"Stable multi-day recordings from chronically implanted microelectrodes within the dorsolateral prefrontal cortex of two monkeys performing three Go/NoGo visual discrimination tasks (one requiring well-learned responses, two requiring learning) demonstrated that the majority of prefrontal neurons were 'functionally stable'. Action potentials of 94 neurons were stable over 2-9 days; 66/94 (70%) of these cells responded each day, 22/94 (23%) never responded significantly, and 6/94 (6%) responded one day but not the next. Of 66 responsive neurons, 55 were selective for either Go or NoGo trials, individual stimuli, or eye movements." (Greenberg and Wilson, 2004) Selectivity was maintained, for 46/55 neurons across all recording days. Response strength (baseline vs. post-stimulation firing rates) and event-related response timing also displayed stability. Stability generalized across neuronal response type suggesting that functional stability is a general property. Long-term recordings from other studies supported similar conclusions suggesting that neurons throughout the brain are functionally stable. Single-day recordings from different neurons within the same cortical regions demonstrated neuronal response flexibility while monkeys learned associations among visual cues, and Go/NoGo behavioral responses. Of 116 neurons, 57 (49%) displayed significant change points in firing rates during novel learning (n=18), reversal learning (n=12), or both tasks (n=27). Six of 57(10.5%) neurons had firing rates changes prior to learning and might have been causally related to the monkeys' behavioral changes. However, only 18/152 (12%) of the total number of firing rate changes occurred prior to the monkeys' learning meaning that most appeared to be the consequences of learning rather than the causes.

In a rapidly changing visual environment, the delay between perception and action might impair the probability of survival of a prey or the efficiency of a predator. In order to compensate for delays associated with sensory-motor processing, primates often make predictions about future events and initiate anticipatory movements. To prepare an anticipatory movement, an estimation of when and where to a target is likely to move is necessary. Such an internal representation is often termed 'expectation'. The aim of this thesis was to investigate the gradual changes of a subject's expectation at the behavioral and electrophysiological levels. Anticipatory smooth pursuit was used in order to study temporal and directional changes in expectation. We found that temporal uncertainty strongly modulated the latency and the velocity of anticipatory movements suggesting that monkeys could estimate the hazard rate of target motion onset in order to decide when to initiate an anticipatory movement. In addition, we have shown that monkeys could use prior directional information in order to voluntarily initiate anticipatory responses in the direction of expected target motion. This prior directional information significantly affected the latency and velocity of these movements. Finally, we have shown that the majority of recorded supplementary eye field (SEF) neurons encoded expected target motion direction. The presence of a directional cue induced an increase of activity in the preferred direction of the neuron. Moreover, a large sub-population of neurons encoded the direction of future anticipatory movement. These results suggest that the SEF could be involved in the cognitive control of anticipatory pursuit eye movements when prior temporal and directional information is provided.

abstract: Recent new experiments showed that wide-field imaging at millimeter scale is capable of recording hundreds of neurons in behaving mice brain. Monitoring hundreds of individual neurons at a high frame rate provides a promising tool for discovering spatiotemporal features of large neural networks. However, processing the massive data sets is impossible without automated procedures. Thus, this thesis aims at developing a new tool to automatically segment and track individual neuron cells. The new method used in this study employs two major ideas including feature extraction based on power spectral density of single neuron temporal activity and clustering tree to separate overlapping cells. To address issues associated with high-resolution imaging of a large recording area, focused areas and out-of-focus areas were analyzed separately. A static segmentation with a fixed PSD thresholding method is applied to within focus visual field. A dynamic segmentation by comparing maximum PSD with surrounding pixels is applied to out-of-focus area. Both approaches helped remove irrelevant pixels in the background. After detection of potential single cells, some of which appeared in groups due to overlapping cells in the image, a hierarchical clustering algorithm is applied to separate them. The hierarchical clustering uses correlation coefficient as a distance measurement to group similar pixels into single cells. As such, overlapping cells can be separated. We tested the entire algorithm using two real recordings with the respective truth carefully determined by manual inspections. The results show high accuracy on tested datasets while false positive error is controlled within an acceptable range. Furthermore, results indicate robustness of the algorithm when applied to different image sequences. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2016

Final publication is available at http://dx.doi.org/10.1111/ejn.12882 / 京都大学 / 0048 / 新制・論文博士 / 博士(医学) / 乙第13040号 / 論医博第2115号 / 新制||医||1017(附属図書館) / 33032 / 京都大学大学院医学研究科医学専攻 / (主査)教授 渡邉 大, 教授 影山 龍一郎, 教授 髙橋 良輔 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

