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Each neuron needs to maintain a careful balance between the changes implicit in experience, and the demands of stability required by its function. This balance tips depending on the neuronal system, but in any role, disease or neural disorders can develop when the regulatory mechanisms involved in neuronal stability fail. The objective of this thesis was to characterize mechanisms underlying neuronal stability and activity maintenance, in the hopes that further understanding of these processes might someday lead to novel interventions for neurological disorders. The pyloric circuit of decapod crustaceans controls the rhythmic contractions of the foregut musculature, and has long been recognized as an excellent model system in which to study neuronal network stability. Recent experimental evidence has shown that each neuronal cell type of this circuit exhibits a unique set of positive linear correlations between ionic membrane conductances, which suggests that coordinated expression of ion channels plays a role in constraining neuronal electrical activity. In Aim 1, we hypothesized a causal relationship between expressed conductance correlations and features of cellular identity, namely electrical activity type. We partitioned an existing database of conductance-based model neurons based on various measures of intrinsic activity to approximate distinctions between biological cell types. We then tested individual conductance pairs for linear dependence to identify correlations. Similar to experimental results, each activity type investigated had a unique combination of correlated conductances. Furthermore, we found that populations of models that conform to a specific conductance correlation have a higher likelihood of exhibiting a particular feature of electrical activity. We conclude that regulating conductance ratios can support proper electrical activity of a wide range of cell types, particularly when the identity of the cell is well-defined by one or two features of its activity. The phenomenon of pyloric network recovery after removal of top-down neuromodulatory input--a process termed decentralization--is seen as a classic model of homeostatic change after injury. After decentralization, the pyloric central pattern generator briefly loses its characteristic rhythmic activity, but the same activity profile is recovered 3-5 days later via poorly understood homeostatic changes. This re-emergence of the pyloric rhythm occurs without the full pre-decentralization set of fixed conductance ratios. If conductance ratios stabilize pyloric activity before decentralization as we showed in Aim 1, then other mechanisms must account for the return of the pyloric rhythm after network recovery. Based on vertebrate studies demonstrating a role for the extracellular matrix (ECM) in activity regulation, we hypothesized in Aim 2 that the ECM was participating in activity maintenance in the stomatogastric nervous system. We used the enzyme chondroitinase ABC (chABC) to degrade extracellular chondroitin sulfate (CS) in the stomatogastric ganglion while in organ culture. Our results are the first to demonstrate the presence of CS in the crustacean nervous system via immunochemistry. Furthermore, we show that while ongoing activity is not disrupted by chABC treatment, recovery of pyloric activity after decentralization was significantly delayed without intact extracellular CS. Our results are the first to show that CS has a role in neuronal activity maintenance in crustaceans, and suggest that CS may be involved in initiating or directing activity maintenance needed in times of neuronal stress.

Investigamos o papel de condutâncias iônicas lentas na transmissão/codificação de informação entre neurônios que disparam em rajadas ou bursts. Para isso, desenvolvemos um protocolo experimental no qual a interação em tempo real entre computador e neurônio biológico permite isolar o efeito da dinâmica de um determinado tipo de canal iônico e estudar sua inuência nos mecanismos de codificação de informação. Os experimentos foram realizados com neurônios do gânglio estomatogástrico do siri azul, Callinectes sapidus, que não possuem condutâncias lentas capazes de fazê-los apresentar rajadas de disparos quando in vitro, condição na qual apresentam comportamento quiescente ou disparam tonicamente. Durante os experimentos, alteramos artificialmente o comportamento de um destes neurônios, conectando-o a um computador que introduz uma corrente capaz de fazê-lo apresentar rajadas. Essa corrente possui uma componente senoidal (vinda de um gerador de funções) e uma componente devido a uma condutância iônica lenta modelada matematicamente. A condutância lenta pode ser escolhida entre duas versões: uma em que a condutância é calculada em tempo real, a partir do valor instantâneo do potencial de membrana do neurônio biológico, e outra em que o valor da condutância é oriundo de uma série temporal previamente gravada. A fonte de informação utilizada nos experimentos é um neurônio artificial pré-sináptico, que possui uma distribuição de potenciais de ação (spikes) escolhida pelo experimentador, e é conectado ao neurônio biológico modificado através de um modelo de sinapse química inibidora. A quantidade de