Hierarchical model of the population dynamics of hippocampal dentate granule cells

Lack of β-amyloid cleaving enzyme-1 (BACE1) impairs long-term synaptic plasticity but enhances granule cell excitability and oscillatory activity in the dentate gyrus in vivo

BACE1 is a β-secretase involved in the cleavage of amyloid precursor protein and the pathogenesis of Alzheimer's disease (AD). The entorhinal cortex and the dentate gyrus are important for learning and memory, which are affected in the early stages of AD. Since BACE1 is a potential target for AD therapy, it is crucial to understand its physiological role in these brain regions. Here, we examined the function of BACE1 in the dentate gyrus. We show that loss of BACE1 in the dentate gyrus leads to increased granule cell excitability, indicated by enhanced efficiency of synaptic potentials to generate granule cell spikes. The increase in granule cell excitability was accompanied by prolonged paired-pulse inhibition, altered network gamma oscillations, and impaired synaptic plasticity at entorhinal-dentate synapses of the perforant path. In summary, this is the first detailed electrophysiological study of BACE1 deletion at the network level in vivo. The results suggest that BACE1 is important for normal dentate gyrus network function. This has implications for the use of BACE1 inhibitors as therapeutics for AD therapy, since BACE1 inhibition could similarly disrupt synaptic plasticity and excitability in the entorhinal-dentate circuitry.

Deletion of the amyloid precursor-like protein 1 (APLP1) enhances excitatory synaptic transmission, reduces network inhibition but does not impair synaptic plasticity in the mouse dentate gyrus

Amyloid precursor-like protein 1 (APLP1) is a transmembrane synaptic protein belonging to the amyloid precursor protein (APP) gene family. Although the role of this gene family-in particular of APP-has been intensely studied in the context of Alzheimer's disease, the physiological roles of its family members remain poorly understood. In particular, the function of APLP1, which is predominantly expressed in the nervous system, has remained enigmatic. Since APP has been implicated in synaptic plasticity, we wondered whether APLP1 could play a similar role. First, using in situ hybridization and laser microdissection combined with reverse transcription-quantitative polymerase chain reaction (PCR) we observed that Aplp1 mRNA is highly expressed in dentate granule cells. Having this examined, we studied synaptic plasticity at the perforant path-granule cell synapses in the dentate gyrus of APLP1-deficient mice in vivo. Analysis of field excitatory postsynaptic potentials evoked by stimulation of perforant path fibers revealed increased excitatory transmission in APLP1-deficient mice. Moreover, we observed decreased paired-pulse inhibition of population spikes indicating a decrease in network inhibition upon deletion of APLP1. In contrast, short-term presynaptic plasticity (STP) as well as long-term synaptic plasticity (LTP) was unchanged in the absence of APLP1. Based on these results we conclude that APLP1 deficiency on its own does not lead to defects in synaptic plasticity, but affects synaptic transmission and network inhibition in the dentate gyrus.

Aberrant dendritic excitability: a common pathophysiology in CNS disorders affecting memory?

Discovering the etiology of pathophysiologies and aberrant behavior in many central nervous system (CNS) disorders has proven elusive because susceptibility to these diseases can be a product of multiple factors such as genetics, epigenetics, and environment. Advances in molecular biology and wide-scale genomics have shown that a large heterogeneity of genetic mutations are potentially responsible for the neuronal pathologies and dysfunctional behaviors seen in CNS disorders. Despite this seemingly complex array of genetic and physiological factors, many disorders of the CNS converge on common dysfunctions in memory. In this review, we propose that mechanisms underlying the development of many CNS disorders may share an underlying cause involving abnormal dendritic integration of synaptic signals. Through understanding the relationship between molecular genetics and dendritic computation, future research may uncover important links between neuronal physiology at the cellular level and higher-order circuit and network abnormalities observed in CNS disorders, and their subsequent affect on memory.

