Abstract The synaptic input received by neurons in cortical circuits is in constant flux. From both environmental sensory changes and learning mechanisms that modify synaptic strengths, the excitatory and inhibitory signals received by a post-synaptic cell vary on a continuum of time scales. These variable inputs inherent in different sensory environments, as well as inputs changed by Hebbian learning mechanisms (which have been shown to destabilize the activity of neural circuits) serve to limit the input ranges over which a neural network can effectively operate. To avoid circuit behavior which is either quiescent or epileptic, there are a variety of homeostatic mechanisms in place to maintain proper levels of circuit activity. This article provides a basic overview of the biological mechanisms, and consider the advantages and disadvantages of homeostasis on a theoretical level. / text

付記する学位プログラム名: 京都大学卓越大学院プログラム「メディカルイノベーション大学院プログラム」 / 京都大学 / 新制・課程博士 / 博士(医学) / 甲第24509号 / 医博第4951号 / 新制||医||1064(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 林 康紀, 教授 渡邉 大, 教授 高橋 淳 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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Há evidências convincentes de que o início da doença de Alzheimer é precedido por uma redução de estímulos sensoriais, como ocorre durante a aposentadoria, catarata, surdez e degeneração macular, em um cérebro idoso que apresenta deficiência de receptores tipo GABAA. Neste trabalho foi utilizado um modelo computacional fenomenológico do koniocortex, que é a primeira camada cortical que recebe estímulos sensoriais, adaptado para simular as fases iniciais da doença de Alzheimer. A arquitetura e as propriedades dos neurônios do modelo computacional do koniocortex se assemelham as do cérebro, sendo também capaz de aprender, permitindo com isso que a memória de curto prazo seja testada em qualquer momento. Usando o modelo computacional é possível também analisar as fases iniciais da doença de Alzheimer simulando o \"envelhecimento\" do koniocortex artificial através de um conjunto de parâmetros referentes à plasticidade intrínseca, à acetilcolina, aos estímulos sensoriais, ao pruning sináptico, entre outros. O modelo computacional revela que, quando o envelhecimento afeta os neurônios que expressam receptores GABA-A ocorrendo na sequência uma redução dos estímulos sensoriais, o resultado dessa cascata de eventos leva ao hipermetabolismo e ao início da fase de deposição excessiva das placas -amiloide / There is compelling evidence that Alzheimers disease onset is preceded by a reduction of sensory stimuli like during job retirement, cataract, deafness or even macular degeneration, over an aged brain with impaired GABA-A receptor inhibitions. In this paper, was adapted a phenomenological computational model of the koniocortex which is the first cortical layer that receives sensory stimuli to simulate the initial phases of Alzheimers disease. The architecture and neurons properties of the modeled koniocortex resemble those of the brain, so that the model is also able to learn, thereby allowing the assessment of short-term memory at any moment. By using the computational model, it is possible to analyze the initial phases of Alzheimers disease by aging the artificial koniocortex through a set of parameters related to intrinsic plasticity, acetylcholine, sensory stimuli, synaptic pruning, among others. The computational model shows that when aging occurs in such way that GABA-A receptor expressing neurons are affected, and, in the sequence, a reduction of sensory stimuli takes place, the result of this cascade of events leads to hypermetabolism and to the initial phase excessive deposition of beta-amyloid plaques

