-burst durations insures that a value of the intrinsic current is on for long enough that it can have an affect on the rest of the system. Using random variable equations gives the user flexibility in how they want to set those parameters.

2. Random Neuron Properties

Random Neuron Dialog

Figure 1. This is a dialog for setting the properties for a random neuron.

3. Random Mapping Functions

The random mapping functions are done exactly like the sensory mapping function. For a more detailed description of the mapping functionality please see section 4.1.6.3 of the sensory neuron. The big difference is that there are three of these mapping functions for the random neuron and that they are used as random variable equations. The first equation is used to calculate the intrinsic current during a burst. The second equation is used to calculate the burst duration. And the third equation is used to calculate the duration between a burst. The input to each of the functions is a random number between 0 and 100. The equation determines the output value.

4. Random Neuron Output

Random Neuron Output.

Figure 2. This graph shows the random nature of the output from this type of neuron.

Figure 2 shows some output from a random neuron. The burst and inter-burst durations were decreased a lot more than would normally be the case in order to get several of them into the graph in a reasonable amount of time. Normally you would want these times to be much longer than is shown so that they could have an appreciable effect on the system. One thing that can be seen from the graph is that the value for the intrinsic current is at a different level for each of the bursts. Also, while the durations seem similar, it is possible to see that there are some noticeably longer bursts interspersed in the graph.

5. Random Neuron Overview

Random neurons inject an element of chance into the system. The user can control these systems by setting the properties which control the size of the intrinsic currents and the durations of the burst and inter-burst times. Random neurons will not be used very often. But they do provide a very important function. Within the insect simulator system they are really only used to help in building a random wanderer controller. This controller is used to implement a random search behavior in order to find food.