Multi-scale modeling of altered synaptic plasticity related to Amyloid ß effects.

As suggested by Palop and Mucke (2010) pathologically elevated ß-amyloid (Aß) impairs long term potentiation (LTP) and enhances long term depression (LTD) possible underlying mechanisms in Alzheimer's Disease (AD). In the present paper we adopt and further elaborate a phenomenological computational model of bidirectional plasticity based on the calcium control hypothesis of Shouval et al. (2002). First, to account for Aß effects the activation function Ω was modified assuming competition between LTP and LTD, and parameter sets were identified that well describe both normal and pathological synaptic plasticity processes. Second, a biophysically plausible kinetic model of bidirectional synaptic plasticity by D'Alcantara et al. (2003) was used to support findings of the phenomenological model and to further explain underlying kinetic processes. Model fitting pointed out molecular contributors, particularly calcineurin and type 1 protein phosphatase that might contribute to observed physiological disturbances in AD.

Modelling Ebola.

This paper is the combination of a review on the problems of data collection and modelling efforts of the recent Ebola epidemics. After a brief review of data availability, the modelling frameworks have been discussed. Both deterministic and stochastic models have been reviewed and supplemented with a short discussion of some problems of parameter estimation. The methods have been illustrated by a realistic case study. A hint is given for the scope and limits of prediction and control.

Teaching computational neuroscience.

The problems and beauty of teaching computational neuroscience are discussed by reviewing three new textbooks.

Anxiolytic drugs and altered hippocampal theta rhythms: the quantitative systems pharmacological approach.

The spirit of systems pharmacology was adopted to study the possible mechanisms of anxiolytic drugs on hippocampal electric patterns. The frequency of the hippocampal theta rhythm increases linearly with the intensity of electrical stimulation to the brainstem. The reduction of mean theta frequency in this paradigm predicts the clinical efficacy of anxiolytic drugs. The purpose of this study was to investigate the mechanisms by which anxiolytics produce their characteristic effects on the slope and intercept of the stimulus-frequency relationship of hippocampal theta. A network of neuron populations that generates septo-hippocampal theta rhythm was modeled using a compartmental modeling technique. The influence of cellular and synaptic parameters on network frequency was studied. Results show that halving the rate of rise and fall of pyramidal hyperpolarization-activated (Ih) conductance lowers nPO elicited theta frequency at low levels of stimulation. Results also suggest that increasing the decay time constant of inhibitory post-synaptic current can reduce the frequency of low nPO stimulation elicited theta rhythm, while maximal synaptic conductance of GABA-mediated synapses has little effect on frequency. Given their similar effect on network dynamics as by known anxiolytics, these parameter manipulations may mimic or predict the biophysical manifestations of anxiolytic action within the septo-hippocampal system.

Localization of single-cell current sources based on extracellular potential patterns: the spike CSD method.

Traditional current source density (tCSD) calculation method calculates neural current source distribution of extracellular (EC) potential patterns, thus providing important neurophysiological information. While the tCSD method is based on physical principles, it adopts some assumptions, which can not hold for single-cell activity. Consequently, tCSD method gives false results for single-cell activity. A new, spike CSD (sCSD) method has been developed, specifically designed to reveal CSD distribution of single cells during action potential generation. This method is based on the inverse solution of the Poisson-equation. The efficiency of the method is tested and demonstrated with simulations, and showed, that the sCSD method reconstructed the original CSD more precisely than the tCSD. The sCSD method is applied to EC spatial potential patterns of spikes, measured in cat primary auditory cortex with a 16-channel chronically implanted linear probe in vivo. Using our method, the cell-electrode distances were estimated and the spatio-temporal CSD distributions were reconstructed. The results suggested, that the new method is potentially useful in determining fine details of the spatio-temporal dynamics of spikes.

Model-based dynamical analysis of functional disconnection in schizophrenia.

Schizophrenia is shown to be associated with impaired interactions in functional macro-networks of the brain. The focus of our study was if there is an impairment of cognitive control of learning during schizophrenia. To investigate this question, we collected fMRI data from a group of stable schizophrenia patients and controls performing an object-location associative learning task in which the learning performance of the patient group was significantly worse. We applied Dynamic Causal Modeling to analyze the fMRI data. A set of causal models of BOLD signal generation was defined to evaluate connections between five regions material to the task (Primary Visual Cortex, Superior Parietal and Inferior Temporal Cortex, Hippocampus and Dorsal Prefrontal Cortex). Bayesian model selection was used to investigate hypotheses on differences in model architecture across groups, and indicated fundamental differences in model architecture in patients compared to controls. Models lacking connections related to cognitive control were more probable in the patient group. Hypotheses on differences in effective connectivity between groups were tested by comparing estimates of neural coupling parameters in winning model structures. This analysis indicated reduced fronto-hippocampal and hippocampo-inferior temporal coupling in patients, and reduced excitatory modulation of these pathways by learning. These findings may account for the documented reductions in learning performance of schizophrenia patients.

