Every neuron is part of a network, exerting its function by transforming multiple spatiotemporal synaptic input patterns into a single spiking output. This function is specified by the particular shape and passive electrical properties of the neuronal membrane, and the composition and spatial distribution of ion channels across its processes. For a variety of physiological or pathological reasons, the intrinsic input/output function may change during a neuron’s lifetime. This process results in high variability in the peak specific conductance of ion channels in individual neurons. The mechanisms responsible for this variability are not well understood, although there are clear indications from experiments and modeling that degeneracy and correlation among multiple channels may be involved. Here, we studied this issue in biophysical models of hippocampal CA1 pyramidal neurons and interneurons. Using a unified data-driven simulation workflow and starting from a set of experimental recordings and morphological reconstructions obtained from rats, we built and analyzed several ensembles of morphologically and biophysically accurate single cell models with intrinsic electrophysiological properties consistent with experimental findings. The results suggest that the set of conductances expressed in any given hippocampal neuron may be considered as belonging to two groups: one subset is responsible for the major characteristics of the firing behavior in each population and the other is responsible for a robust degeneracy. Analysis of the model neurons suggests several experimentally testable predictions related to the combination and relative proportion of the different conductances that should be expressed on the membrane of different types of neurons for them to fulfill their role in the hippocampus circuitry.

Strong constraints on the neural mechanisms underlying the formation of place fields in the rodent hippocampus come from the systematic changes in spatial activity patterns that are consequent on systematic environmental manipulations. We describe an attractor network model of area CA3 in which local, recurrent, excitatory, and inhibitory interactions generate appropriate place cell representations from location-and directionspecific activity in the entorhinal cortex.In the model, familiarity with the environment, as reflected by activity in neuromodulatory systems, influences the efficacy and plasticity of the recurrent and feedforward inputs to CA3. In unfamiliar, novel, environments, mossy fiber inputs impose activity patterns on CA3, and the recurrent collaterals and the perforant path inputs are subject to graded Hebbian plasticity.This sculpts CA3 attractors and associates them with activity patterns in the entorhinal cortex. In familiar environments, place fields are controlled by the way that perforant path inputs select among the attractors.Depending on the training experience provided, the model generates place fields that are either directional or nondirectional and whose changes when the environment undergoes simple geometric transformations are in accordance with experimental data. Representations of multiple environments can be stored and recalled with little interference, and these have the appropriate degrees of similarity in visually similar environments. Key words: hippocampus; place cells; CA3; recurrent network; plasticity; familiarity; neuromodulation; directionality; attractor; modelThe hippocampus is known to be involved in spatial learning and memory in rodents. Some of the most convincing evidence for this is the presence of place cells in areas CA3 and CA1 of the hippocampus (O' Keefe and Dostrovsky, 1971;O'Keefe, 1976) and of many other types of spatially selective cells in neighboring areas (Quirk et al., 1992;Jung and McNaughton, 1993). Principal neurons in CA3 and CA1 are active only when the animal is located in a well defined local region of the environment (a place field) and collectively provide a population code for spatial position (Wilson and McNaughton, 1993). The question we address is how this comes to be in a way that is consistent with the evidence for the involvement of the hippocampus in more general forms of memory.A key anatomical feature of area CA3 is that its pyramidal cells receive the majority of their inputs from other CA3 pyramidal cells (Amaral and Witter, 1989;Amaral et al., 1990). The resulting recurrent network has been extensively explored as a plastic attractor model of the way that the hippocampus acts as a general memory (Marr, 1971;McNaughton and Morris, 1987;Hasselmo et al., 1996;Levy, 1996;Rolls, 1996) but has been widely ignored by models that are intended to account for various properties of place cells (Zipser, 1985;Sharp, 1991;Touretzky and Redish, 1996; Burgess et al., 1997) (but see Battaglia and Treves, 1998).The model of Samsonovich and McN...