Há evidências convincentes de que o início da doença de Alzheimer é precedido por uma redução de estímulos sensoriais, como ocorre durante a aposentadoria, catarata, surdez e degeneração macular, em um cérebro idoso que apresenta deficiência de receptores tipo GABAA. Neste trabalho foi utilizado um modelo computacional fenomenológico do koniocortex, que é a primeira camada cortical que recebe estímulos sensoriais, adaptado para simular as fases iniciais da doença de Alzheimer. A arquitetura e as propriedades dos neurônios do modelo computacional do koniocortex se assemelham as do cérebro, sendo também capaz de aprender, permitindo com isso que a memória de curto prazo seja testada em qualquer momento. Usando o modelo computacional é possível também analisar as fases iniciais da doença de Alzheimer simulando o \"envelhecimento\" do koniocortex artificial através de um conjunto de parâmetros referentes à plasticidade intrínseca, à acetilcolina, aos estímulos sensoriais, ao pruning sináptico, entre outros. O modelo computacional revela que, quando o envelhecimento afeta os neurônios que expressam receptores GABA-A ocorrendo na sequência uma redução dos estímulos sensoriais, o resultado dessa cascata de eventos leva ao hipermetabolismo e ao início da fase de deposição excessiva das placas -amiloide / There is compelling evidence that Alzheimers disease onset is preceded by a reduction of sensory stimuli like during job retirement, cataract, deafness or even macular degeneration, over an aged brain with impaired GABA-A receptor inhibitions. In this paper, was adapted a phenomenological computational model of the koniocortex which is the first cortical layer that receives sensory stimuli to simulate the initial phases of Alzheimers disease. The architecture and neurons properties of the modeled koniocortex resemble those of the brain, so that the model is also able to learn, thereby allowing the assessment of short-term memory at any moment. By using the computational model, it is possible to analyze the initial phases of Alzheimers disease by aging the artificial koniocortex through a set of parameters related to intrinsic plasticity, acetylcholine, sensory stimuli, synaptic pruning, among others. The computational model shows that when aging occurs in such way that GABA-A receptor expressing neurons are affected, and, in the sequence, a reduction of sensory stimuli takes place, the result of this cascade of events leads to hypermetabolism and to the initial phase excessive deposition of beta-amyloid plaques

Ion channels are fundamental molecules in the nervous system that catalyze the flux of ions across the cell membrane. There are mounting evidences suggesting that the kinetic properties of ion channels undergo activity-dependent changes in various pathophysiological conditions. Here such activity-dependent changes were studied in case of two different ion channels; the rat brain derived voltage-gated Na+ channel, rNav1.2 and the human background leak K+ channel, hTREK1 using the single channel patch-clamp technique. Our results on the voltage-gated Na+ channel (Chapter III) illustrated that sustained membrane depolarization, as seen in pathophysiological conditions like epilepsy, induced a defined non-linear variation in the unitary conductance, activation, inactivation and recovery kinetic properties of the channel. Signal processing tools attributed a pseudo-oscillatory nature to the non-linearity observed in the channel properties. Prolonged membrane depolarization also induced a “molecular memory” phenomenon, characterized by clustering of dwell time events and strong autocorrelation in the dwell time series. The persistence of such molecular memory was found to be dependent on the duration of depolarization. Similar plastic changes were observed in case of the hTREK1 channel in presence of saturating concentrations of agonist, trichloroethanol (TCE) (Chapter IV). TREK1 channel behaves similar to single enzyme molecules with a single binding site for the substrate K+ ion whereas TCE acts as an allosteric activator of the channel. We observed that with increasing concentration of TCE (10 M to 10 mM) the catalytic turnover rate exhibited progressive departure from monoexponential to multi-exponential distribution suggesting the presence of ‘dynamic disorder’ analogous to single enzyme molecules. In addition, we observed the induction of strong correlation in successive waiting times and flux intensities, exemplified by distinct mode switching between high and low flux activity, which implied the induction of memory in single ion channel. Our observation of such molecular memory in two different ion channels in different experimental conditions highlights the importance and generality of the phenomenon which is normally hidden under the ensemble behaviour of ion channels. In the final part of the work (chapter V) we observed strong negative cooperativity and half-of-sites saturation kinetics in the interaction of local anesthetic, lidocaine with hTREK1 channel. We also mapped the specific anesthetic binding site in the c-terminal domain of the channel. Further, single channel analysis and the heterodimer studies enabled us to propose a model for this interaction and provide a plausible paradigm for the inhibitory action of lidocaine on hTREK1.

The degree by which a species can adapt to the demands of its changing environment defines how well it can exploit the resources of new ecological niches. Since the nervous system is the seat of an organism's behavior, studying adaptation starts from there. The nervous system adapts through neuronal plasticity, which may be considered as the brain's reaction to environmental perturbations. In a natural setting, these perturbations are always changing. As such, a full understanding of how the brain functions requires studying neuronal plasticity under temporally varying stimulation conditions, i.e., studying the role of plasticity in carrying out spatiotemporal computations. It is only then that we can fully benefit from the full potential of neural information processing to build powerful brain-inspired adaptive technologies. Here, we focus on homeostatic plasticity, where certain properties of the neural machinery are regulated so that they remain within a functionally and metabolically desirable range. Our main goal is to illustrate how homeostatic plasticity interacting with associative mechanisms is functionally relevant for spatiotemporal computations. The thesis consists of three studies that share two features: (1) homeostatic and synaptic plasticity act on a dynamical system such as a recurrent neural network. (2) The dynamical system is nonautonomous, that is, it is subject to temporally varying stimulation. In the first study, we develop a rigorous theory of spatiotemporal representations and computations, and the role of plasticity. Within the developed theory, we show that homeostatic plasticity increases the capacity of the network to encode spatiotemporal patterns, and that synaptic plasticity associates these patterns to network states. The second study applies the insights from the first study to the single node delay-coupled reservoir computing architecture, or DCR. The DCR's activity is sampled at several computational units. We derive a homeostatic plasticity rule acting on these units. We analytically show that the rule balances between the two necessary processes for spatiotemporal computations identified in the first study. As a result, we show that the computational power of the DCR significantly increases. The third study considers minimal neural control of robots. We show that recurrent neural control with homeostatic synaptic dynamics endows the robots with memory. We show through demonstrations that this memory is necessary for generating behaviors like obstacle-avoidance of a wheel-driven robot and stable hexapod locomotion.