Parallel computational subunits in dentate granule cells generate multiple place fields.

A fundamental question in understanding neuronal computations is how dendritic events influence the output of the neuron. Different forms of integration of neighbouring and distributed synaptic inputs, isolated dendritic spikes and local regulation of synaptic efficacy suggest that individual dendritic branches may function as independent computational subunits. In the present paper, we study how these local computations influence the output of the neuron. Using a simple cascade model, we demonstrate that triggering somatic firing by a relatively small dendritic branch requires the amplification of local events by dendritic spiking and synaptic plasticity. The moderately branching dendritic tree of granule cells seems optimal for this computation since larger dendritic trees favor local plasticity by isolating dendritic compartments, while reliable detection of individual dendritic spikes in the soma requires a low branch number. Finally, we demonstrate that these parallel dendritic computations could contribute to the generation of multiple independent place fields of hippocampal granule cells.

Distance coding strategies based on the entorhinal grid cell system.

Estimating and keeping track of the distance from salient points of the environment are important constituents of the spatial awareness and navigation. In rodents, the majority of principal cells in the hippocampus are known to be correlated with the position of the animal. However, the lack of topography in the hippocampal cognitive map does not support the assumption that connections between these cells are able to store and recall distances between coded positions. In contrast, the firing fields of the grid cells in the medial entorhinal cortex form triangular grids and are organized on metrical principles. We suggest a model in which a hypothesized 'distance cell' population is able to extract metrics from the activity of grid cells. We show that storing the momentary activity pattern of the grid cell system in a freely chosen position by one-shot learning and comparing it to the actual grid activity at other positions results in a distance dependent activity of these cells. The actual distance of the animal from the origin can be decoded directly by selecting the distance cell receiving the largest excitation or indirectly via transmission of local interneurons. We found that direct decoding works up to the longest grid spacing, but fails on smaller scales, while the indirect way provides precise distance determination up to the half of the longest grid spacing. In both cases, simulated distance cells have a multi-peaked, patchy spatial activity pattern consistent with the experimentally observed behavior of granule cells in the dentate gyrus.

Impaired associative learning in schizophrenia: behavioral and computational studies.

Associative learning is a central building block of human cognition and in large part depends on mechanisms of synaptic plasticity, memory capacity and fronto-hippocampal interactions. A disorder like schizophrenia is thought to be characterized by altered plasticity, and impaired frontal and hippocampal function. Understanding the expression of this dysfunction through appropriate experimental studies, and understanding the processes that may give rise to impaired behavior through biologically plausible computational models will help clarify the nature of these deficits. We present a preliminary computational model designed to capture learning dynamics in healthy control and schizophrenia subjects. Experimental data was collected on a spatial-object paired-associate learning task. The task evinces classic patterns of negatively accelerated learning in both healthy control subjects and patients, with patients demonstrating lower rates of learning than controls. Our rudimentary computational model of the task was based on biologically plausible assumptions, including the separation of dorsal/spatial and ventral/object visual streams, implementation of rules of learning, the explicit parameterization of learning rates (a plausible surrogate for synaptic plasticity), and learning capacity (a plausible surrogate for memory capacity). Reductions in learning dynamics in schizophrenia were well-modeled by reductions in learning rate and learning capacity. The synergy between experimental research and a detailed computational model of performance provides a framework within which to infer plausible biological bases of impaired learning dynamics in schizophrenia.

Pharmacological and computational analysis of alpha-subunit preferential GABA(A) positive allosteric modulators on the rat septo-hippocampal activity.

