Neuromodulatory control of hippocampal function: towards a model of Alzheimer's disease

Formation and retrieval of cell assemblies in a biologically realistic spiking neural network model of area CA3 in the mouse hippocampus

The hippocampal formation is critical for episodic memory, with area Cornu Ammonis 3 (CA3) a necessary substrate for auto-associative pattern completion. Recent theoretical and experimental evidence suggests that the formation and retrieval of cell assemblies enable these functions. Yet, how cell assemblies are formed and retrieved in a full-scale spiking neural network (SNN) of CA3 that incorporates the observed diversity of neurons and connections within this circuit is not well understood. Here, we demonstrate that a data-driven SNN model quantitatively reflecting the neuron type-specific population sizes, intrinsic electrophysiology, connectivity statistics, synaptic signaling, and long-term plasticity of the mouse CA3 is capable of robust auto-association and pattern completion via cell assemblies. Our results show that a broad range of assembly sizes could successfully and systematically retrieve patterns from heavily incomplete or corrupted cues after a limited number of presentations. Furthermore, performance was robust with respect to partial overlap of assemblies through shared cells, substantially enhancing memory capacity. These novel findings provide computational evidence that the specific biological properties of the CA3 circuit produce an effective neural substrate for associative learning in the mammalian brain.

Firing Alterations of Neurons in Alzheimer's Disease: Are They Merely a Consequence of Pathogenesis or a Pivotal Component of Disease Progression?

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder, yet its underlying causes remain elusive. The conventional perspective on disease pathogenesis attributes alterations in neuronal excitability to molecular changes resulting in synaptic dysfunction. Early hyperexcitability is succeeded by a progressive cessation of electrical activity in neurons, with amyloid beta (Aβ) oligomers and tau protein hyperphosphorylation identified as the initial events leading to hyperactivity. In addition to these key proteins, voltage-gated sodium and potassium channels play a decisive role in the altered electrical properties of neurons in AD. Impaired synaptic function and reduced neuronal plasticity contribute to a vicious cycle, resulting in a reduction in the number of synapses and synaptic proteins, impacting their transportation inside the neuron. An understanding of these neurophysiological alterations, combined with abnormalities in the morphology of brain cells, emerges as a crucial avenue for new treatment investigations. This review aims to delve into the detailed exploration of electrical neuronal alterations observed in different AD models affecting single neurons and neuronal networks.

Neuroprosthetics: from sensorimotor to cognitive disorders

Neuroprosthetics is a multidisciplinary field at the interface between neurosciences and biomedical engineering, which aims at replacing or modulating parts of the nervous system that get disrupted in neurological disorders or after injury. Although neuroprostheses have steadily evolved over the past 60 years in the field of sensory and motor disorders, their application to higher-order cognitive functions is still at a relatively preliminary stage. Nevertheless, a recent series of proof-of-concept studies suggest that electrical neuromodulation strategies might also be useful in alleviating some cognitive and memory deficits, in particular in the context of dementia. Here, we review the evolution of neuroprosthetics from sensorimotor to cognitive disorders, highlighting important common principles such as the need for neuroprosthetic systems that enable multisite bidirectional interactions with the nervous system.

Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders

The investigation of synaptic functions remains one of the most fascinating challenges in the field of neuroscience and a large number of experimental methods have been tuned to dissect the mechanisms taking part in the neurotransmission process. Furthermore, the understanding of the insights of neurological disorders originating from alterations in neurotransmission often requires the development of (i) animal models of pathologies, (ii) invasive tools and (iii) targeted pharmacological approaches. In the last decades, additional tools to explore neurological diseases have been provided to the scientific community. A wide range of computational models in fact have been developed to explore the alterations of the mechanisms involved in neurotransmission following the emergence of neurological pathologies. Here, we review some of the advancements in the development of computational methods employed to investigate neuronal circuits with a particular focus on the application to the most diffuse neurological disorders.

Dysregulation of excitatory neural firing replicates physiological and functional changes in aging visual cortex

The mammalian visual system has been the focus of countless experimental and theoretical studies designed to elucidate principles of neural computation and sensory coding. Most theoretical work has focused on networks intended to reflect developing or mature neural circuitry, in both health and disease. Few computational studies have attempted to model changes that occur in neural circuitry as an organism ages non-pathologically. In this work we contribute to closing this gap, studying how physiological changes correlated with advanced age impact the computational performance of a spiking network model of primary visual cortex (V1). Our results demonstrate that deterioration of homeostatic regulation of excitatory firing, coupled with long-term synaptic plasticity, is a sufficient mechanism to reproduce features of observed physiological and functional changes in neural activity data, specifically declines in inhibition and in selectivity to oriented stimuli. This suggests a potential causality between dysregulation of neuron firing and age-induced changes in brain physiology and functional performance. While this does not rule out deeper underlying causes or other mechanisms that could give rise to these changes, our approach opens new avenues for exploring these underlying mechanisms in greater depth and making predictions for future experiments.