informação do neurônio artificial (variabilidade dos padrões de disparo) codificada pelo neurônio biológico é inferida calculando-se a informação mútua média entre eles para as duas versões da condutância lenta (dinâmica ou previamente gravada). Nossos experimentos reproduziram qualitativamente as observações feitas por nosso grupo no circuito pilórico intacto do siri, que possui neurônios conectados por mutua inibição que naturalmente apresentam bursts. Além disso, observamos que vários picos de informação mútua média, presentes quando a condutância é dinâmica, desaparecem quando esta é substituída pela série temporal previamente gravada da condutância. Assim, pudemos confirmar os resultados previamente obtidos com simulações computacionais em que foram utilizados apenas modelos matemáticos e na ausência de ruído e demonstramos que as condutâncias iônicas lentas constituem um mecanismo biofísico que permite a codificação de estímulos sinápticos em neurônios que apresentam rajadas. / We investigated the role of slow ionic conductances on information processing by bursting neurons. A real time experimental protocol was developed to allow interacting computer models and biological neurons to address the effect of dynamical details of a single type of ion channel in information coding mechanisms. We experimented on Callinectes sapidus (blue crab) stomatogastric ganglion neurons. Such neurons were chosen because they do not present the slow conductances that can led to bursting activity in vitro (in such conditions they can be found either in a quiescent or in a tonic firing state). The experiments consisted in artificially changing the behavior of one of these neurons by injecting a computer generated current to achieve bursting. Such current has a sinusoidal component (from a function generator) and a component due to mathematical model of a slow ionic conductance. The slow conductance was implemented in two versions: in one of them the instantaneous value of the conductance is computed in real time and according to the membrane potential of the biological neuron, in another version the value of the conductance simply comes from a time series previously stored in the computer. A pre-synaptic artificial neuron, with a spike distribution chosen by the experimenter, provided input for the biological neuron through an artificial chemical inhibitory synapse. The amount of information (variability of spike patterns from the artificial neuron) coded by the biological neuron was inferred by calculating the average mutual information along stimulus and response bursts for the two conditions of the slow conductance (dynamically calculated or from file previously stored). Our experiments reproduced the results found in intact pyloric central pattern generator bursting neurons connected by mutual inhibition. Moreover, we show that the average mutual information peaks, found when the conductance is dynamically calculated, disappear when we use the previously recorded time series of the conductance. Such results validate those only found previously in numerical simulations in the absence of noise and point the role of the slow ionic conductances in a biophysical mechanism that allow bursting motor neurons to encode in a nontrivial fashion the information they receive through a single synapse.

Neuronal networks produce reliable functional output throughout the lifespan of an animal despite ceaseless molecular turnover and a constantly changing environment. The cellular and molecular mechanisms underlying the ability of these networks to maintain functional stability remain poorly understood. Central pattern generating circuits produce a stable, predictable rhythm, making them ideal candidates for studying mechanisms of activity maintenance. By identifying and characterizing the regulators of activity in small neuronal circuits, we not only obtain a clearer understanding of how neural activity is generated, but also arm ourselves with knowledge that may eventually be used to improve medical care for patients whose normal nervous system activity has been disrupted through trauma or disease. We utilize the pattern-generating pyloric circuit in the crustacean stomatogastric nervous system to investigate the general scientific question: How are specific aspects of rhythmic activity regulated in a small neuronal network? The first aim of this thesis poses this question in the context of a single neuron. We used a single-compartment model neuron database to investigate whether co-regulation of ionic conductances supports the maintenance of spike phase in rhythmically bursting “pacemaker” neurons. The second aim of the project extends the question to a network context. Through a combination of computational and electrophysiology studies, we investigated how the intrinsic membrane conductances of the pacemaker neuron influence its response to synaptic input within the framework of the Phase Resetting Curve (PRC). The third aim of the project further extends the question to a systems-level context. We examined how ambient temperatures affect the stability of the pyloric rhythm in the intact, behaving animal. The results of this work have furthered our understanding of the principles underlying the long-term stability of neuronal network function.

