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We study the problem of verifying multi-agent systems composed of arbitrarily many neural-symbolic agents. We introduce a novel parameterised model, where the parameter denotes the number of agents in the system, each homogeneously constructed from an agent template equipped with a neural network-based perception unit and a traditionally programmed action selection mechanism. We define the verification and emergence identification problems for these models against a bounded fragment of CTL. We put forward an abstraction methodology that enables us to recast both problems to the problem of checking Neural Interpreted Systems with a bounded number of agents. We present an implementation and discuss experimental results obtained on a social dilemma game based on guarding.