Functional consequences of the lack of amyloid precursor protein in the mouse dentate gyrus in vivo

The amyloid precursor protein (APP) plays a crucial role in the pathogenesis of Alzheimer's disease. Here, we studied whether the lack of APP affects the synaptic properties in the dentate gyrus by measuring granule cell field potentials evoked by perforant path stimulation in anesthetized 9-11-month-old APP-deficient mice in vivo. We found decreased paired-pulse facilitation, indicating altered presynaptic short-term plasticity in the APP-deficient dentate gyrus. In contrast, excitatory synaptic strength and granule cell firing were unchanged in APP knockout mice. Likewise, long-term potentiation (LTP) induced by a theta-burst stimulation protocol was not impaired in the absence of APP. These findings suggest that the deletion of APP may affect presynaptic plasticity of synaptic transmission at the perforant path-granule cell synapse but leaves synaptic efficacy intact and LTP preserved, possibly due to functional redundancy within the APP gene family.

Integrated multiscale modeling of the nervous system: predicting changes in hippocampal network activity by a positive AMPA receptor modulator

One of the fundamental characteristics of the brain is its hierarchical organization. Scales in both space and time that must be considered when integrating across hierarchies of the nervous system are sufficiently great as to have impeded the development of routine multilevel modeling methodologies. Complex molecular interactions at the level of receptors and channels regulate activity at the level of neurons; interactions between multiple populations of neurons ultimately give rise to complex neural systems function and behavior. This spatial complexity takes place in the context of a composite temporal integration of multiple, different events unfolding at the millisecond, second, minute, hour, and longer time scales. In this study, we present a multiscale modeling methodology that integrates synaptic models into single neuron, and multineuron, network models. We have applied this approach to the specific problem of how changes at the level of kinetic parameters of a receptor-channel model are translated into changes in the temporal firing pattern of a single neuron, and ultimately, changes in the spatiotemporal activity of a network of neurons. These results demonstrate how this powerful methodology can be applied to understand the effects of a given local process within multiple hierarchical levels of the nervous system.

The Neurobiological Basis of Cognition: Identification by Multi-Input, Multioutput Nonlinear Dynamic Modeling: A method is proposed for measuring and modeling human long-term memory formation by mathematical analysis and computer simulation of nerve-cell dynamics

The successful development of neural prostheses requires an understanding of the neurobiological bases of cognitive processes, i.e., how the collective activity of populations of neurons results in a higher level process not predictable based on knowledge of the individual neurons and/or synapses alone. We have been studying and applying novel methods for representing nonlinear transformations of multiple spike train inputs (multiple time series of pulse train inputs) produced by synaptic and field interactions among multiple subclasses of neurons arrayed in multiple layers of incompletely connected units. We have been applying our methods to study of the hippocampus, a cortical brain structure that has been demonstrated, in humans and in animals, to perform the cognitive function of encoding new long-term (declarative) memories. Without their hippocampi, animals and humans retain a short-term memory (memory lasting approximately 1 min), and long-term memory for information learned prior to loss of hippocampal function. Results of more than 20 years of studies have demonstrated that both individual hippocampal neurons, and populations of hippocampal cells, e.g., the neurons comprising one of the three principal subsystems of the hippocampus, induce strong, higher order, nonlinear transformations of hippocampal inputs into hippocampal outputs. For one synaptic input or for a population of synchronously active synaptic inputs, such a transformation is represented by a sequence of action potential inputs being changed into a different sequence of action potential outputs. In other words, an incoming temporal pattern is transformed into a different, outgoing temporal pattern. For multiple, asynchronous synaptic inputs, such a transformation is represented by a spatiotemporal pattern of action potential inputs being changed into a different spatiotemporal pattern of action potential outputs. Our primary thesis is that the encoding of short-term memories into new, long-term memories represents the collective set of nonlinearities induced by the three or four principal subsystems of the hippocampus, i.e., entorhinal cortex-to-dentate gyrus, dentate gyrus-to-CA3 pyramidal cell region, CA3-to-CA1 pyramidal cell region, and CA1-to-subicular cortex. This hypothesis will be supported by studies using in vivo hippocampal multineuron recordings from animals performing memory tasks that require hippocampal function. The implications for this hypothesis will be discussed in the context of "cognitive prostheses"-neural prostheses for cortical brain regions believed to support cognitive functions, and that often are subject to damage due to stroke, epilepsy, dementia, and closed head trauma.

