Seizure suppression by deep brain stimulation (DBS) applies high frequency stimulation (HFS) to grey matter to block seizures. In this presentation, I will present the results of a different method that employs low frequency stimulation (LFS) (1 to 10Hz) of white matter tracts to prevent seizures. The approach has been shown to be effective in the hippocampus by stimulating the ventral and dorsal hippocampal commissure in both animal and human studies respectively for mesial temporal lobe seizures. A similar stimulation paradigm has been shown to be effective at controlling focal cortical seizures in rats with corpus callosum stimulation. This stimulation targets the axons of the corpus callosum innervating the focal zone at low frequencies (5 to 10Hz) and has been shown to significantly reduce both seizure and spike frequency. The mechanisms of this suppression paradigm have been elucidated with in-vitro studies and involve the activation of two long-lasting inhibitory potentials GABAB and sAHP. LFS mechanisms are similar in both hippocampus and cortical brain slices. Additionally, the results show that LFS does not block seizures but rather decreases the excitability of the tissue to prevent seizures. Three methods of seizure suppression, LFS applied to fiber tracts, HFS applied to focal zone and stimulation of the anterior nucleus of the thalamus (ANT) were compared directly in the same animal in an in-vivo epilepsy model. The results indicate that LFS generated a significantly higher level of suppression, indicating LFS of white matter tract could be a useful addition as a stimulation paradigm for the treatment of epilepsy.

Spike Timing Dependent Plasticity (STDP) is argued to modulate synaptic strength depending on the timing of pre- and postsynaptic spikes. Physiological experiments identified a variety of temporal kernels: Hebbian, anti-Hebbian and symmetrical LTP/LTD. In this work we present a novel plasticity model, the Voltage-Dependent Pathway Model (VDP), which is able to replicate those distinct kernel types and intermediate versions with varying LTP/LTD ratios and symmetry features. In addition, unlike previous models it retains these characteristics for different neuron models, which allows for comparison of plasticity in different neuron types. The plastic updates depend on the relative strength and activation of separately modeled LTP and LTD pathways, which are modulated by glutamate release and postsynaptic voltage. We used the 15 neuron type parametrizations in the GLIF5 model presented by Teeter et al. (2018) in combination with the VDP to simulate a range of standard plasticity protocols including standard STDP experiments, frequency dependency experiments and low frequency stimulation protocols. Slight variation in kernel stability and frequency effects can be identified between the neuron types, suggesting that the neuron type may have an effect on the effective learning rule. This plasticity model builds a middle ground between biophysical and phenomenological models allowing not just for the combination with more complex and biophysical neuron models, but is also computationally efficient so can be used in network simulations. Therefore it offers the possibility to explore the functional role of the different kernel types and electrophysiological differences in heterogeneous networks in future work.