Long-lasting desynchronization in rat hippocampal slice induced by coordinated reset stimulation

Abstract

In computational models it has been shown that appropriate stimulation protocols may reshape the connectivity pattern of neural or oscillator networks with synaptic plasticity in a way that the network learns or unlearns strong synchronization. The underlying mechanism is that a network is shifted from one attractor to another, so that long-lasting stimulation effects are caused which persist after the cessation of stimulation. Here we study long-lasting effects of multisite electrical stimulation in a rat hippocampal slice rendered epileptic by magnesium withdrawal. We show that desynchronizing coordinated reset stimulation causes a long-lasting desynchronization between hippocampal neuronal populations together with a widespread decrease in the amplitude of the epileptiform activity. In contrast, periodic stimulation induces a long-lasting increase in both synchronization and amplitude. Synchronization is a classical self-organization phenomenon ͓1͔. Abnormally strong neuronal synchronization is, e.g., found in epilepsy ͓2͔. Experiments have been devoted to suppress epileptiform activity by continuous or adaptive pulsatile or smooth electrical stimulation ͓3͔. Reinitiation of epileptiform activity follows stimulus removal immediately or after a few minutes ͑e.g., 4 min͒ ͓3͔. In contrast, our modelbased approach does not aim at a suppression of epileptiform activity during stimulation, but at a long-lasting change in the network's dynamics due to a stimulation-induced reshaping of the network connectivity.Network topology strongly impacts on network dynamics ͓4͔. Unlike in many physical systems, in biological neural networks the ͑coupling͒ strength of a synapse ͑conveying signals from a presynaptic to a postsynaptic neuron͒, is not constant, but depends on the neuronal timing ͓5͔: if a presynaptic spike advances the postsynaptic spike, the synaptic weight increases, whereas in the opposite case the synaptic weight decreases. This mechanism, the spike timingdependent plasticity ͑STDP͒, is fundamental to learning and memory in nervous systems ͓5͔. Dynamical and connectivity changes are tightly connected in neuronal populations ͓6͔. Neural and phase oscillator networks with STDP are multistable ͓7͔: the attractors differ in their fast neuronal dynamics and their slow synaptic dynamics ͑connectivity pattern͒. Stable states with stronger synchronization and stronger average synaptic weight coexist with stable desynchronized states with a weaker average synaptic weight ͓7͔.As shown numerically, the tight relationship between network dynamics and connectivity enables to reshape the connectivity by controlling the dynamics. Stimulation techniques may shift a network from one attractor to another, so that the stimulation effect outlasts stimulus offset: the network learns or unlearns synchrony ͓7,8͔. Periodic stimulation increases the amount of synchrony along with the rate of coincidences and the mean synaptic weight. The network is shifted from a desynchronized state with weak coupling to a synchronized state with str...

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