Synaptic plasticity is thought to underlie learning and memory [1]. Plasticity rules can be active in different synaptic connection types, such as excitatory-excitatory (EE) and inhibitory-excitatory (IE) connections, with in silico and experimental evidence suggesting distinct mechanisms for each type [2,3,4]. While the interaction between connection types in learning and memory is an active research focus [5], studies typically probe only one type of plasticity at a time. Thus, at scale, the contributions of connection types and their co-active plasticity rules to memory processes remain unclear. Here, we took advantage of a recent dataset of co-active Hebbian STDP and rate-dependent plasticity rules that have been meta-learned to maintain homeostasis in large recurrent spiking networks [6]. The baseline-configured spiking network comprises plastic EE, EI, IE, and II connections, each active with a different plasticity rule that together produce stable activity. To investigate how sets of rules can govern network computations, we simulated each set of co-active rules in a familiarity detection task. First, we present a specific stimulus multiple times. Following a delay period, we evaluate memory performance by presenting this familiar, as well as novel stimuli. Memory retention is quantified as the difference in population firing rate between stimuli. After assessing memory performance in the baseline network, we knock out plasticity in either one, or three rules for the duration of the task, allowing us to evaluate each rule's individual contribution and its role in relation to co-active rules. We show that there are synapse type-specific contributions to stability and memory retention. Blocking plasticity in IE connections disrupts network stability in over one-third of knockout simulations, such that networks do not display cortical-like asynchronous-irregular firing dynamics. Blocking EE plasticity, on the other hand, usually allows for memory retention during replay, but results in highly variable population firing rates in response to novel and familiar stimuli. Our results suggest that EE plasticity plays a crucial role in stabilizing both pattern recognition and separation, linking specific synapse types and rules to network computation. More generally, it underscores the complexity of the landscape of co-active plasticity rules, and its many open avenues of investigation.