Models that contain the Implementer : Rich, Scott [sbrich at umich.edu]

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    Models   Description
1.  Human Cortical L5 Pyramidal Cell (Rich et al. 2021)
This paper presents a full spiking, biophysically detailed multi-compartment model of a human cortical layer 5 (L5) pyramidal cell, where model development was primarily based on morphological and electrophysiological data from the same neuron. Focus was placed on capturing distinctly human dynamics of the h-channel and led to the articulation of a novel model of this channel's dynamics in humans. This led to an explanation for the surprising lack of subthreshold resonance seen in these cells in the human as opposed to rodent setting.
2.  Inhibitory network bistability explains increased activity prior to seizure onset (Rich et al 2020)
" ... the mechanisms predisposing an inhibitory network toward increased activity, specifically prior to ictogenesis, without a permanent change to inputs to the system remain unknown. We address this question by comparing simulated inhibitory networks containing control interneurons and networks containing hyperexcitable interneurons modeled to mimic treatment with 4-Aminopyridine (4-AP), an agent commonly used to model seizures in vivo and in vitro. Our in silico study demonstrates that model inhibitory networks with 4-AP interneurons are more prone than their control counterparts to exist in a bistable state in which asynchronously firing networks can abruptly transition into synchrony driven by a brief perturbation. This transition into synchrony brings about a corresponding increase in overall firing rate. We further show that perturbations driving this transition could arise in vivo from background excitatory synaptic activity in the cortex. Thus, we propose that bistability explains the increase in interneuron activity observed experimentally prior to seizure via a transition from incoherent to coherent dynamics. Moreover, bistability explains why inhibitory networks containing hyperexcitable interneurons are more vulnerable to this change in dynamics, and how such networks can undergo a transition without a permanent change in the drive. ..."

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