Alzheimer's Disease as a Result of Stimulus Reduction in a GABA-A-Deficient Brain: A Neurocomputational Model

Several research studies point to the fact that sensory and cognitive reductions like cataracts, deafness, macular degeneration, or even lack of activity after job retirement, precede the onset of Alzheimer's disease. To simulate Alzheimer's disease earlier stages, which manifest in sensory cortices, we used a computational model of the koniocortex that is the first cortical stage processing sensory information. The architecture and physiology of the modeled koniocortex resemble those of its cerebral counterpart being capable of continuous learning. This model allows one to analyze the initial phases of Alzheimer's disease by “aging” the artificial koniocortex through synaptic pruning, by the modification of acetylcholine and GABA-A signaling, and by reducing sensory stimuli, among other processes. The computational model shows that during aging, a GABA-A deficit followed by a reduction in sensory stimuli leads to a dysregulation of neural excitability, which in the biological brain is associated with hypermetabolism, one of the earliest symptoms of Alzheimer's disease.

Effects of Inducing Gamma Oscillations in Hippocampal Subregions DG, CA3, and CA1 on the Potential Alleviation of Alzheimer's Disease-Related Pathology: Computer Modeling and Simulations

The aim of this study was to evaluate the possibility of the gamma oscillation function (40–130 Hz) to reduce Alzheimer’s disease related pathology in a computer model of the hippocampal network dentate gyrus, CA3, and CA1 (DG-CA3-CA1) regions. Methods: Computer simulations were made for a pathological model in which Alzheimer’s disease was simulated by synaptic degradation in the hippocampus. Pathology modeling was based on sequentially turning off the connections with entorhinal cortex layer 2 (EC2) and the dentate gyrus on CA3 pyramidal neurons. Gamma induction modeling consisted of simulating the oscillation provided by the septo-hippocampal pathway with band frequencies from 40–130 Hz. Pathological models with and without gamma induction were compared with a control. Results: In the hippocampal regions of DG, CA3, and CA1, and jointly DG-CA3-CA1 and CA3-CA1, gamma induction resulted in a statistically significant improvement in terms of increased numbers of spikes, spikes per burst, and burst duration as compared with the model simulating Alzheimer’s disease (AD). The positive maximal Lyapunov exponent was negative in both the control model and the one with gamma induction as opposed to the pathological model where it was positive within the DG-CA3-CA1 region. Gamma induction resulted in decreased transfer entropy in accordance with the information flow in DG → CA3 and CA3 → CA1. Conclusions: The results of simulation studies show that inducing gamma oscillations in the hippocampus may reduce Alzheimer’s disease related pathology. Pathologically higher transfer entropy values after gamma induction returned to values comparable to the control model.

Computer Modeling of Alzheimer's Disease-Simulations of Synaptic Plasticity and Memory in the CA3-CA1 Hippocampal Formation Microcircuit

This paper aims to present computer modeling of synaptic plasticity and memory in the CA3-CA1 hippocampal formation microcircuit. The computer simulations showed a comparison of a pathological model in which Alzheimer's disease (AD) was simulated by synaptic degradation in the hippocampus and control model (healthy) of CA3-CA1 networks with modification of weights for the memory. There were statistically higher spike values of both CA1 and CA3 pyramidal cells in the control model than in the pathological model (p = 0.0042 for CA1 and p = 0.0033 for CA3). A similar outcome was achieved for frequency (p = 0.0002 for CA1 and p = 0.0001 for CA3). The entropy of pyramidal cells of the healthy CA3 network seemed to be significantly higher than that of AD (p = 0.0304). We need to study a lot of physiological parameters and their combinations of the CA3-CA1 hippocampal formation microcircuit to understand AD. High statistically correlations were obtained between memory, spikes and synaptic deletion in both CA1 and CA3 cells.

Computer Model of Synapse Loss During an Alzheimer's Disease-Like Pathology in Hippocampal Subregions DG, CA3 and CA1-The Way to Chaos and Information Transfer

The aim of the study was to compare the computer model of synaptic breakdown in an Alzheimer’s disease-like pathology in the dentate gyrus (DG), CA3 and CA1 regions of the hippocampus with a control model using neuronal parameters and methods describing the complexity of the system, such as the correlative dimension, Shannon entropy and positive maximal Lyapunov exponent. The model of synaptic breakdown (from 13% to 50%) in the hippocampus modeling the dynamics of an Alzheimer’s disease-like pathology was simulated. Modeling consisted in turning off one after the other EC2 connections and connections from the dentate gyrus on the CA3 pyramidal neurons. The pathological model of synaptic disintegration was compared to a control. The larger synaptic breakdown was associated with a statistically significant decrease in the number of spikes (R = −0.79, P < 0.001), spikes per burst (R = −0.76, P < 0.001) and burst duration (R = −0.83, P < 0.001) and an increase in the inter-burst interval (R = 0.85, P < 0.001) in DG-CA3-CA1. The positive maximal Lyapunov exponent in the control model was negative, but in the pathological model had a positive value of DG-CA3-CA1. A statistically significant decrease of Shannon entropy with the direction of information flow DG->CA3->CA1 (R = −0.79, P < 0.001) in the pathological model and a statistically significant increase with greater synaptic breakdown (R = 0.24, P < 0.05) of the CA3-CA1 region was obtained. The reduction of entropy transfer for DG->CA3 at the level of synaptic breakdown of 35% was 35%, compared with the control. Entropy transfer for CA3->CA1 at the level of synaptic breakdown of 35% increased to 95% relative to the control. The synaptic breakdown model in an Alzheimer’s disease-like pathology in DG-CA3-CA1 exhibits chaotic features as opposed to the control. Synaptic breakdown in which an increase of Shannon entropy is observed indicates an irreversible process of Alzheimer’s disease. The increase in synapse loss resulted in decreased information flow and entropy transfer in DG->CA3, and at the same time a strong increase in CA3->CA1.

