Whole Cell Stochastic Model Reproduces the Irregularities Found in the Membrane Potential of Bursting Neurons

Abstract

. Whole cell stochastic model reproduces the irregularities found in the membrane potential of bursting neurons. J Neurophysiol 94: 1169 -1179, 2005. First published March 30, 2005 doi:10.1152/jn.00070.2005. Irregular intrinsic behavior of neurons seems ubiquitous in the nervous system. Even in circuits specialized to provide periodic and reliable patterns to control the repetitive activity of muscles, such as the pyloric central pattern generator (CPG) of the crustacean stomatogastric ganglion (STG), many bursting motor neurons present irregular activity when deprived from synaptic inputs. Moreover, many authors attribute to these irregularities the role of providing flexibility and adaptation capabilities to oscillatory neural networks such as CPGs. These irregular behaviors, related to nonlinear and chaotic properties of the cells, pose serious challenges to developing deterministic Hodgkin-Huxley-type (HHtype) conductance models. Only a few deterministic HH-type models based on experimental conductance values were able to show such nonlinear properties, but most of these models are based on slow oscillatory dynamics of the cytosolic calcium concentration that were never found experimentally in STG neurons. Based on an up-to-date single-compartment deterministic HH-type model of a STG neuron, we developed a stochastic HH-type model based on the microscopic Markovian states that an ion channel can achieve. We used tools from nonlinear analysis to show that the stochastic model is able to express the same kind of irregularities, sensitivity to initial conditions, and low dimensional dynamics found in the neurons isolated from the STG. Without including any nonrealistic dynamics in our whole cell stochastic model, we show that the nontrivial dynamics of the membrane potential naturally emerge from the interplay between the microscopic probabilistic character of the ion channels and the nonlinear interactions among these elements. Moreover, the experimental irregular behavior is reproduced by the stochastic model for the same parameters for which the membrane potential of the original deterministic model exhibits periodic oscillations.

Noise, transient dynamics, and the generation of realistic interspike interval variation in square-wave burster neurons

“…Irregular activity in neuronal models has been associated with the presence of deterministic chaos [39][40][41]. Nevertheless, the main cause of irregularities in our model is the amplification of stochastic phenomena by the transient dynamics.…”

Single Synapse Information Coding in Intraburst Spike Patterns of Central Pattern Generator Motor Neurons

“…The stochastic AN is based on a stochastic LP model that was previously developed to reproduce the irregular behavior of biological LP neurons, which is absent in the Hodgkin-Huxley deterministic model (Carelli et al, 2005). Due to its stochastic nature, the model naturally presented spike timing variability from burst to burst.…”

“…The tuning of these parameters was done by visual comparison between the membrane potential traces of the original LP and of the stochastic AN. All other parameters were kept the same as described in the original reference (Carelli et al, 2005). The dynamic clamp synapse from PD to the stochastic AN was set to shutdown AN when PD starts a burst.…”

“…The AN was implemented either as a random spike generator or as a conductance-based model with additional stochastic dynamics (Carelli et al, 2005) and it was prepared to mimic the bursting phase and the average number of spikes/burst found in the original LP. The PD hyperpolarization onset determined when AN was allowed to burst and it was the only influence the biological circuit had over the AN.…”

Implementação de um protocolo Dynamic Clamp em sistema Linux em tempo real para a produção de condutâncias em neurônios biológicos e eletrônicos

“…Nestes experimentos utilizamos modelos de sinapses químicas como o descrito no Capítulo 6, neurônios eletronicos (ENs) tipo HR como os descritos no Capítulo 4 (ajustados para apresentar comportamento caótico quando isolados) e o modelo de neurônio estocástico descrito no capítulo 5 que foi implementado na forma de modelo do RTLDC pelo estudante Pedro Carelli (Carelli et al, 2005). Um segundo computador que possui um sistema de aquisição de dados para eletrofisiologia Digidata 1322 e software Axoscope 9 (ambos da Axon Instruments) foi usado para digitalizar os diversos sinais a uma taxa de 10 kHz e armazená-los em disco para futura análise.…”