Reduced excitability in the dentate gyrus network of betaIV-spectrin mutant mice in vivo

The submembrane cytoskeletal meshwork of the axon contains the scaffolding protein betaIV-spectrin. It provides mechanical support for the axon and anchors membrane proteins. Quivering (qv(3j)) mice lack functional betaIV-spectrin and have reduced voltage-gated sodium channel (VGSC) immunoreactivity at the axon initial segment and nodes of Ranvier. Because VGSCs are critically involved in action potential generation and conduction, we hypothesized that qv(3j) mice should also show functional deficits at the network level. To test this hypothesis, we investigated granule cell function in the dentate gyrus of anesthetized qv(3j) mice after electrical stimulation of the perforant path in vivo. This revealed an impaired input-output relationship between stimulus intensity and granule cell population spikes and an enhanced paired-pulse inhibition of population spikes, indicating a reduced ability of granule cells to generate action potentials and decreased network excitability. In contrast, the input-output curve for evoked field excitatory postsynaptic potentials (fEPSPs) and paired-pulse facilitation of fEPSPs were unchanged, suggesting normal excitatory synaptic transmission at perforant path-granule cell synapses in qv(3j) mutants. To corroborate our findings, we analyzed the influence of VGSC density reduction on dentate network activity using an established computational model of the dentate gyrus network. This in silico approach confirmed that the loss of VGSCs is sufficient to explain the electrophysiological changes observed in qv(3j) mice. Taken together, our findings demonstrate that betaIV-spectrin is required for normal granule cell firing and for physiological levels of network excitability in the mouse dentate gyrus in vivo.

Convergence among non-sister dendritic branches: an activity-controlled mean to strengthen network connectivity

The manner by which axons distribute synaptic connections along dendrites remains a fundamental unresolved issue in neuronal development and physiology. We found in vitro and in vivo indications that dendrites determine the density, location and strength of their synaptic inputs by controlling the distance of their branches from those of their neighbors. Such control occurs through collective branch convergence, a behavior promoted by AMPA and NMDA glutamate receptor activity. At hubs of convergence sites, the incidence of axo-dendritic contacts as well as clustering levels, pre- and post-synaptic protein content and secretion capacity of synaptic connections are higher than found elsewhere. This coupling between synaptic distribution and the pattern of dendritic overlapping results in ‘Economical Small World Network’, a network configuration that enables single axons to innervate multiple and remote dendrites using short wiring lengths. Thus, activity-mediated regulation of the proximity among dendritic branches serves to pattern and strengthen neuronal connectivity.

A new paradigm for theory in integrative biology: the principle of auto-associative stabilization: biochemical networks and the selection of neuronal groups

This paper discusses rationale for a theory in biology: what exactly is a theory in biology? Is it of a mathematical nature? How to conceive an integrative theory and why? Replies to these questions are offered for subsequent discussions as concerns the mathematical theory of integrative physiology (MTIP) proposed by the author. It is shown that such a theory is a theoretical framework built on a representation in terms of hierarchical functional interactions and a specific formalism, the S-Propagator, to traverse the levels of organization. As for all natural theories, the MTIP is based on a general principle specific to biology, the principle of auto-associative stabilization (PAAS). In this framework, two models are revisited for a novel interpretation: the first addresses the dynamics of biochemical networks, the second addresses the selection of groups of neurons (TSGN) as suggested by Edelman.

Mathematical constraints for building learning rules in the Purkinje cell system

We first present a method to mathematically build a learning rule for closed-loop neural networks. This rule is then applied to climbing fibers in the cerebellar cortex. Our analytical study is based on previous experimental non-analytical studies, which suggests that climbing fibers carry out an error signal to the brain. Thus, our goal is to find the class of functions for the activity propagated by climbing fibers, allowing the output of the Purkinje cell to converge towards a desired output. These functions must tend towards zero when the objective is reached. Our techniques are generalized to other network models.