Neural Coding With Bursts-Current State and Future Perspectives

Neuronal action potentials or spikes provide a long-range, noise-resistant means of communication between neurons. As point processes single spikes contain little information in themselves, i.e., outside the context of spikes from other neurons. Moreover, they may fail to cross a synapse. A burst, which consists of a short, high frequency train of spikes, will more reliably cross a synapse, increasing the likelihood of eliciting a postsynaptic spike, depending on the specific short-term plasticity at that synapse. Both the number and the temporal pattern of spikes in a burst provide a coding space that lies within the temporal integration realm of single neurons. Bursts have been observed in many species, including the non-mammalian, and in brain regions that range from subcortical to cortical. Despite their widespread presence and potential relevance, the uncertainties of how to classify bursts seems to have limited the research into the coding possibilities for bursts. The present series of research articles provides new insights into the relevance and interpretation of bursts across different neural circuits, and new methods for their analysis. Here, we provide a succinct introduction to the history of burst coding and an overview of recent work on this topic.

Computational modeling and biomarker studies of pharmacological treatment of Alzheimer's disease (Review)

Alzheimer's disease (AD) is a complex and multifactorial disease. In order to understand the genetic influence in the progression of AD, and to identify novel pharmaceutical agents and their associated targets, the present study discusses computational modeling and biomarker evaluation approaches. Based on mechanistic signaling pathway approaches, various computational models, including biochemical and morphological models, are discussed to explore the strategies that may be used to target AD treatment. Different biomarkers are interpreted on the basis of morphological and functional features of amyloid β plaques and unstable microtubule‑associated tau protein, which is involved in neurodegeneration. Furthermore, imaging and cerebrospinal fluids are also considered to be key methods in the identification of novel markers for AD. In conclusion, the present study reviews various biochemical and morphological computational models and biomarkers to interpret novel targets and agonists for the treatment of AD. This review also highlights several therapeutic targets and their associated signaling pathways in AD, which may have potential to be used in the development of novel pharmacological agents for the treatment of patients with AD. Computational modeling approaches may aid the quest for the development of AD treatments with enhanced therapeutic efficacy and reduced toxicity.

An in Silico, Biomarker-Based Method for the Evaluation of Virtual Neuropsychiatric Drug Effects

The recent explosion in neuroscience research has markedly increased our understanding of the neurobiological correlates of many psychiatric illnesses, but this has unfortunately not translated into more effective pharmacologic treatments for these conditions. At the same time, researchers have increasingly sought out biological markers, or biomarkers, as a way to categorize psychiatric illness, as these are felt to be closer to underlying genetic and neurobiological vulnerabilities. While biomarker-based drug discovery approaches have tended to employ in vivo (e.g., rodent) or in vitro test systems, relatively little attention has been paid to the potential of computational, or in silico, methodologies. Here we describe such a methodology, using as an example a biophysically detailed computational model of hippocampus that is made to generate putative schizophrenia biomarkers by the inclusion of a number of neuropathological changes that have been associated with the illness (NMDA system deficit, decreased neural connectivity, hyperdopaminergia). We use the specific inability to attune to gamma band (40 Hz) auditory stimulus as our illness biomarker. We expose this system to a large number of virtual medications, defined by systematic variation of model parameters corresponding to five cellular-level effects. The potential efficacy of virtual medications is determined by a wellness metric (WM) that we have developed. We identify a number of virtual agents that consist of combinations of mechanisms, which are not simply reversals of the causative lesions. The manner in which this methodology could be extended to other neuropsychiatric conditions, such as Alzheimer's disease, autism, and fragile X syndrome, is discussed.

Effects of increasing CREB-dependent transcription on the storage and recall processes in a hippocampal CA1 microcircuit

The involvement of the hippocampus in learning processes and major brain diseases makes it an ideal candidate to investigate possible ways to devise effective therapies for memory-related pathologies like Alzheimer's Disease (AD). It has been previously reported that augmenting CREB activity increases the synaptic Long-Term Potentiation (LTP) magnitude in CA1 pyramidal neurons and their intrinsic excitability in healthy rodents. It has also been suggested that hippocampal CREB signaling is likely to be down-regulated during AD, possibly degrading memory functions. Therefore, the concept of CREB-based memory enhancers, i.e. drugs that would boost memory by activation of CREB, has emerged. Here, using a model of a CA1 microcircuit, we investigate whether hippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach to treat AD.