Sharp wave-ripples and interictal events are physiological and pathological forms of transient high activity in the hippocampus with similar features. Sharp wave-ripples have been shown to be essential in memory consolidation, whereas epileptiform (interictal) events are thought to be damaging. It is essential to grasp the difference between physiological sharp wave-ripples and pathological interictal events to understand the failure of control mechanisms in the latter case. We investigated the dynamics of activity generated intrinsically in the Cornu Ammonis region 3 of the mouse hippocampus in vitro, using four different types of intervention to induce epileptiform activity. As a result, sharp wave-ripples spontaneously occurring in Cornu Ammonis region 3 disappeared, and following an asynchronous transitory phase, activity reorganized into a new form of pathological synchrony. During epileptiform events, all neurons increased their firing rate compared to sharp wave-ripples. Different cell types showed complementary firing: parvalbumin-positive basket cells and some axo-axonic cells stopped firing as a result of a depolarization block at the climax of the events in high potassium, 4-aminopyridine and zero magnesium models, but not in the gabazine model. In contrast, pyramidal cells began firing maximally at this stage. To understand the underlying mechanism we measured changes of intrinsic neuronal and transmission parameters in the high potassium model. We found that the cellular excitability increased and excitatory transmission was enhanced, whereas inhibitory transmission was compromised. We observed a strong short-term depression in parvalbumin-positive basket cell to pyramidal cell transmission. Thus, the collapse of pyramidal cell perisomatic inhibition appears to be a crucial factor in the emergence of epileptiform events.

Sharp-wave ripples are transient oscillatory events in the hippocampus that are associated with the reactivation of neuronal ensembles within specific circuits during memory formation. Fast-spiking, parvalbumin-expressing interneurons (FS-PV INs) are thought to provide fast integration in these oscillatory circuits by suppressing regenerative activity in their dendrites. Here, using fast 3D two-photon imaging and a caged glutamate, we challenge this classical view by demonstrating that FS-PV IN dendrites can generate propagating Ca(2+) spikes during sharp-wave ripples. The spikes originate from dendritic hot spots and are mediated dominantly by L-type Ca(2+) channels. Notably, Ca(2+) spikes were associated with intrinsically generated membrane potential oscillations. These oscillations required the activation of voltage-gated Na(+) channels, had the same frequency as the field potential oscillations associated with sharp-wave ripples, and controlled the phase of action potentials. Furthermore, our results demonstrate that the smallest functional unit that can generate ripple-frequency oscillations is a segment of a dendrite.

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The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

Key pointsr Sleep spindle frequency positively, duration negatively correlates with brain temperature. r Local heating of the thalamus produces similar effects in the heated area. r Thalamic network model corroborates temperature dependence of sleep spindle frequency. r Brain temperature shows spontaneous microfluctuations during both anesthesia and natural sleep.r Larger fluctuations are associated with epochs of REM sleep. r Smaller fluctuations correspond to the alteration of spindling and delta epochs of infra-slow oscillation.Abstract Every form of neural activity depends on temperature, yet its relationship to brain rhythms is poorly understood. In this work we examined how sleep spindles are influenced by changing brain temperatures and how brain temperature is influenced by sleep oscillations. We employed a novel thermoelectrode designed for measuring temperature while recording neural activity. We found that spindle frequency is positively correlated and duration negatively correlated with brain temperature. Local heating of the thalamus replicated the temperature dependence of spindle parameters in the heated area only, suggesting biophysical rather than global modulatory mechanisms, a finding also supported by a thalamic network model. Finally, Marton Csernai is a postdoctoral researcher with a background in electrical engineering and computer science. His primary research focuses on the network effects of sleep processes, especially how cell ensembles behave during sleep spindles. M. Csernai, S. Borbély and K. Kocsis contributed equally to this work.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. 4070M. Csernai and others J Physiol 597.15 we show that switches between oscillatory states also influence brain temperature on a shorter and smaller scale. Epochs of paradoxical sleep as well as the infra-slow oscillation were associated with brain temperature fluctuations below 0.2°C. Our results highlight that brain temperature is massively intertwined with sleep oscillations on various time scales.

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