54 research outputs found
NeuroManager: a workflow analysis based simulation management engine for computational neuroscience
Dendritic Excitability Modulates Dendritic Information Processing in a Purkinje Cell Model
Using an electrophysiological compartmental model of a Purkinje cell we quantified the contribution of individual active dendritic currents to processing of synaptic activity from granule cells. We used mutual information as a measure to quantify the information from the total excitatory input current (IGlu) encoded in each dendritic current. In this context, each active current was considered an information channel. Our analyses showed that most of the information was encoded by the calcium (ICaP) and calcium activated potassium (IKc) currents. Mutual information between IGlu and ICaP and IKc was sensitive to different levels of excitatory and inhibitory synaptic activity that, at the same time, resulted in the same firing rate at the soma. Since dendritic excitability could be a mechanism to regulate information processing in neurons we quantified the changes in mutual information between IGlu and all Purkinje cell currents as a function of the density of dendritic Ca (gCaP) and Kca (gKc) conductances. We extended our analysis to determine the window of temporal integration of IGlu by ICaP and IKc as a function of channel density and synaptic activity. The window of information integration has a stronger dependence on increasing values of gKc than on gCaP, but at high levels of synaptic stimulation information integration is reduced to a few milliseconds. Overall, our results show that different dendritic conductances differentially encode synaptic activity and that dendritic excitability and the level of synaptic activity regulate the flow of information in dendrites
Synaptic activity and excitability modulates information transfer in Purkinje cells: a modeling study
On the role of theory and modeling in neuroscience
In recent years, the field of neuroscience has gone through rapid
experimental advances and extensive use of quantitative and computational
methods. This accelerating growth has created a need for methodological
analysis of the role of theory and the modeling approaches currently used in
this field. Toward that end, we start from the general view that the primary
role of science is to solve empirical problems, and that it does so by
developing theories that can account for phenomena within their domain of
application. We propose a commonly-used set of terms - descriptive,
mechanistic, and normative - as methodological designations that refer to the
kind of problem a theory is intended to solve. Further, we find that models of
each kind play distinct roles in defining and bridging the multiple levels of
abstraction necessary to account for any neuroscientific phenomenon. We then
discuss how models play an important role to connect theory and experiment, and
note the importance of well-defined translation functions between them.
Furthermore, we describe how models themselves can be used as a form of
experiment to test and develop theories. This report is the summary of a
discussion initiated at the conference Present and Future Theoretical
Frameworks in Neuroscience, which we hope will contribute to a much-needed
discussion in the neuroscientific community
Transient extracellular application of gold nanostars increases hippocampal neuronal activity
Quantifying the Effects of Elastic Collisions and Non-Covalent Binding on Glutamate Receptor Trafficking in the Post-Synaptic Density
One mechanism of information storage in neurons is believed to be determined by the strength of synaptic contacts. The strength of an excitatory synapse is partially due to the concentration of a particular type of ionotropic glutamate receptor (AMPAR) in the post-synaptic density (PSD). AMPAR concentration in the PSD has to be plastic, to allow the storage of new memories; but it also has to be stable to preserve important information. Although much is known about the molecular identity of synapses, the biophysical mechanisms by which AMPAR can enter, leave and remain in the synapse are unclear. We used Monte Carlo simulations to determine the influence of PSD structure and activity in maintaining homeostatic concentrations of AMPARs in the synapse. We found that, the high concentration and excluded volume caused by PSD molecules result in molecular crowding. Diffusion of AMPAR in the PSD under such conditions is anomalous. Anomalous diffusion of AMPAR results in retention of these receptors inside the PSD for periods ranging from minutes to several hours in the absence of strong binding of receptors to PSD molecules. Trapping of receptors in the PSD by crowding effects was very sensitive to the concentration of PSD molecules, showing a switch-like behavior for retention of receptors. Non-covalent binding of AMPAR to anchored PSD molecules allowed the synapse to become well-mixed, resulting in normal diffusion of AMPAR. Binding also allowed the exchange of receptors in and out of the PSD. We propose that molecular crowding is an important biophysical mechanism to maintain homeostatic synaptic concentrations of AMPARs in the PSD without the need of energetically expensive biochemical reactions. In this context, binding of AMPAR with PSD molecules could collaborate with crowding to maintain synaptic homeostasis but could also allow synaptic plasticity by increasing the exchange of these receptors with the surrounding extra-synaptic membrane
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