 The Multiplasticity Network, MPN, is a new type of neural network which uses only synaptic modulation to perform computation. This network was found to be capable of performing complex tasks similar to those performed by recurrent neural networks, INNs, but without the need for any recurrent connections. By studying the neural population dynamics of the MPN, we were able to identify key features of the network's operation and compare them to known INN dynamics. Our results showed that the MPN had a fundamentally different attractor structure than INNs, allowing it to outperform INNs on several neuroscience-related tasks. Additionally, we found that the MPN could be trained across a variety of neuroscience tasks, suggesting that it may be useful for modeling biological systems. This article was authored by Carl Akin and Stefan Mahalas.