Fig 2 - uploaded by Joaquín J Torres
Content may be subject to copyright.
![Dynamics of the calcium model. Panel A, from top to bottom: voltage traces of heart interneurons HN(L,1), HN(L,3), HN(R,3). Panel B, from top to bottom: [Ca 2+ ] ef f , V shif t and voltage time course for oscillator interneuron HN(L,3)](profile/Joaquin-Torres-2/publication/221308856/figure/fig3/AS:667682491875331@1536199319327/Dynamics-of-the-calcium-model-Panel-A-from-top-to-bottom-voltage-traces-of-heart.png)
Dynamics of the calcium model. Panel A, from top to bottom: voltage traces of heart interneurons HN(L,1), HN(L,3), HN(R,3). Panel B, from top to bottom: [Ca 2+ ] ef f , V shif t and voltage time course for oscillator interneuron HN(L,3)
Source publication
We have explored the role of the interaction of slow and fast intracellular dynamics in generating precise spiking-bursting
activity in a model of the heartbeat central pattern generator of the leech. In particular we study the effect of calcium-dependent
currents on the neural signatures generated in the circuit. These neural signatures are cell-s...
Contexts in source publication
Context 1
... is surely rate limiting for [Ca 2+ ] ef f . This model will from now on be referred to as the calcium model, as opposed to the original model described in [16]. Note that the implementa- tion of the coordinating interneurons and of the synaptic coupling is the same in the two models. The calcium model also produces a robust oscillatory rhythm ( Fig. 2A) with a period of 6.16 ± ...
Context 2
... to the symmetry of the model only results from one neuron of each kind are presented. Figure 2B shows the membrane potential of an oscillator interneuron together with the time course of [Ca 2+ ] ef f and V shif t . As the cell depolarizes, Ca 2+ enters through voltage dependent calcium channels and [Ca 2+ ] ef f correspondingly rises until approximately one third of the whole duration of the burst. ...
Similar publications
Citations
... Calcium is also a player in the global dynamics of the neuron. It contributes to generate complex patterns of spiking-bursting activity and can also affect the reliability of particular features of the electrical response [3]. These two effects, short-term memory and a fundamental role in the single-neuron dynamics, make calcium a major player in the network dynamics. ...
... Further analysis is needed to clarify more aspects of the relationship between calcium and voltage. For example, the participation of calcium in the neuronal dynamics can regulate the timing precision of action potentials in specific regions of the burst [3]. Recent experimental work by our group shows the presence of regions of focalized timing precision in bursting activity [5]. ...
The interaction between slow calcium dynamics and voltage dynamics increases the processing capabilities of neurons. A proper understanding of this interaction has to take into account its stochastic nature. We recorded voltage and calcium from spiking–bursting neurons and performed a statistical study of the correlation between both variables. We found a rich repertoire of time scales and correlated and uncorrelated parameters. We expect our results to constrain future modelling of spike bursting.
A traditional view in neuroscience is that information arriving through one channel, i.e. a synapse, is encoded through a single code in the signal, e.g., the rate or the precise timing of the incoming events. However, not all the neural readers have to be interested in the same aspect of a common input signal, especially in multifunctional networks that can take advantage of several simultaneous codes. Multiple codes can be used to discriminate or contextualize certain inputs, even in single neurons. Dynamical mechanisms can add to the existing hard-wired connectivity for this task. Recent experiments have revealed the existence of neural signatures in the activity of bursting cells of invertebrate central pattern generators. These signatures consist of cell-specific spike timings in the bursting activity of the neurons. The signatures coexist with the information encoded in the frequency and/or phase relationships of the slow waves. The functional role of these neural fingerprints is still unclear. Based on experiments and using conductance-based models, we discuss the origin and the role of neural signatures as a part of a multicoding strategy for single cells in different types of neural circuits.
Neural signatures are cell-specific interspike interval distributions found in bursting neurons. These intraburst signatures have been first described for the neurons of the pyloric CPG of crustacean. Their functional role is still unknown and their presence in other CPG systems has not been shown yet. Modeling has shown that neural signatures can be part of a multicoding strategy in spiking–bursting neurons. In this paper, we analyze the interspike interval distribution of bursting motoneurons of the leech heartbeat CPG. We describe the cell-specific characteristics of the neural signatures found in this circuit and we discuss plausible functional roles.
Recent experimental findings have shown the presence of robust and cell-type-specific intraburst firing patterns in bursting neurons. We address the problem of characterizing these patterns under the assumption that the bursts exhibit well-defined firing time distributions. We propose a method for estimating these distributions based on a burst alignment algorithm that minimizes the overlap among the firing time distributions of the different spikes within the burst. This method provides a good approximation to the burst's intrinsic temporal structure as a set of firing time distributions. In addition, the method allows labeling the spikes in any particular burst, establishing a correspondence between each spike and the distribution that best explains it, and identifying missing spikes. Our results on both simulated and experimental data from the lobster stomatogastric ganglion show that the proposed method provides a reliable characterization of the intraburst firing patterns and avoids the errors derived from missing spikes. This method can also be applied to nonbursting neurons as a general tool for the study and the interpretation of firing time distributions as part of a temporal neural code.