The ability of the visual system for object recognition is remarkable. A better understanding of its processing would lead to better computer vision systems and could improve our understanding of the underlying principles which produce intelligence. We propose a computational model of the visual areas V1 and V2, implementing a rich connectivity inspired by the neocortical circuit. We combined the three most important cortical plasticity mechanisms. 1) Hebbian synaptic plasticity to learn the synapse strengths of excitatory and inhibitory neurons, including trace learning to learn invariant representations. 2) Intrinsic plasticity to regulate the neurons responses and stabilize the learning in deeper layers. 3) Structural plasticity to modify the connections and to overcome the bias for the learnings from the initial definitions. Among others, we show that our model neurons learn comparable receptive fields to cortical ones. We verify the invariant object recognition performance of the model. We further show that the developed weight strengths and connection probabilities are related to the response correlations of the neurons. We link the connection probabilities of the inhibitory connections to the underlying plasticity mechanisms and explain why inhibitory connections appear unspecific. The proposed model is more detailed than previous approaches. It can reproduce neuroscientific findings and fulfills the purpose of the visual system, invariant object recognition. / Das visuelle System des Menschen hat die herausragende Fähigkeit zur invarianten Objekterkennung. Ein besseres Verständnis seiner Arbeitsweise kann zu besseren Computersystemen für das Bildverstehen führen und könnte darüber hinaus unser Verständnis von den zugrundeliegenden Prinzipien unserer Intelligenz verbessern. Diese Arbeit stellt ein Modell der visuellen Areale V1 und V2 vor, welches eine komplexe, von den Strukturen des Neokortex inspirierte, Verbindungsstruktur integriert. Es kombiniert die drei wichtigsten kortikalen Plastizitäten: 1) Hebbsche synaptische Plastizität, um die Stärke der exzitatorischen und inhibitorischen Synapsen zu lernen, welches auch „trace“-Lernen, zum Lernen invarianter Repräsentationen, umfasst. 2) Intrinsische Plastizität, um das Antwortverhalten der Neuronen zu regulieren und damit das Lernen in tieferen Schichten zu stabilisieren. 3) Strukturelle Plastizität, um die Verbindungen zu modifizieren und damit den Einfluss anfänglicher Festlegungen auf das Lernergebnis zu reduzieren. Neben weiteren Ergebnissen wird gezeigt, dass die Neuronen des Modells vergleichbare rezeptive Felder zu Neuronen des visuellen Kortex erlernen. Ebenso wird die Leistungsfähigkeit des Modells zur invariante Objekterkennung verifiziert. Des Weiteren wird der Zusammenhang von Gewichtsstärke und Verbindungswahrscheinlichkeit zur Korrelation der Aktivitäten der Neuronen aufgezeigt. Die gefundenen Verbindungswahrscheinlichkeiten der inhibitorischen Neuronen werden in Zusammenhang mit der Funktionsweise der inhibitorischen Plastizität gesetzt, womit erklärt wird warum inhibitorische Verbindungen unspezifisch erscheinen. Das vorgestellte Modell ist detaillierter als vorangegangene Arbeiten. Es ermöglicht neurowissenschaftliche Erkenntnisse nachzuvollziehen, wobei es ebenso die Hauptleistung des visuellen Systems erbringt, invariante Objekterkennung. Darüber hinaus ermöglichen sein Detailgrad und seine Selbstorganisationsprinzipien weitere neurowissenschaftliche Erkenntnisse und die Modellierung komplexerer Modelle der Verarbeitung im Gehirn.