Clinically most active anxiolytic drugs are positive allosteric modulators (PAMs) of GABA(A) receptors, represented by benzodiazepine compounds. Due to their non-selective profile, however, they potently modulate several sup-type specific GABA(A) receptors, contributing to their broad-range side effects. Based on observations in genetically altered mice, however, it has been proposed that anxiolytic action of benzodiazepines is predominantly mediated by GABA(A) alpha2/3 subunit-containing receptors. In the present study we analyzed the actions of the preferential GABA(A) alpha1 and alpha2/3 PAMs, zolpidem and L-838417, respectively on hippocampal EEG and medial septum neuronal activity in anesthetized rats. In parallel, a computational model was constructed to model pharmacological actions of these compounds on the septo-hippocampal circuitry. The present results demonstrated that zolpidem inhibited theta oscillation both in the hippocampus and septum, and profoundly inhibited firing activity of septal neurons. L-838417 also inhibited hippocampal and septal theta oscillation, however, it did not significantly alter firing rate activity of septal neurons. Our computational model showed that cessation of periodic firing of hippocampo-septal neurons, representing absence of hippocampal theta activity, disrupted oscillation of septal units, without altering their overall firing activity, similar to changes observed in our in vivo experiments following administration of L-838417. Understanding the correlation between changes in septo-hippocampal activity and actions of selective modulators of GABA(A) subtype receptor modulators would further advance design of anxiolytic drugs.

Intrinsic and synaptic mechanisms determining the timing of neuron population activity during hippocampal theta oscillation.

Hippocampal theta (3-8 Hz) is a major electrophysiological activity in rodents, which can be found in primates and humans as well. During theta activity, pyramidal cells and different classes of interneurons were shown to discharge at different phases of the extracellular theta. A recent in vitro study has shown that theta-frequency oscillation can be elicited in a hippocampal CA1 slice by the activation of metabotropic glutamate receptors with similar pharmacological and physiological profile that was found in vivo. We constructed a conductance based three-population network model of the hippocampal CA1 region to study the specific roles of neuron types in the generation of the in vitro theta oscillation and the emergent network properties. Interactions between pairs of neuron populations were studied systematically to assess synchronization and delay properties. We showed that the circuitry consisting of pyramidal cells and two types of hippocampal interneurons [basket and oriens lacunosum-moleculare (O-LM) neurons] was able to generate coherent theta-frequency population oscillation. Furthermore, we found that hyperpolarization-activated nonspecific cation current in pyramidal cells, but not in O-LM neurons, plays an important role in the timing of spike generation, and thus synchronization of pyramidal cells. The model was shown to exhibit the same phase differences between neuron population activities found in vivo, supporting the idea that these patterns of activity are determined internal to the hippocampus.

From electric patterns to drugs: perspectives of computational neuroscience in drug design.

A novel way of computational modeling by integrating compartmental neural techniques and detailed kinetic description of pharmacological modulation of transmitter-receptor interaction is offered as a method to test the electro-physiological and behavioral effects of potential drugs. Even more, an inverse method is suggested as a method for controlling a neural system to realize a prescribed temporal pattern. Generation and pharamcological modulation of theta rhythm in area CA1 of the hippocampus related to anxiety is analyzed. Integrative modeling might help to find positive allosteric modulators of alpha1 subunits as potential candidates for being selective anxyolitics.

Computational neuropharmacology: dynamical approaches in drug discovery.

Computational approaches that adopt dynamical models are widely accepted in basic and clinical neuroscience research as indispensable tools with which to understand normal and pathological neuronal mechanisms. Although computer-aided techniques have been used in pharmaceutical research (e.g. in structure- and ligand-based drug design), the power of dynamical models has not yet been exploited in drug discovery. We suggest that dynamical system theory and computational neuroscience--integrated with well-established, conventional molecular and electrophysiological methods--offer a broad perspective in drug discovery and in the search for novel targets and strategies for the treatment of neurological and psychiatric diseases.

Hippocampal theta rhythms from a computational perspective: code generation, mood regulation and navigation.

In this paper three computer models are summarized discussing different functions of the cortico-hippocampal system. Mood regulation, rhythm and code generation and navigation are integrated into a coherent conceptual framework around the concepts of structural hierarchy and circular causality. First, a model of spatio-temporal code generation is reviewed in which the hippocampal population theta rhythm plays an important role. Next, generation and pharmcological modulation of this rhythm is examined using a computer model of multiple cell populations forming a feed-back loop within the hippocampus and between the septum and the hippocampus. Last, an abstract, but biologically motivated model of navigation is described which achieves a near optimal mode of navigation by composing hierarchical levels of the cortico-hippocampal system. The connections among the different hierarchical structures of the cortico-hippocampal organization and their functional roles are discussed.

Theta oscillation-coupled dendritic spiking integrates inputs on a long time scale.