The effect of neural noise on spike time precision in a detailed CA3 neuron model

Experimental and computational studies emphasize the role of the millisecond precision of neuronal spike times as an important coding mechanism for transmitting and representing information in the central nervous system. We investigate the spike time precision of a multicompartmental pyramidal neuron model of the CA3 region of the hippocampus under the influence of various sources of neuronal noise. We describe differences in the contribution to noise originating from voltage-gated ion channels, synaptic vesicle release, and vesicle quantal size. We analyze the effect of interspike intervals and the voltage course preceding the firing of spikes on the spike-timing jitter. The main finding of this study is the ranking of different noise sources according to their contribution to spike time precision. The most influential is synaptic vesicle release noise, causing the spike jitter to vary from 1 ms to 7 ms of a mean value 2.5 ms. Of second importance was the noise incurred by vesicle quantal size variation causing the spike time jitter to vary from 0.03 ms to 0.6 ms. Least influential was the voltage-gated channel noise generating spike jitter from 0.02 ms to 0.15 ms.

Opioid system and Alzheimer's disease

The opioid system may be involved in the pathogenesis of AD, including cognitive impairment, hyperphosphorylated tau, Aβ production, and neuroinflammation. Opioid receptors influence the regulation of neurotransmitters such as acetylcholine, norepinephrine, GABA, glutamate, and serotonin which have been implicated in the pathogenesis of AD. Opioid system has a close relation with Aβ generation since dysfunction of opioid receptors retards the endocytosis and degradation of BACE1 and γ-secretase and upregulates BACE1 and γ-secretase, and subsequently, the production of Aβ. Conversely, activation of opioid receptors increases the endocytosis of BACE1 and γ-secretase and downregulates BACE1 and γ-secretase, limiting the production of Aβ. The dysfunction of opioid system (opioid receptors and opioid peptides) may contribute to hyperphosphorylation of tau and neuroinflammation, and accounts for the degeneration of cholinergic neurons and cognitive impairment. Thus, the opioid system is potentially related to AD pathology and may be a very attractive drug target for novel pharmacotherapies of AD.

Fast network oscillations in vitro exhibit a slow decay of temporal auto-correlations

Ongoing neuronal oscillations in vivo exhibit non-random amplitude fluctuations as reflected in a slow decay of temporal auto-correlations that persist for tens of seconds. Interestingly, the decay of auto-correlations is altered in several brain-related disorders, including epilepsy, depression and Alzheimer's disease, suggesting that the temporal structure of oscillations depends on intact neuronal networks in the brain. Whether structured amplitude modulation occurs only in the intact brain or whether isolated neuronal networks can also give rise to amplitude modulation with a slow decay is not known. Here, we examined the temporal structure of cholinergic fast network oscillations in acute hippocampal slices. For the first time, we show that a slow decay of temporal correlations can emerge from synchronized activity in isolated hippocampal networks from mice, and is maximal at intermediate concentrations of the cholinergic agonist carbachol. Using zolpidem, a positive allosteric modulator of GABA(A) receptor function, we found that increased inhibition leads to longer oscillation bursts and more persistent temporal correlations. In addition, we asked if these findings were unique for mouse hippocampus, and we therefore analysed cholinergic fast network oscillations in rat prefrontal cortex slices. We observed significant temporal correlations, which were similar in strength to those found in mouse hippocampus and human cortex. Taken together, our data indicate that fast network oscillations with temporal correlations can be induced in isolated networks in vitro in different species and brain areas, and therefore may serve as model systems to investigate how altered temporal correlations in disease may be rescued with pharmacology.

Hippocampus, microcircuits and associative memory

The hippocampus is one of the most widely studied brain region. One of its functional roles is the storage and recall of declarative memories. Recent hippocampus research has yielded a wealth of data on network architecture, cell types, the anatomy and membrane properties of pyramidal cells and interneurons, and synaptic plasticity. Understanding the functional roles of different families of hippocampal neurons in information processing, synaptic plasticity and network oscillations poses a great challenge but also promises deep insight into one of the major brain systems. Computational and mathematical models play an instrumental role in exploring such functions. In this paper, we provide an overview of abstract and biophysical models of associative memory with particular emphasis on the operations performed by the diverse (inter)neurons in encoding and retrieval of memories in the hippocampus.