Investigamos o papel de condutâncias iônicas lentas na transmissão/codificação de informação entre neurônios que disparam em rajadas ou bursts. Para isso, desenvolvemos um protocolo experimental no qual a interação em tempo real entre computador e neurônio biológico permite isolar o efeito da dinâmica de um determinado tipo de canal iônico e estudar sua inuência nos mecanismos de codificação de informação. Os experimentos foram realizados com neurônios do gânglio estomatogástrico do siri azul, Callinectes sapidus, que não possuem condutâncias lentas capazes de fazê-los apresentar rajadas de disparos quando in vitro, condição na qual apresentam comportamento quiescente ou disparam tonicamente. Durante os experimentos, alteramos artificialmente o comportamento de um destes neurônios, conectando-o a um computador que introduz uma corrente capaz de fazê-lo apresentar rajadas. Essa corrente possui uma componente senoidal (vinda de um gerador de funções) e uma componente devido a uma condutância iônica lenta modelada matematicamente. A condutância lenta pode ser escolhida entre duas versões: uma em que a condutância é calculada em tempo real, a partir do valor instantâneo do potencial de membrana do neurônio biológico, e outra em que o valor da condutância é oriundo de uma série temporal previamente gravada. A fonte de informação utilizada nos experimentos é um neurônio artificial pré-sináptico, que possui uma distribuição de potenciais de ação (spikes) escolhida pelo experimentador, e é conectado ao neurônio biológico modificado através de um modelo de sinapse química inibidora. A quantidade de informação do neurônio artificial (variabilidade dos padrões de disparo) codificada pelo neurônio biológico é inferida calculando-se a informação mútua média entre eles para as duas versões da condutância lenta (dinâmica ou previamente gravada). Nossos experimentos reproduziram qualitativamente as observações feitas por nosso grupo no circuito pilórico intacto do siri, que possui neurônios conectados por mutua inibição que naturalmente apresentam bursts. Além disso, observamos que vários picos de informação mútua média, presentes quando a condutância é dinâmica, desaparecem quando esta é substituída pela série temporal previamente gravada da condutância. Assim, pudemos confirmar os resultados previamente obtidos com simulações computacionais em que foram utilizados apenas modelos matemáticos e na ausência de ruído e demonstramos que as condutâncias iônicas lentas constituem um mecanismo biofísico que permite a codificação de estímulos sinápticos em neurônios que apresentam rajadas. / We investigated the role of slow ionic conductances on information processing by bursting neurons. A real time experimental protocol was developed to allow interacting computer models and biological neurons to address the effect of dynamical details of a single type of ion channel in information coding mechanisms. We experimented on Callinectes sapidus (blue crab) stomatogastric ganglion neurons. Such neurons were chosen because they do not present the slow conductances that can led to bursting activity in vitro (in such conditions they can be found either in a quiescent or in a tonic firing state). The experiments consisted in artificially changing the behavior of one of these neurons by injecting a computer generated current to achieve bursting. Such current has a sinusoidal component (from a function generator) and a component due to mathematical model of a slow ionic conductance. The slow conductance was implemented in two versions: in one of them the instantaneous value of the conductance is computed in real time and according to the membrane potential of the biological neuron, in another version the value of the conductance simply comes from a time series previously stored in the computer. A pre-synaptic artificial neuron, with a spike distribution chosen by the experimenter, provided input for the biological neuron through an artificial chemical inhibitory synapse. The amount of information (variability of spike patterns from the artificial neuron) coded by the biological neuron was inferred by calculating the average mutual information along stimulus and response bursts for the two conditions of the slow conductance (dynamically calculated or from file previously stored). Our experiments reproduced the results found in intact pyloric central pattern generator bursting neurons connected by mutual inhibition. Moreover, we show that the average mutual information peaks, found when the conductance is dynamically calculated, disappear when we use the previously recorded time series of the conductance. Such results validate those only found previously in numerical simulations in the absence of noise and point the role of the slow ionic conductances in a biophysical mechanism that allow bursting motor neurons to encode in a nontrivial fashion the information they receive through a single synapse.