Abstract :
Biological organisms do not evolve toper fection, but to out compete others in their ecological niche, and therefore survive and reproduce. This paper views the const r aints i mposed on imper fec t organisms, p ar ticularly on t heir neur al systems and abilit yto capture a nd process infor mation accur ately. By understanding biological constraints of the physical properties of neurons,simpler a nd more e fficient ar t ificial n eu r al n etwor ks can be made (e.g ., spiking networ ks w i ll t r ansmit less infor mation than g r adedpotential n etwor ks, spikes only occur i n n ature due to limitations of car r y ing elect r ical ch arges over large distances). Fu r t her m ore,understanding the b ehav iour al and e colog i cal const r aints on animals allows an understanding of the l imitations of bio-inspiredsolutions, but also an understanding of why bio-inspired solutions m ay fail and how to cor rect t hese failures.