Evidence of multistability in a realistic computer simulation of hippocampus subfield CA1

The manner in which hippocampus processes neural signals is thought to be central to the memory encoding process. A theoretically oriented literature has suggested that this is carried out via "attractors" or distinctive spatio-temporal patterns of activity. However, these ideas have not been thoroughly investigated using computational models featuring both realistic single-cell physiology and detailed cell-to-cell connectivity. Here we present a 452 cell simulation based on Traub et al.'s pyramidal cell [Traub RD, Jefferys JG, Miles R, Whittington MA, Toth K. A branching dendritic model of a rodent CA3 pyramidal neurone. J Physiol (Lond) 1994;481:79-95] and interneuron [Traub RD, Miles R, Pyramidal cell-to-inhibitory cell spike transduction explicable by active dendritic conductances in inhibitory cell. J Comput Neurosci 1995;2:291-8] models, incorporating patterns of synaptic connectivity based on an extensive review of the neuroanatomic literature. When stimulated with a one second physiologically realistic input, our simulated tissue shows the ability to hold activity on-line for several seconds; furthermore, its spiking activity, as measured by frequency and interspike interval (ISI) distributions, resembles that of in vivo hippocampus. An interesting emergent property of the system is its tendency to transition from stable state to stable state, a behavior consistent with recent experimental findings [Sasaki T, Matsuki N, Ikegaya Y. Metastability of active CA3 networks. J Neurosci 2007;27:517-28]. Inspection of spike trains and simulated blockade of K(AHP) channels suggest that this is mediated by spike frequency adaptation. This finding, in conjunction with studies showing that apamin, a K(AHP) channel blocker, enhances the memory consolidation process in laboratory animals, suggests the formation of stable attractor states is central to the process by which memories are encoded. Ways that this methodology could shed light on the etiology of mental illness, such as schizophrenia, are discussed.

Distinct classes of pyramidal cells exhibit mutually exclusive firing patterns in hippocampal area CA3b

It is thought that CA3 pyramidal neurons communicate mainly through bursts of spikes rather than so-called trains of regular firing action potentials. Reports of both burst firing and nonburst firing CA3 cells suggest that they may fire with more than one output pattern. With the use of whole-cell recording methods we studied the firing properties of rat hippocampal pyramidal neurons in vitro within the CA3b subregion and found three distinct types of firing patterns. Approximately 37% of cells were regular firing where spikes generated by minimal current injection (rheobase) were elicited with a short latency and with stronger current intensities trains of spikes exhibited spike frequency adaptation (SFA). Another 46% of neurons exhibited a delayed onset at rheobase with a weakly-adapting firing pattern upon stronger stimulation. The remaining 17% of cells showed a burst-firing pattern, though only elicited in response to strong current injection and spontaneous bursts were never observed. Control experiments indicated that the distinct firing patterns were not due to our particular slicing methods or recording techniques. Finally, computer modeling was used to identify how relative differences in K+ conductances, specifically K(C), K(M), and K(D), between cells contribute to the different characteristics of the three types of firing patterns observed experimentally.

Knowledge-based localization of hippocampus in human brain MRI

We present a novel and efficient method for localization of human brain structures such as hippocampus. Landmark localization is important for segmentation and registration. This method follows a statistical roadmap, consisting of anatomical landmarks, to reach the desired structures. Using a set of desired and undesired landmarks, identified on a training set, we estimate Gaussian models and determine optimal search areas for desired landmarks. The statistical models form a set of rules to evaluate the extracted landmarks during the search procedure. When applied on 900 MR images of 10 epileptic patients, this method demonstrated an overall success rate of 83%.

Computational models of dementia and neurological problems

A critical goal of neuroscience is to fully understand neural processes and their relations to mental processes, and cognitive, affective, and behavioral disorders. Computational modeling, although still in its infancy, continues to play a central role in this endeavor. Presented here is a review of different aspects of computational modeling that help to explain many features of neuropsychological syndromes and psychiatric disease. Recent advances in computational modeling of epilepsy, cortical reorganization after lesions, Parkinson's and Alzheimer diseases are also reviewed. Additionally, this chapter will also identify some trends in the computational modeling of brain functions.

Facilitatory effect of aspirin on glutamate release from rat hippocampal nerve terminals: involvement of protein kinase C pathway

The effect of aspirin on glutamate release from isolated nerve terminals (synaptosomes) from rat hippocampus was examined. The Ca(2+)-dependent release of glutamate evoked by 4-aminopyridine (4AP) was facilitated by aspirin in a concentration-dependent manner, but the 4AP-evoked Ca(2+)-independent release was not modified. Also, aspirin-mediated facilitation of glutamate release was completely inhibited by bafilomycin A1, which depletes vesicle content by inhibiting the synaptic vesicle H(+)-ATPase that drives glutamate uptake, not by l-trans-pyrrolidine-2,4-dicarboxylic acid (l-trans-PDC), a excitatory amino acid (EAA) transporter inhibitor, suggesting that the facilitation of glutamate release produced by aspirin originates from synaptic vesicle exocytosis rather than reversal of the plasma membrane glutamate transporter. In addition, aspirin did not alter either 4AP-evoked depolarization of the synaptosomal plasma membrane potential or Ca(2+) ionophore ionomycin-induced glutamate release, but significantly increased in 4AP-evoked Ca(2+) influx. A possible effect of aspirin on synaptosomal Ca(2+) channels was confirmed in experiments where synaptosomes pretreated with a combination of the N- and P/Q-type Ca(2+) channel blockers, which abolished the aspirin-mediated facilitation of glutamate release. The facilitatory action by aspirin observed in glutamate release was mimicked and occluded by arachidonic acid (AA) and eicosatetraynoic acid (ETYA), an analogue of AA that mimics the effect of AA but cannot be metabolized. Furthermore, this aspirin-mediated facilitation of glutamate release may depend on activation of protein kinase C (PKC), because PKC activator and PKC inhibitor, respectively, superseding or suppressing the facilitatory effect of aspirin. Together, these results suggest that aspirin exerts their presynaptic facilitatory effect, likely through AA directly to induce the activation of PKC, which subsequently enhances the Ca(2+) influx through voltage-dependent N- and P/Q-type Ca(2+) channels to cause an increase in evoked glutamate release from rat hippocampal nerve terminals.

