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Simple models of quantitative firing phenotypes in hippocampal neurons: Comprehensive coverage of intrinsic diversity.

ABSTRACT: Patterns of periodic voltage spikes elicited by a neuron help define its dynamical identity. Experimentally recorded spike trains from various neurons show qualitatively distinguishable features such as delayed spiking, spiking with or without frequency adaptation, and intrinsic bursting. Moreover, the input-dependent responses of a neuron not only show different quantitative features, such as higher spike frequency for a stronger input current injection, but can also exhibit qualitatively different responses, such as spiking and bursting under different input conditions, thus forming a complex phenotype of responses. In previous work, the comprehensive knowledge base of hippocampal neuron types Hippocampome.org systematically characterized various spike pattern phenotypes experimentally identified from 120 neuron types/subtypes. In this paper, we present a complete set of simple phenomenological models that quantitatively reproduce the diverse and complex phenotypes of hippocampal neurons. In addition to point-neuron models, we created compact multi-compartment models with up to four compartments, which will allow spatial segregation of synaptic integration in network simulations. Electrotonic compartmentalization observed in our compact multi-compartment models is qualitatively consistent with experimental observations. The models were created using an automated pipeline based on evolutionary algorithms. This work maps 120 neuron types/subtypes in the rodent hippocampus to a low-dimensional model space and adds another dimension to the knowledge accumulated in Hippocampome.org. Computationally efficient representations of intrinsic dynamics, along with other pieces of knowledge available in Hippocampome.org, provide a biologically realistic platform to explore the large-scale interactions of various neuron types at the mesoscopic level.

SUBMITTER: Venkadesh S

PROVIDER: S-EPMC6837624 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Simple models of quantitative firing phenotypes in hippocampal neurons: Comprehensive coverage of intrinsic diversity.

Venkadesh Siva S Komendantov Alexander O AO Wheeler Diek W DW Hamilton David J DJ Ascoli Giorgio A GA

PLoS computational biology 20191028 10

Patterns of periodic voltage spikes elicited by a neuron help define its dynamical identity. Experimentally recorded spike trains from various neurons show qualitatively distinguishable features such as delayed spiking, spiking with or without frequency adaptation, and intrinsic bursting. Moreover, the input-dependent responses of a neuron not only show different quantitative features, such as higher spike frequency for a stronger input current injection, but can also exhibit qualitatively diffe ...[more]

PMID: 31658260

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Project description:KEY POINTS:Neurons in the hypothalamus of the brain which secrete the peptide kisspeptin are important regulators of reproduction, and normal reproductive development. Electrical activity, in the form of action potentials, or spikes, leads to secretion of peptides and neurotransmitters, influencing the activity of downstream neurons; in kisspeptin neurons, this activity is highly irregular, but the mechanism of this is not known. In this study, we show that irregularity depends on the presence of a particular type of potassium ion channel in the membrane, which opens transiently in response to electrical excitation. The results contribute to understanding how kisspeptin neurons generate and time their membrane potential spikes, and how reliable this process is. Improved understanding of the activity of kisspeptin neurons, and how it shapes their secretion of peptides, is expected to lead to better treatment for reproductive dysfunction and disorders of reproductive development. ABSTRACT:Kisspeptin neurons in the hypothalamus are critically involved in reproductive function, via their effect on GnRH neuron activity and consequent gonadotropin release. Kisspeptin neurons show an intrinsic irregularity of firing, but the mechanism of this remains unclear. To address this, we carried out targeted whole-cell patch-clamp recordings of kisspeptin neurons in the arcuate nucleus (Kiss1Arc ), in brain slices isolated from adult male Kiss-Cre:tdTomato mice. Cells fired irregularly in response to constant current stimuli, with a wide range of spike time variability, and prominent subthreshold voltage fluctuations. In voltage clamp, both a persistent sodium (NaP) current and a fast transient (A-type) potassium current were apparent, activating at potentials just below the threshold for spiking. These currents have also previously been described in irregular-spiking cortical interneurons, in which the A-type current, mediated by Kv4 channels, interacts with NaP current to generate complex dynamics of the membrane potential, and irregular firing. In Kiss1Arc neurons, A-type current was blocked by phrixotoxin, a specific blocker of Kv4.2/4.3 channels, and consistent expression of Kv4.2 transcripts was detected by single-cell RT-PCR. In addition, firing irregularity was correlated to the density of A-type current in the membrane. Using conductance injection, we demonstrated that adding Kv4-like potassium conductance (gKv4 ) to a cell produces a striking increase in firing irregularity, and excitability is reduced, while subtracting gKv4 has the opposite effects. Thus, we propose that Kv4 interacting dynamically with NaP is a key determinant of the irregular firing behaviour of Kiss1Arc neurons, shaping their physiological function in gonadotropin release.

Project description:The diversity of intrinsic dynamics observed in neurons may enhance the computations implemented in the circuit by enriching network-level emergent properties such as synchronization and phase locking. Large-scale spiking network models of entire brain regions offer a platform to test theories of neural computation and cognitive function, providing useful insights on information processing in the nervous system. However, a systematic in-depth investigation requires network simulations to capture the biological intrinsic diversity of individual neurons at a sufficient level of accuracy. The computationally efficient Izhikevich model can reproduce a wide range of neuronal behaviors qualitatively. Previous studies using optimization techniques, however, were less successful in quantitatively matching experimentally recorded voltage traces. In this article, we present an automated pipeline based on evolutionary algorithms to quantitatively reproduce features of various classes of neuronal spike patterns using the Izhikevich model. Employing experimental data from Hippocampome.org, a comprehensive knowledgebase of neuron types in the rodent hippocampus, we demonstrate that our approach reliably fit Izhikevich models to nine distinct classes of experimentally recorded spike patterns, including delayed spiking, spiking with adaptation, stuttering, and bursting. Importantly, by leveraging the parameter-exploration capabilities of evolutionary algorithms, and by representing qualitative spike pattern class definitions in the error landscape, our approach creates several suitable models for each neuron type, exhibiting appropriate feature variabilities among neurons. Moreover, we demonstrate the flexibility of our methodology by creating multi-compartment Izhikevich models for each neuron type in addition to single-point versions. Although the results presented here focus on hippocampal neuron types, the same strategy is broadly applicable to any neural systems.

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Simple models of quantitative firing phenotypes in hippocampal neurons: Comprehensive coverage of intrinsic diversity.

Publications

Simple models of quantitative firing phenotypes in hippocampal neurons: Comprehensive coverage of intrinsic diversity.

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