Interactions between calmodulin and neurogranin govern the dynamics of CaMKII as a leaky integrator

AbstractCalmodulin-dependent kinase II (CaMKII) has long been known to play an important role in learning and memory as well as long term potentiation (LTP). More recently it has been suggested that it might be involved in the time averaging of synaptic signals, which can then lead to the high precision of information stored at a single synapse. However, the role of the scaffolding molecule, neurogranin (Ng), in governing the dynamics of CaMKII is not yet fully understood. In this work, we adopt a rule-based modeling approach through the Monte Carlo method to study the effect of Ca2+ signals on the dynamics of CaMKII phosphorylation in the postsynaptic density (PSD). Calcium surges are observed in synaptic spines during an EPSP and back-propagating action potential due to the opening of NMDA receptors and voltage dependent calcium channels. We study the differences between the dynamics of phosphorylation of CaMKII monomers and dodecameric holoenzymes. The scaffolding molecule Ng, when present in significant concentration, limits the availability of free calmodulin (CaM), the protein which activates CaMKII in the presence of calcium. We show that it plays an important modulatory role in CaMKII phosphorylation following a surge of high calcium concentration. We find a non-intuitive dependence of this effect on CaM concentration that results from the different affinities of CaM for CaMKII depending on the number of calcium ions bound to the former. It has been shown previously that in the absence of phosphatase CaMKII monomers integrate over Ca2+ signals of certain frequencies through autophosphorylation (Pepke et al, Plos Comp. Bio., 2010). We also study the effect of multiple calcium spikes on CaMKII holoenzyme autophosphorylation, and show that in the presence of phosphatase CaMKII behaves as a leaky integrator of calcium signals, a result that has been recently observed in vivo. Our models predict that the parameters of this leaky integrator are finely tuned through the interactions of Ng, CaM, CaMKII, and PP1. This is a possible mechanism to precisely control the sensitivity of synapses to calcium signals.

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A sensory integration account for time perception

10.1101/2020.08.02.232801 ◽

2020 ◽

Author(s):

Alessandro Toso ◽

Arash Fassihi ◽

Luciano Paz ◽

Francesca Pulecchi ◽

Mathew E. Diamond

Keyword(s):

Time Perception ◽

Time Constant ◽

Sensory Integration ◽

Neuronal Firing ◽

Elapsed Time ◽

Leaky Integrator ◽

Long Time ◽

Integrator Model ◽

Short Time ◽

Sense Of Touch

ABSTRACTThe connection between stimulus perception and time perception remains unknown. The present study combines human and rat psychophysics with sensory cortical neuronal firing to construct a computational model for the percept of elapsed time embedded within sense of touch. When subjects judged the duration of a vibration applied to the fingertip (human) or whiskers (rat), increasing stimulus mean speed led to increasing perceived duration. Symmetrically, increasing vibration duration led to increasing perceived intensity. We modeled spike trains from vibrissal somatosensory cortex as input to dual leaky integrators – an intensity integrator with short time constant and a duration integrator with long time constant – generating neurometric functions that replicated the actual psychophysical functions of rats. Returning to human psychophysics, we then confirmed specific predictions of the dual leaky integrator model. This study offers a framework, based on sensory coding and subsequent accumulation of sensory drive, to account for how a feeling of the passage of time accompanies the tactile sensory experience.

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Sodium Channels Implement a Molecular Leaky Integrator to Sense Spiking Frequency and Regulate Neuronal Firing

Biophysical Journal ◽

10.1016/j.bpj.2018.11.2101 ◽

2019 ◽

Vol 116 (3) ◽

pp. 388a

Author(s):

Marco A. Navarro ◽

Jenna Lin ◽

Autoosa Salari ◽

Mirela Milescu ◽

Lorin S. Milescu

Keyword(s):

Sodium Channels ◽

Neuronal Firing ◽

Leaky Integrator

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Delay independent robust stability analysis of delayed fractional quaternion-valued leaky integrator echo state neural networks with QUAD condition

Applied Mathematics and Computation ◽

10.1016/j.amc.2019.04.083 ◽

2019 ◽

Vol 359 ◽

pp. 278-293 ◽

Cited By ~ 4

Author(s):

Seyed Mehdi Abedi Pahnehkolaei ◽

Alireza Alfi ◽

J.A. Tenreiro Machado

Keyword(s):

Neural Networks ◽

Stability Analysis ◽

Robust Stability ◽

Robust Stability Analysis ◽

Leaky Integrator

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An Evolutionary Ecological Approach to the Study of Learning Behavior Using a Robot-Based Model

Adaptive Behavior ◽

10.1177/1059712302919993004 ◽

2002 ◽

Vol 10 (3-4) ◽

pp. 201-221 ◽

Cited By ~ 13

Author(s):

Elio Tuci ◽

Matt Quinn ◽

Inman Harvey

Keyword(s):

Neural Networks ◽

Control System ◽

Behavioral Responses ◽

Evolutionary Robotics ◽

Ecological Models ◽

Autonomous Learning ◽

Ecological Approach ◽

Learning Behavior ◽

Leaky Integrator

We are interested in the construction of ecological models of the evolution of learning behavior using methodological tools developed in the field of evolutionary robotics. In this article, we explore the applicability of integrated (i.e., nonmodular) neural networks with fixed connection weights and simple 'leaky-integrator' neurons as controllers for autonomous learning robots. In contrast to Yamauchi and Beer (1994a), we show that such a control system is capable of integrating reactive and learned behaviour without explicitly needing hand-designed modules, dedicated to a particular behavior, or an externally introduced reinforcement signal. In our model, evolutionary and ecological contingencies structure the controller and the behavioral responses of the robot. This allows us to concentrate on examining the conditions under which learning behavior evolves.

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