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Table 1: A summary of constraints of biological neural networks and the possible associated constraints for designing artificial neural networks. For further examples and references of these constraints refer to the main text.

Process or structure Constraints in biological networks Possible constraints in artificial networks Chemical synapses Slow and prone to fatigue Synaptic modification possible without these constraints or accurate representation of the biological process (although fatigue can be useful in learning) Graded potential neurons Electric charge (ions) leaks, so information can only be transported a short distance Electronics or computer simulations do not present these constraints Spiking neurons Less information can be carried than with graded potential neurons No need to simulate spiking neurons, as no artificial constraints on graded potential neurons exist Size of neural networks Energetically costly, especially in small/cognitively simple animals Artificial networks will have higher energy costs. This may be a constraint depending on the application (i.e., importance of battery life) Ecological function Many well studied neural networks are related to survival behaviours—and are tightly tuned to these behaviours Artificial networks designed to implement different behaviours may not work, as they are not performing exactly the same role Multi-stimuli responses No single neural network is optimal for a behaviour, and several environmental stimuli and neural pathways combine to produce a robust behaviour Optimising a single neural network for a behaviour may be difficult. Unexpected (and possibly poor) results may occur in overly simple environments