Small Boolean Liquid State Machines (B-LSMs) offer a simplified yet expressive biologically inspired model of recurrent computation, in which network attractor dynamics can be systematically analyzed. In their untrained form, B-LSMs exhibit complex, often chaotic dynamics with short-lived memory traces. This study investigates how local synaptic plasticity (STDP) and a global plasticity (GP) mechanism jointly shape the attractor landscapes of these networks. Specifically, we show that synaptic modifications can drive B-LSMs to exhibit exponentially many attractors, each corresponding to a potential memory. Such high attractor regimes are attainable through global synaptic crafting. Under noisy background conditions, STDP tends to drive the networks back to low attractor regimes; however, when receiving carefully designed inputs, STDP maintains the networks’ rich attractor dynamics. Overall, our findings highlight the theoretical potential for storing an impressive number of memories in recurrent neural networks, with significant implications for theoretical neuroscience and neuromorphic computing.