THE BACKPROPAGATION ALGORITHM - IMPLICATIONS FOR THE BIOLOGICAL BASESOF INDIVIDUAL-DIFFERENCES IN INTELLIGENCE

Variations in brain structure and function may explain individual vari ation in human intelligence. However, it is not currently possible to directly examine this hypothesis. As an indirect examination, a neural network employing the backpropagation algorithm to solve the exclusiv e-or function was manipulated to possess different numbers of processi ng elements (neurons), connections (synapses), and conduction failure (synaptic failure). The effect of the variations on network accuracy a nd energy utilization were compared to human reaction time and cerebra l metabolic data to evaluate which variations most reliably reproduced the human results. Varying the synaptic failure rare appears essentia l for mimicking the human reaction time data and increasing network co nnectivity is the most efficient way to improve network accuracy for a given degree of neuronal activation. The results suggest that variati ons in the physiologic events of synaptic neurotransmission and variat ions in the structural interconnectivity of the neurons in the brain w ill be found to underlie an important portion of the variation in huma n intelligence.