Ginkgolide B, a constituent of Ginkgo biloba, facilitates glutamate exocytosis from rat hippocampal nerve terminals

Although previous studies have demonstrated that Ginkgo biloba extract has modest effects in the improvement of memory and cognitive function of the Alzheimer's disease patients, the mechanism(s) underlying its beneficial effects remain(s) unclear. In this study, the effect of ginkgolide B, one of the major constituents of Ginkgo biloba extract, on the release of endogenous glutamate from rat hippocampal nerve terminals (synaptosomes) was studied. Ginkgolide B facilitated the Ca2+-dependent release of glutamate evoked by 4-aminopyridine in a concentration-dependent manner. The facilitatory action of ginkgolide B was not due to it increasing synaptosomal excitability because ginkgolide B did not alter the 4-aminopyridine-evoked depolarization of the synaptosomal plasma membrane potential. Rather, examination of the effect of ginkgolide B on cytosolic free Ca2+ concentration revealed that the facilitation of glutamate release could be attributed to an enhancement of presynaptic voltage-dependent Ca2+ influx. Consistent with this, the ginkgolide B-mediated facilitation of glutamate release was significantly prevented in synaptosomes pretreated with a wide spectrum blocker of N-, P-, and Q-type Ca2+ channels, omega-conotoxin MVIIC. Moreover, the facilitation produced by ginkgolide B was completely abolished by the protein kinase A inhibitor, but not by the protein kinase C inhibitor. These results suggest that ginkgolide B effects a increase in protein kinase A activation, which subsequently enhances the Ca2+ entry through voltage-dependent N- and P/Q-type Ca2+ channels to cause a increase in evoked glutamate release from rat hippocampal nerve terminals. In addition, glutamate release elicited by Ca2+ ionophore (ionomycin) was also facilitated by ginkgolide B, which suggests that ginkgolide B may have a direct effect on the secretory apparatus downstream of Ca2+ entry. These actions of ginkgolide B may provide some information regarding the beneficial effects of Ginkgo biloba in the central nervous system.

Effects of dendritic morphology on CA3 pyramidal cell electrophysiology: a simulation study

We investigated the effect of morphological differences on neuronal firing behavior within the hippocampal CA3 pyramidal cell family by using three-dimensional reconstructions of dendritic morphology in computational simulations of electrophysiology. In this paper, we report for the first time that differences in dendritic structure within the same morphological class can have a dramatic influence on the firing rate and firing mode (spiking versus bursting and type of bursting). Our method consisted of converting morphological measurements from three-dimensional neuroanatomical data of CA3 pyramidal cells into a computational simulator format. In the simulation, active channels were distributed evenly across the cells so that the electrophysiological differences observed in the neurons would only be due to morphological differences. We found that differences in the size of the dendritic tree of CA3 pyramidal cells had a significant qualitative and quantitative effect on the electrophysiological response. Cells with larger dendritic trees: (1) had a lower burst rate, but a higher spike rate within a burst, (2) had higher thresholds for transitions from quiescent to bursting and from bursting to regular spiking and (3) tended to burst with a plateau. Dendritic tree size alone did not account for all the differences in electrophysiological responses. Differences in apical branching, such as the distribution of branch points and terminations per branch order, appear to effect the duration of a burst. These results highlight the importance of considering the contribution of morphology in electrophysiological and simulation studies.

Computational model of carbachol-induced delta, theta, and gamma oscillations in the hippocampus

Field potential recordings from the rat hippocampus in vivo contain distinct frequency bands of activity, including delta (0.5-2 Hz), theta (4-12 Hz), and gamma (30-80 Hz), that are correlated with the behavioral state of the animal. The cholinergic agonist carbachol (CCH) induces oscillations in the delta (CCH-delta), theta (CCH-theta), and gamma (CCH-gamma) frequency ranges in the hippocampal slice preparation, eliciting asynchronous CCH-theta, synchronous CCH-delta, and synchronous CCH-theta with increasing CCH concentration (Fellous and Seinowski, Hippocampus 2000;1 0:187-197). In a network model of area CA3, the time scale for CCH-delta corresponded to the decay constant of the gating variable of the calcium-dependent potassium (K-AHP) current, that of CCH-theta to an intrinsic subthreshold membrane potential oscillation of the pyramidal cells, and that of CCH-gamma to the decay constant of GABAergic inhibitory synaptic potentials onto the pyramidal cells. In model simulations, the known physiological effects of carbachol on the muscarinic and K-AHP currents, and on the strengths of excitatory postsynaptic potentials, reproduced transitions from asynchronous CCH-theta to CCH-delta and from CCH-delta to synchronous CCH-theta. The simulations also exhibited the interspersed CCH-gamma/CCH-delta and CCH-gamma/CCH-theta that were observed in experiments. The model, in addition, predicted an oscillatory state with all three frequency bands present, which has not yet been observed experimentally.

