|Many neural systems encode information by generating specific sequences of action potentials forming temporal activity patterns (codes). These systems emit functionally equivalent activity patterns with a certain variability in their temporal structure. In this work, we have implemented in real-time a closed-loop stimulation protocol for the study of temporal coding in neural systems. The stimulation was triggered by matching a target code and a code obtained from the system's activity using the Victor-Purpura distance. This protocol has been developed for the Real-Time eXperiment Interface tool. Latencies during closed-loop experiments were within strict real-time temporal constraints, thus validating the use of the protocol to study information processing in activity-dependent stimulation experiments. In addition, its functionality has been validated through a proof of concept in which we have conditioned the activity of the Hindmarsh-Rose neural model to evoke a different dynamic activity state. With this protocol, equivalence between different matching patterns can be inferred when closed-loop stimulation driven by them leads to equivalent responses. Results show that the dynamic state evoked by closed-loop stimulation is difficult to generate under open-loop stimulation (i.e, without precise activity-dependent stimulation of the system).
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