From a Swarm to a Biological Computer

According to behaviourism, any swarm behaviour can be managed by outer stimuli: attractants (motivational reinforcement) and repellents (motivational punishment). In the meanwhile, there are the following two main stages in reactions to stimuli: (i) sensing (perceiving signals) and (ii) motoring (appropriate direct reactions to signals). Hence, by placing attractants and repellents at different sites we can manage and program the swarm behaviour. This opportunity allows us to design a biological computer - an abstract machine (i) with inputs presented by stimuli coming from attractants and repellents and (ii) with outputs presented by the swarm reactions to appropriate stimuli. This computer can be realized on different swarms differently. The point is that different matters are attractants and repellents for different animals. They differ a lot even for microorganisms. Nevertheless, their logic and mathematics are the same. Behaviourism means that (i) the complex of swarm behavioural patterns can be reduced to a composition of some elementary swarm patterns, (ii) if we know an appropriate attractant or repellent for each elementary pattern, then from a complex of attractants and repellents we can deduce a complex of patterns. Nevertheless, it can be shown that both assumptions are false. The point is that swarms are populations which behave as a distributed network, capable of responding to a wide range of spatially represented stimuli so that in their behaviours we can observe effects of neural networks with lateral activation and lateral inhibition mechanisms. As a result, behavioral patterns cannot be additive. In the paper it is discussed what we can do with this feature of swarm behaviour to program swarms.