The nicotinic allosteric potentiating ligand galantamine facilitates synaptic transmission in the mammalian central nervous system

In this study, the patch-clamp technique was used to determine the effects of galantamine, a cholinesterase inhibitor and a nicotinic allosteric potentiating ligand (APL) used for treatment of Alzheimer's disease, on synaptic transmission in brain slices. In rat hippocampal and human cerebral cortical slices, 1 microM galantamine, acting as a nicotinic APL, increased gamma-aminobutyric acid (GABA) release triggered by 10 microM acetylcholine (ACh). Likewise, 1 microM galantamine, acting as an APL on presynaptically located nicotinic receptors (nAChRs) that are tonically active, potentiated glutamatergic or GABA-ergic transmission between Schaffer collaterals and CA1 neurons in rat hippocampal slices. The cholinesterase inhibitors rivastigmine, donepezil, and metrifonate, which are devoid of nicotinic APL action, did not affect synaptic transmission. Exogenous application of ACh indicated that high and low levels of nAChR activation in the Schaffer collaterals inhibit and facilitate, respectively, glutamate release onto CA1 neurons. The finding then that the nAChR antagonists methyllycaconitine and dihydro-beta-erythroidine facilitated glutamatergic transmission between Schaffer collaterals and CA1 neurons indicated that in a single hippocampal slice, the inhibitory action of strongly, tonically activated nAChRs in some glutamatergic fibers prevails over the facilitatory action of weakly, tonically activated nAChRs in other glutamatergic fibers synapsing onto a given neuron. Galantamine is known to sensitize nAChRs to activation by low, but not high agonist concentrations. Therefore, at 1 microM, galantamine is likely to increase facilitation of synaptic transmission by weakly, tonically activated nAChRs just enough to override inhibition by strongly, tonically activated nAChRs. In conclusion, the nicotinic APL action can be an important determinant of the therapeutic effectiveness of galantamine.

Simulations of the role of the muscarinic-activated calcium-sensitive nonspecific cation current INCM in entorhinal neuronal activity during delayed matching tasks

Entorhinal lesions impair performance in delayed matching tasks, and blockade of muscarinic cholinergic receptors also impairs performance in these tasks. Physiological data demonstrate that muscarinic cholinergic receptor stimulation activates intrinsic cellular currents in entorhinal neurons that could underlie the role of entorhinal cortex in performance of these tasks. Here we use a network biophysical simulation of the entorhinal cortex to demonstrate the potential role of this cellular mechanism in the behavioral tasks. Simulations demonstrate how the muscarinic-activated calcium-sensitive nonspecific cation current I(NCM) could provide a cellular mechanism for features of the neuronal activity observed during performance of delayed matching tasks. In particular, I(NCM) could underlie (1) the maintenance of sustained spiking activity during the delay period, (2) the enhancement of spiking activity during the matching period relative to the sample period, and (3) the resistance of sustained activity to distractors. Simulation of a larger entorhinal network with connectivity chosen randomly within constraints on number, distribution, and weight demonstrates appearance of other phenomena observed in unit recordings from awake animals, including match suppression, non-match enhancement, and non-match suppression.

Neuroengineering models of brain disease

The techniques of computational simulation have begun to be applied to modeling neurological disease and mental illness. Such neuroengineering models provide a conceptual bridge between molecular/cellular pathology and cognitive performance. We consider models of Alzheimer's disease, Parkinson's disease, and schizophrenia. Each of these diseases involves a disorder of neuromodulation coupled with underlying neuronal pathology. Parallels arising between these models suggests that a common set of computational mechanisms may account for functional loss across a spectrum of brain diseases. In particular, we focus on attractor-based network dynamics and how they arise from neural architectures, on mechanisms for linking sequences of attractor states and their role in cognition, and on the role of neuromodulation in controlling these processes. These studies suggest new approaches to understanding the forebrain circuits underlying cognition, and point toward a new tool for dissecting the pathophysiology of brain disease.

Nicotinic cholinergic modulation: galantamine as a prototype

Nicotinic acetylcholine receptor pharmacology is becoming increasingly important in the clinical symptomatology of neurodegenerative diseases in general and of cognitive and behavioral aspects in particular. In addition, the concept of allosteric modulation of nicotinic acetylcholine receptors has become a research focus for the development of therapeutic agents. In this review the scientific evidence for changes in nicotinic acetylcholine receptors in Alzheimer's disease is described. Within this context, the pharmacology of galantamine, a recently approved drug for cognition enhancement in Alzheimer's disease, is reviewed along with preclinical studies of its efficacy on learning and memory. Galantamine modestly inhibits acetylcholinesterase and has an allosteric potentiating ligand effect at nicotinic receptors. The data collected in this review suggest that the unique combination of acetylcholinesterase inhibition and nicotinic acetylcholine receptor modulation offers potentially significant benefits over acetylcholinesterase inhibition alone in facilitating acetylcholine neurotransmission.

