In the human neocortex, coherent theta oscillations between superficial and deep cortical layers are driven by deep layer neurons, suggesting distinct intrinsic electrophysiological properties of neurons across cortical layers. Here, we used in vitro whole-cell recordings to characterize pyramidal cells in layer 2/3 (L2/3) and layer 5 (L5) of the human neocortex. We found that human L5 pyramidal cells were more excitable and were endowed with a more prominent sag voltage and larger Ih currents relative to L2/3 neurons, that were abolished through direct pharmacological blockade. Although no peak in subthreshold resonance was observed for either L2/3 or L5 cells, we found that L5 neurons demonstrated greater spiking gain at low frequencies. Integrating patient-level demographic features revealed larger sag amplitudes in pyramidal cells recorded from older patients. These data suggest that sag is prominently expressed in L5 pyramidal cells and is a dynamic feature of human cortical microcircuits.

In the human neocortex coherent interlaminar theta oscillations are driven by deep cortical layers, suggesting neurons in these layers exhibit distinct electrophysiological properties. To characterize this potential distinctiveness, we use in vitro whole-cell recordings from cortical layers 2 and 3 (L2&3), layer 3c (L3c) and layer 5 (L5) of the human cortex. Across all layers we observe notable heterogeneity, indicating human cortical pyramidal neurons are an electrophysiologically diverse population. L5 pyramidal cells are the most excitable of these neurons and exhibit the most prominent sag current (abolished by blockade of the hyperpolarization activated cation current, Ih). While subthreshold resonance is more common in L3c and L5, we rarely observe this resonance at frequencies greater than 2 Hz. However, the frequency dependent gain of L5 neurons reveals they are most adept at tracking both delta and theta frequency inputs, a unique feature that may indirectly be important for the generation of cortical theta oscillations.

The plethora of inhibitory interneurons in the hippocampus and cortex play a pivotal role in generating rhythmic activity by clustering and synchronizing cell firing. Results of our simulations demonstrate that both the intrinsic cellular properties of neurons and the degree of network connectivity affect the characteristics of clustered dynamics exhibited in randomly connected, heterogeneous inhibitory networks. We quantify intrinsic cellular properties by the neuron's current-frequency relation (IF curve) and Phase Response Curve (PRC), a measure of how perturbations given at various phases of a neurons firing cycle affect subsequent spike timing. We analyze network bursting properties of networks of neurons with Type I or Type II properties in both excitability and PRC profile; Type I PRCs strictly show phase advances and IF curves that exhibit frequencies arbitrarily close to zero at firing threshold while Type II PRCs display both phase advances and delays and IF curves that have a non-zero frequency at threshold. Type II neurons whose properties arise with or without an M-type adaptation current are considered. We analyze network dynamics under different levels of cellular heterogeneity and as intrinsic cellular firing frequency and the time scale of decay of synaptic inhibition are varied. Many of the dynamics exhibited by these networks diverge from the predictions of the interneuron network gamma (ING) mechanism, as well as from results in all-to-all connected networks. Our results show that randomly connected networks of Type I neurons synchronize into a single cluster of active neurons while networks of Type II neurons organize into two mutually exclusive clusters segregated by the cells' intrinsic firing frequencies. Networks of Type II neurons containing the adaptation current behave similarly to networks of either Type I or Type II neurons depending on network parameters; however, the adaptation current creates differences in the cluster dynamics compared to those in networks of Type I or Type II neurons. To understand these results, we compute neuronal PRCs calculated with a perturbation matching the profile of the synaptic current in our networks. Differences in profiles of these PRCs across the different neuron types reveal mechanisms underlying the divergent network dynamics.

An improved understanding of the mechanisms underlying neuromodulatory approaches to mitigate seizure onset is needed to identify clinical targets for the treatment of epilepsy. Using a Wilson–Cowan-motivated network of inhibitory and excitatory populations, we examined the role played by intrinsic and extrinsic stimuli on the network’s predisposition to sudden transitions into oscillatory dynamics, similar to the transition to the seizure state. Our joint computational and mathematical analyses revealed that such stimuli, be they noisy or periodic in nature, exert a stabilizing influence on network responses, disrupting the development of such oscillations. Based on a combination of numerical simulations and mean-field analyses, our results suggest that high variance and/or high frequency stimulation waveforms can prevent multi-stability, a mathematical harbinger of sudden changes in network dynamics. By tuning the neurons’ responses to input, stimuli stabilize network dynamics away from these transitions. Furthermore, our research shows that such stabilization of neural activity occurs through a selective recruitment of inhibitory cells, providing a theoretical undergird for the known key role these cells play in both the healthy and diseased brain. Taken together, these findings provide new vistas on neuromodulatory approaches to stabilize neural microcircuit activity.