Persistent neural activity lasting for seconds after transient stimulation has been observed in several brain areas. This activity has been taken to be indicative of the integration of inputs on long time scales. Passive membrane properties render neural time constants to be on the order of milliseconds. Intense synaptic bombardment, characteristic of in vivo states, was previously shown to further reduce the time scale of effective integration. We explored how long-term integration in single cells could be supported by dendritic spikes coupled with the theta oscillation, a prominent brain rhythm often observed during working memory tasks. We used a two-compartmental conductance-based model of a hippocampal pyramidal cell to study the interplay of intrinsic dynamics with periodic inputs in the theta frequency band. We show that periodic dendritic spiking integrates inputs by shifting the phase relative to an external oscillation, since spiking frequency is quasi-linearly modulated by current injection. The time-constant of this integration process is practically infinite for input intensities above a threshold (the integration threshold) and can be still several hundred milliseconds long below the integration threshold. The somatic compartment received theta frequency stimulation in antiphase with the dendritic oscillation. Consequently, dendritic spikes could only elicit somatic action potentials when they were sufficiently phase-shifted and thus coincided with somatic depolarization. Somatic depolarization modulated the frequency but not the phase of firing, endowing the cell with the capability to code for two different variables at the same time. Inputs to the dendrite shifted the phase of dendritic spiking, while somatic input was modulating its firing rate. This mechanism resulted in firing patterns that closely matched experimental data from hippocampal place cells of freely behaving rats. We discuss the plausibility of our proposed mechanism and its potential to account for the firing pattern of cells outside the hippocampus during working memory tasks.

Computational theories on the function of theta oscillations.

Neural rhythms can be studied in terms of conditions for their generation, or in terms of their functional significance. The theta oscillation is a particularly prominent rhythm, reported to be present in many brain areas, and related to many important cognitive processes. The generating mechanisms of theta have extensively been studied and reviewed elsewhere; here we discuss ideas that have accumulated over the past decades on the computational roles it may subserve. Theories propose different aspects of theta oscillations as being relevant for their cognitive functions: limit cycle oscillations in neuronal firing rates, subthreshold membrane potential oscillations, periodic modulation of synaptic transmission and plasticity, and phase precession of hippocampal place cells. The relevant experimental data is briefly summarized in the light of these theories. Specific models proposing a function for theta in pattern recognition, memory, sequence learning and navigation are reviewed critically. Difficulties with testing and comparing alternative models are discussed, along with potentially important future research directions in the field.

Model-based source localization of extracellular action potentials.

A new model-based analysis method was set up for revealing information encrypted in extracellular spatial potential patterns of neocortical action potentials. Spikes were measured by extracellular linear multiple microelectrode in vivo cat's primary auditory cortex and were analyzed based on current source density (CSD) distribution models. Validity of the monopole and other point source approximations were tested on the measured potential patterns by numerical fitting. We have found, that point source models could not provide accurate description of the measured patterns. We introduced a new model of the CSD distribution on a spiking cell, called counter-current model (CCM). This new model was shown to provide better description of the spatial current distribution of the cell during the initial negative deflection of the extracellular action potential, from the onset of the spike to the negative peak. The new model was tested on simulated extracellular potentials. We proved numerically, that all the parameters of the model could be determined accurately based on measurements. Thus, fitting of the CCM allowed extraction of these parameters from the measurements. Due to model fitting, CSD could be calculated with much higher accuracy as done with the traditional method because distance dependence of the spatial potential patterns was explicitly taken into consideration in our method. Average CSD distribution of the neocortical action potentials was calculated and spatial decay constant of the dendritic trees was determined by applying our new method.

Theta-modulated feedforward network generates rate and phase coded firing in the entorhino-hippocampal system.

Principal cells of the hippocampus and of its only cortical input region, the entorhinal cortex exhibit place specific activity in the freely moving rat. While entorhinal cells have widely tuned place fields, hippocampal place fields are more localized and determine not only the rate but also the timing of place cell spikes. Several models have successfully attempted to explain this fine tuning making use of intrahippocampal attractor network dynamics provided by the recurrent collaterals of hippocampal area CA3. Recent experimental evidence shows that CA1 place cells preserve their tuning curves even in the absence of input from CA3. We propose a model in which entorhinal and hippocampal pyramidal cell populations are only connected via feedforward connections. Synaptic transmission in our sytem is gated by a class of interneurons inhibiting specifically the entorhino-hippocampal pathway. Theta rhythm modulates the activity of each component. Our results show that rhythmic shunting inhibition endows entorhinal cells with a novel type of temporal code conveyed by the phase jitter of individual spikes. This converts coarsely tuned place-specific activity in the entorhinal cortex to velocity-dependent postsynaptic excitation and, thus, provides hippocampal place cells with an input that has recently been proposed to account for their rate and phase coded firing. Hippocampal place fields are generated through this mechanism and also shown to be robust against variations in the level of tonic inhibition.