Effect of local synaptic strengthening on global activity-wave growth in the hippocampus

Analysis of a continuum model of the hippocampus shows that strengthening of the synaptic connections within localized regions can convert the global activity-wave properties from decay to growth. These growing waves can play a part in the implantation of long-term memory in the higher brain regions. The wavelength of the fastest-growing mode decreases with increasing local synaptic strength and can be modified by the chemical state as reflected by synaptic sensitivity to stimuli. The temporal period of the response is a constant, equal to twice the delay time exhibited by some of the hippocampal inhibitory neurons ("d-cells"). The value for the period obtained from this relationship and measurement of the delay time agrees with the hippocampal gamma rhythm. For normal hippocampal function the proportion of d-cells is limited to one-third the total number of inhibitory neurons.

Associative memory in networks of spiking neurons

Here, we develop and investigate a computational model of a network of cortical neurons on the base of biophysically well constrained and tested two-compartmental neurons developed by Pinsky and Rinzel [Pinsky, P. F., & Rinzel, J. (1994). Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons. Journal of Computational Neuroscience, 1, 39-60]. To study associative memory, we connect a pool of cells by a structured connectivity matrix. The connection weights are shaped by simple Hebbian coincidence learning using a set of spatially sparse patterns. We study the neuronal activity processes following an external stimulation of a stored memory. In two series of simulation experiments, we explore the effect of different classes of external input, tonic and flashed stimulation. With tonic stimulation, the addressed memory is an attractor of the network dynamics. The memory is displayed rhythmically, coded by phase-locked bursts or regular spikes. The participating neurons have rhythmic activity in the gamma-frequency range (30-80 Hz). If the input is switched from one memory to another, the network activity can follow this change within one or two gamma cycles. Unlike similar models in the literature, we studied the range of high memory capacity (in the order of 0.1 bit/synapse), comparable to optimally tuned formal associative networks. We explored the robustness of efficient retrieval varying the memory load, the excitation/inhibition parameters, and background activity. A stimulation pulse applied to the identical simulation network can push away ongoing network activity and trigger a phase-locked association event within one gamma period. Unlike as under tonic stimulation, the memories are not attractors. After one association process, the network activity moves to other states. Applying in close succession pulses addressing different memories, one can switch through the space of memory patterns. The readout speed can be increased up to the point where in every gamma cycle another pattern is displayed. With pulsed stimulation. bursts become relevant for coding, their occurrence can be used to discriminate relevant processes from background activity.

Replay and time compression of recurring spike sequences in the hippocampus

Information in neuronal networks may be represented by the spatiotemporal patterns of spikes. Here we examined the temporal coordination of pyramidal cell spikes in the rat hippocampus during slow-wave sleep. In addition, rats were trained to run in a defined position in space (running wheel) to activate a selected group of pyramidal cells. A template-matching method and a joint probability map method were used for sequence search. Repeating spike sequences in excess of chance occurrence were examined by comparing the number of repeating sequences in the original spike trains and in surrogate trains after Monte Carlo shuffling of the spikes. Four different shuffling procedures were used to control for the population dynamics of hippocampal neurons. Repeating spike sequences in the recorded cell assemblies were present in both the awake and sleeping animal in excess of what might be predicted by random variations. Spike sequences observed during wheel running were "replayed" at a faster timescale during single sharp-wave bursts of slow-wave sleep. We hypothesize that the endogenously expressed spike sequences during sleep reflect reactivation of the circuitry modified by previous experience. Reactivation of acquired sequences may serve to consolidate information.

Neural models of memory

Neural models assist in characterizing the processes carried out by cortical and hippocampal memory circuits. Recent models of memory have addressed issues including recognition and recall dynamics, sequences of activity as the unit of storage, and consolidation of intermediate-term episodic memory into long-term memory.

Adapting a feedforward heteroassociative network to Hodgkin-Huxley dynamics

Using the original McCulloch-Pitts notion of simple on and off spike coding in lieu of rate coding, an Anderson-Kohonen artificial neural network (ANN) associative memory model was ported to a neuronal network with Hodgkin-Huxley dynamics. In the ANN, the use of 0/1 (no-spike/spike) units introduced a cross-talk term that had to be compensated by introducing balanced feedforward inhibition. The resulting ANN showed good capacity and fair selectivity (rejection of unknown input vectors). Translation to the Hodgkin-Huxley model resulted in a network that was functional but not at all robust. Evaluation of the weaknesses of this network revealed that it functioned far better using spike timing, rather than spike occurrence, as the code. The algorithm requires a novel learning algorithm for feedforward inhibition that could be sought physiologically.