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While our understanding of human neurons is often inferred from rodent data, inter-species differences between neurons can be captured by building cellular models specifically from human data. This includes understanding differences at the level of ion channels and their implications for human brain function. Thus, we here present a full spiking, biophysically detailed multi-compartment model of a human layer 5 (L5) cortical pyramidal cell. Model development was primarily based on morphological and electrophysiological data from the same human L5 neuron, avoiding confounds of experimental variability. Focus was placed on describing the behavior of the hyperpolarization-activated cation (h-) channel, given increasing interest in this channel due to its role in pacemaking and differentiating cell types. We ensured that the model exhibited post-inhibitory rebound spiking considering its relationship with the h-current, along with other general spiking characteristics. The model was validated against data not used in its development, which highlighted distinctly slower kinetics of the human h-current relative to the rodent setting. We linked the lack of subthreshold resonance observed in human L5 neurons to these human-specific h-current kinetics. This work shows that it is possible and necessary to build human-specific biophysical neuron models in order to understand human brain dynamics.

Recent experimental literature has revealed that GABAergic interneurons exhibit increased activity prior to seizure onset, alongside additional evidence that such activity is synchronous and may arise abruptly. These findings have led some to hypothesize that this interneuronal activity may serve a causal role in driving the sudden change in brain activity that heralds seizure onset. However, 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. We note that while our comparisons are between networks of control and ictogenic neurons, the conclusions drawn specifically relate to the unusual dynamics that arise prior to seizure, and not seizure onset itself. However, providing a mechanistic explanation for this phenomenon specifically in a pro-ictogenic setting generates experimentally testable hypotheses regarding the role of inhibitory neurons in pre-ictal neural dynamics, and motivates further computational research into mechanisms underlying a newly hypothesized multi-step pathway to seizure initiated by inhibition.

15Most existing multi-compartment, mammalian neuron models are built from rodent data. However, 16 our increasing knowledge of differences between human and rodent neurons suggests that, to 17 understand the cellular basis of human brain function, we should build models from human data. 18 Here, we present the first full spiking, multi-compartment model of a human layer 5 cortical 19 pyramidal neuron. Model development balanced prioritizing current clamp data from the neuron 20 providing the model's morphology, while also ensuring the model's generalizability via preservation 21 of spiking properties observed in a secondary population of neurons, by "cycling" between these 22 data sets. The model was successfully validated against electrophysiological data not used in 23 model development, including experimentally observed subthreshold resonance characteristics. 24 Our model highlights kinetic differences in the h-current across species, with the unique 25 relationship between the model and experimental data allowing for a detailed investigation of the 26 relationship between the h-current and subthreshold resonance. 27 28 30 (Womelsdorf et al., 2014) within the six-layered neocortex stems from invasive and in vitro studies 31 in rodents and non-human primates. Whether or not such principles can be extended to human 32 neocortex remains speculative at best. Despite the significant transcriptomic convergence of 33 human and mouse neurons (Hodge et al., 2019), significant differences between human and rodent 34 cell-type properties exist. In vitro studies have identified differences between mouse and human 35 neurons in morphology (Mohan et al., 2015), dendritic integration (Beaulieu-Laroche et al., 2018; 36 Eyal et al., 2016), synaptic properties (Verhoog et al., 2013), and collective dynamics (McGinn and 37 Valiante, 2014; Molnár et al., 2008; Florez et al., 2013). However, less explored are the active 38 1 of 36 Manuscript submitted to eLife membrane properties of human cortical neurons, which together with their passive and synaptic 39 properties underlie oscillations which are of likely physiological relevance (Akam and Kullmann, 40 2014; Womelsdorf et al., 2014; Fries, 2005; Anastassiou et al., 2011; Hanslmayr et al., 2019; Vaz 41 et al., 2019). 42 Recently it has been shown that increased expression of hyperpolarization activated cation chan-43 nels (h-channels) contribute to the observed subthreshold resonance in supragranular layer human 44 pyramidal cells not seen in their rodent counterparts (Kalmbach et al., 2018). Such differential 45 expression of h-channels also appears to be present between superficial and deep layer neurons 46 of human cortex, with layer 5 (L5) pyramidal cells demonstrating a larger sag voltage mediated 47 65 et al., 2013; Beaulieu-Laroche et al., 2018) leads to two important questions for computational 66neuroscientists: in what settings is it appropriate to utilize rodent neuron models to glean insights 67 into the human brain, and when such approximations are u...