Abstract
AbstractLiquid computers are devices that utilise the properties of liquid volumes or reactants to represent data and outputs. A recent development in this field is the emergence of colloid computers, which employ electromagnetic interactions among functional particles for computation. To assess the potential of colloid computers in implementing neuromorphic dynamical architectures, we have focused on realising Pavlovian reflexes within colloid mixtures. The Pavlovian reflex, a fundamental function of neurological systems in living organisms, enables learning capabilities. Our approach involves implementing Pavlovian learning by associating an increase in synaptic weight with a decrease in the resistance of the colloid mixture. Through experimental laboratory conditions, we have successfully demonstrated the feasibility of Pavlovian learning in colloid systems.
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