<p>Spiking neural P systems (SN P systems) are computing models based on the third generation of neuron models known as spiking neurons. Recent results in neuroscience highlight the importance of <i>extrasynaptic</i> activities of neurons, that is, features and functioning of neurons outside their synapses. Previously it was thought that signals such as <i>neuropeptides</i> only assist neurons, but recently such signals have been given additional importance. Inspired by recent results, we define <i>wireless SN P systems</i> (<i>WSN P systems</i>). In WSN P systems, no synapses exist: regular expressions associated with each neuron are used to decide which spikes it receives. We provide two semantics of how to “interpret” the spikes released by neurons. A specific register machine is simulated to show the different style of programming WSN P systems compared to programming standard SN P systems and other variants. This style emphasizes a trade-off: WSN P systems can be more “flexible” since they are not limited by their synapses for sending spikes; however, losing the useful directed graph structure requires careful design of rules and expressions associated with each neuron. We use linear prime number encodings in constructing the expressions and rules of the neurons to prove that WSN P systems are Turing-complete in both spike semantics.</p>