The Unexplored Territory of Neural Models: Potential Guides for Exploring the Function of Metabotropic Neuromodulation

The space of possible neural models is enormous and under-explored. Single cell computational neuroscience models account for a range of dynamical properties of membrane potential, but typically do not address network function. In contrast, most models focused on network function address the dimensions of excitatory weight matrices and firing thresholds without addressing the complexities of metabotropic receptor effects on intrinsic properties. There are many under-explored dimensions of neural parameter space, and the field needs a framework for representing what has been explored and what has not. Possible frameworks include maps of parameter spaces, or efforts to categorize the fundamental elements and molecules of neural circuit function. Here we review dimensions that are under-explored in network models that include the metabotropic modulation of synaptic plasticity and presynaptic inhibition, spike frequency adaptation due to calcium-dependent potassium currents, and afterdepolarization due to calcium-sensitive non-specific cation currents and hyperpolarization activated cation currents. Neuroscience research should more effectively explore possible functional models incorporating under-explored dimensions of neural function.

Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit

The hippocampal theta rhythm plays important roles in information processing; however, the mechanisms of its generation are not well understood. We developed a data-driven, supercomputer-based, full-scale (1:1) model of the rodent CA1 area and studied its interneurons during theta oscillations. Theta rhythm with phase-locked gamma oscillations and phase-preferential discharges of distinct interneuronal types spontaneously emerged from the isolated CA1 circuit without rhythmic inputs. Perturbation experiments identified parvalbumin-expressing interneurons and neurogliaform cells, as well as interneuronal diversity itself, as important factors in theta generation. These simulations reveal new insights into the spatiotemporal organization of the CA1 circuit during theta oscillations.

Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit

The hippocampal theta rhythm plays important roles in information processing; however, the mechanisms of its generation are not well understood. We developed a data-driven, supercomputer-based, full-scale (1:1) model of the rodent CA1 area and studied its interneurons during theta oscillations. Theta rhythm with phase-locked gamma oscillations and phase-preferential discharges of distinct interneuronal types spontaneously emerged from the isolated CA1 circuit without rhythmic inputs. Perturbation experiments identified parvalbumin-expressing interneurons and neurogliaform cells, as well as interneuronal diversity itself, as important factors in theta generation. These simulations reveal new insights into the spatiotemporal organization of the CA1 circuit during theta oscillations.

DOI:http://dx.doi.org/10.7554/eLife.18566.001

Target-selectivity of parvalbumin-positive interneurons in layer II of medial entorhinal cortex in normal and epileptic animals

The medial entorhinal cortex layer II (MEClayerII ) is a brain region critical for spatial navigation and memory, and it also demonstrates a number of changes in patients with, and animal models of, temporal lobe epilepsy (TLE). Prior studies of GABAergic microcircuitry in MEClayerII revealed that cholecystokinin-containing basket cells (CCKBCs) select their targets on the basis of the long-range projection pattern of the postsynaptic principal cell. Specifically, CCKBCs largely avoid reelin-containing principal cells that form the perforant path to the ipsilateral dentate gyrus and preferentially innervate non-perforant path forming calbindin-containing principal cells. We investigated whether parvalbumin containing basket cells (PVBCs), the other major perisomatic targeting GABAergic cell population, demonstrate similar postsynaptic target selectivity as well. In addition, we tested the hypothesis that the functional or anatomic arrangement of circuit selectivity is disrupted in MEClayerII in chronic TLE, using the repeated low-dose kainate model in rats. In control animals, we found that PVBCs innervated both principal cell populations, but also had significant selectivity for calbindin-containing principal cells in MEClayerII . However, the magnitude of this preference was smaller than for CCKBCs. In addition, axonal tracing and paired recordings showed that individual PVBCs were capable of contacting both calbindin and reelin-containing principal cells. In chronically epileptic animals, we found that the intrinsic properties of the two principal cell populations, the GABAergic perisomatic bouton numbers, and selectivity of the CCKBCs and PVBCs remained remarkably constant in MEClayerII . However, miniature IPSC frequency was decreased in epilepsy, and paired recordings revealed the presence of direct excitatory connections between principal cells in the MEClayerII in epilepsy, which is unusual in normal adult MEClayerII . Taken together, these findings advance our knowledge about the organization of perisomatic inhibition both in control and in epileptic animals. © 2015 Wiley Periodicals, Inc.

Quantitative assessment of CA1 local circuits: knowledge base for interneuron-pyramidal cell connectivity

In this work, through a detailed literature review, data-mining, and extensive calculations, we provide a current, quantitative estimate of the cellular and synaptic constituents of the CA1 region of the rat hippocampus. Beyond estimating the cell numbers of GABAergic interneuron types, we calculate their convergence onto CA1 pyramidal cells and compare it with the known input synapses on CA1 pyramidal cells. The convergence calculation and comparison are also made for excitatory inputs to CA1 pyramidal cells. In addition, we provide a summary of the excitatory and inhibitory convergence onto interneurons. The quantitative knowledge base assembled and synthesized here forms the basis for data-driven, large-scale computational modeling efforts. Additionally, this work highlights specific instances where the available data are incomplete, which should inspire targeted experimental projects toward a more complete quantification of the CA1 neurons and their connectivity.