A New Principle for Information Storage in an Enzymatic Pathway Model

Strong experimental evidence indicates that protein kinase and phosphatase (KP) cycles are critical to both the induction and maintenance of activity-dependent modifications in neurons. However, their contribution to information storage remains controversial, despite impressive modeling efforts. For instance, plasticity models based on KP cycles do not account for the maintenance of plastic modifications. Moreover, bistable KP cycle models that display memory fail to capture essential features of information storage: rapid onset, bidirectional control, graded amplitude, and finite lifetimes. Here, we show in a biophysical model that upstream activation of KP cycles, a ubiquitous mechanism, is sufficient to provide information storage with realistic induction and maintenance properties: plastic modifications are rapid, bidirectional, and graded, with finite lifetimes that are compatible with animal and human memory. The maintenance of plastic modifications relies on negligible reaction rates in basal conditions and thus depends on enzyme nonlinearity and activation properties of the activity-dependent KP cycle. Moreover, we show that information coding and memory maintenance are robust to stochastic fluctuations inherent to the molecular nature of activity-dependent KP cycle operation. This model provides a new principle for information storage where plasticity and memory emerge from a single dynamic process whose rate is controlled by neuronal activity. This principle strongly departs from the long-standing view that memory reflects stable steady states in biological systems, and offers a new perspective on memory in animals and humans

A THEORY of RATE CODING CONTROL by INTRINSIC PLASTICITY EFFECTS

International audienceIntrinsic plasticity (IP) is a ubiquitous activity-dependent process regulating neuronal excitability and a cellular correlate of behavioral learning and neuronal homeostasis. Because IP is induced rapidly and maintained long-term, it likely represents a major determinant of adaptive collective neuronal dynamics. However, assessing the exact impact of IP has remained elusive. Indeed, it is extremely difficult disentangling the complex non-linear interaction between IP effects, by which conductance changes alter neuronal activity, and IP rules, whereby activity modifies conductance via signaling pathways. Moreover, the two major IP effects on firing rate, threshold and gain modulation, remain unknown in their very mechanisms. Here, using extensive simulations and sensitivity analysis of Hodgkin-Huxley models, we show that threshold and gain modulation are accounted for by maximal conductance plasticity of conductance that situate in two separate domains of the parameter space corresponding to sub- and supra threshold conductance (i.e. activating below or above the spike onset threshold potential). Analyzing equivalent integrate-and-fire models, we provide formal expressions of sensitivities relating to conductance parameters, unraveling unprecedented mechanisms governing IP effects. Our results generalize to the IP of other conductance parameters and allow strong inference for calcium-gated conductance, yielding a general picture that accounts for a large repertoire of experimental observations. The expressions we provide can be combined with IP rules in rate or spiking models, offering a general framework to systematically assess the computational consequences of IP of pharmacologically identified conductance with both fine grain description and mathematical tractability. We provide an example of such IP loop model addressing the important issue of the homeostatic regulation of spontaneous discharge. Because we do not formulate any assumptions on modification rules, the present theory is also relevant to other neural processes involving excitability changes, such as neuromodulation, development, aging and neural disorders

Computational Modeling of IP3 Receptor Function and Intracellular Mechanisms in Synaptic Plasticity

Learning and memory in the brain have been shown to involve complex molecular interactions. In the field of computational neuroscience, mathematical modeling and computer simulations are combined with laboratory experiments to better understand the dynamics of these interactions. A vast number of computational models related to intracellular molecular mechanisms calls for means to compare them to each other. In this thesis, computational models and methods for understanding specific molecular mechanisms in synaptic plasticity, a phenomenon involved in learning, are studied and compared both quantitatively and qualitatively. The focus is set on the IP3 receptor kinetics and the intracellular molecular mechanisms including processing of calcium ions in the postsynaptic neuron. Calcium has been shown to play an important role in different types of synaptic plasticity, only the mechanisms and dynamics for elevation of cytosolic calcium concentration vary. The IP3 receptor, an intracellular calcium releasing channel, is one of the major factors responsible for the calcium elevation in neurons. Firstly, the applicability of deterministic and stochastic approaches in modeling the IP3 receptor kinetics, involving small number of molecules, is studied. In this case, the study shows that stochastic approach, especially Gillespie stochastic simulation algorithm, should be favored. Secondly, since a well-established model for IP3 receptor function in neurons is lacking, this thesis provides not only tools for model comparison but also an insight to which model of the tens of models to choose. Using stochastic simulations, four IP3 models are compared to experimental data to clarify how well they model the measured features in neurons. The results show that there are major differences in the statistical properties of the IP3 receptor models although the models have originally been developed to describe the same phenomenon. Thirdly, this study shows that the models for postsynaptic signaling in synaptic plasticity are becoming more sophisticated by involving stochastic properties, incorporating electrophysiolocial properties of the entire neuron, or having diffusion of signaling molecules. Computational comparison of these models reveals that when using the same input, models describing the phenomenon in the same neuron type produce different results. One of the future goals of computational neuroscience is to find predictive computational models for biochemical and biophysical mechanisms of synaptic plasticity in different brain areas and cells of mammals. When describing a system of molecular events, the selection of modeling and simulation approach should be done carefully by keeping the properties of the modeled biological system in mind. Not only do theoreticians and modelers need to consider experimental findings, but the experimental progress could also be enhanced by using simulations to select the most promising experiments. As discussed in this thesis, attention paid to these issues should improve the utility of modeling approaches for investigating molecular mechanisms of synaptic plasticity. Only then is it possible to use the models to learn something new about the mammalian brain function