Topic: temporal vector addition

The ability to combine simple learned elements (primitives) and create novel complex behaviors is a fundamental aspect of cognition in biological brains. However, composing computational primitives in artificial neural networks remains a significant challenge, suggesting that biological systems may possess structural priors which enhance this capability. In this study, we examine the mechanisms by which network structure facilitates flexible compositions of computational primitives. To investigate this question, we analyze recurrent neural networks (RNNs) that perform a temporal vector additio