A neurociência computacional é uma vasta área que tem como objeto de estudo o entendimento ou a emulação da dinâmica cerebral em diversos níveis. Neste trabalho atenta-se ao estudo da dinâmica de neurônios, os quais, no consenso atual, acredita-se serem as unidades fundamentais do processamento cerebral. A importância do estudo sobre o comportamento de neurônios se encontra na diversidade de propriedades que eles podem apresentar. O estudo se torna mais rico quando há interações de sistemas internos ao neurônio em diferentes escalas de tempo, criando propriedades como adaptação, latência e comportamento em rajada, o que pode acarretar em diferentes papéis que os neurônios podem ter na rede. Nesta dissertação é feita uma análise sob o ponto de vista de sistemas dinâmicos e de análise de sensibilidade de seis modelos ao estilo de Hodgkin-Huxley e compartimentais de neurônios encontrados no córtex visual primário de mamíferos. Esses modelos correspondem a seis classes eletrofisiológicas de neurônios corticais e o estudo feito nesta dissertação oferece uma contribuição ao entendimento dos princípios de sistemas dinâmicos subjacentes a essa classificação. / Computational neuroscience is a vast scientific area which has as subject of study the unsderstanding or emulation of brain dynamics at different levels. This work studies the dynamics of neurons, which are believed, according to present consensus, to be the fundamental processing units of the brain. The importance of studying neuronal behavior comes from the diversity of properties they may have. This study becomes richer when there are interactions between distintic neuronal internal systems, in different time scales, creating properties like adaptation, latency and bursting, resulting in different roles that neurons may have in the network. This dissertation contains a study of six reduced compartmental conductance-based models of neurons found in the primary visual cortex of mammals under the dynamical systems and sensitivity analysis viewpoints. These models correspond to six eletrophysiological classes of cortical neurons and this dissertation offers a contribution to the understanding of the dynamical-systems principles underlying such classification.

A neurociência computacional é uma vasta área que tem como objeto de estudo o entendimento ou a emulação da dinâmica cerebral em diversos níveis. Neste trabalho atenta-se ao estudo da dinâmica de neurônios, os quais, no consenso atual, acredita-se serem as unidades fundamentais do processamento cerebral. A importância do estudo sobre o comportamento de neurônios se encontra na diversidade de propriedades que eles podem apresentar. O estudo se torna mais rico quando há interações de sistemas internos ao neurônio em diferentes escalas de tempo, criando propriedades como adaptação, latência e comportamento em rajada, o que pode acarretar em diferentes papéis que os neurônios podem ter na rede. Nesta dissertação é feita uma análise sob o ponto de vista de sistemas dinâmicos e de análise de sensibilidade de seis modelos ao estilo de Hodgkin-Huxley e compartimentais de neurônios encontrados no córtex visual primário de mamíferos. Esses modelos correspondem a seis classes eletrofisiológicas de neurônios corticais e o estudo feito nesta dissertação oferece uma contribuição ao entendimento dos princípios de sistemas dinâmicos subjacentes a essa classificação. / Computational neuroscience is a vast scientific area which has as subject of study the unsderstanding or emulation of brain dynamics at different levels. This work studies the dynamics of neurons, which are believed, according to present consensus, to be the fundamental processing units of the brain. The importance of studying neuronal behavior comes from the diversity of properties they may have. This study becomes richer when there are interactions between distintic neuronal internal systems, in different time scales, creating properties like adaptation, latency and bursting, resulting in different roles that neurons may have in the network. This dissertation contains a study of six reduced compartmental conductance-based models of neurons found in the primary visual cortex of mammals under the dynamical systems and sensitivity analysis viewpoints. These models correspond to six eletrophysiological classes of cortical neurons and this dissertation offers a contribution to the understanding of the dynamical-systems principles underlying such classification.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24505号 / 医博第4947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 渡邉 大, 教授 林 康紀, 教授 井上 治久 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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Die Aktionspotential (AP) -Aktivität einzelner kortikaler Neuronen kann messbare sensorische Effekte hervorrufen. Es ist jedoch nicht bekannt, wie AP-Sequenzen Parameter und spezifische neuronale Subtypen die hervorgerufenen Sinnesempfindungen beeinflussen. Hier haben wir einen ‘Reverse-Physiology‘ Ansatz angewendet, um die Beziehung zwischen der Aktivität einzelner Neuronen und der Empfindung zu untersuchen. Zunächst wird der Prozess der Nanostimulation, eine von der juxtazellulären Markierungstechnik abgeleiteten Einzelzell-Stimulationsmethode, detailliert beschrieben. Nanostimulation ist einfach anzuwenden und kann auf eine Vielzahl von identifizierbaren Neuronen in narkotisierten und wachen Tieren angewandt werden. Wir beschreiben die Aufnahmetechnik und die elektrische Konfiguration für Nanostimulation. Während eine exakte zeitliche Bestimmung der AP nicht erreicht wurde, konnten Frequenz und Anzahl der AP parametrisch kontrolliert werden. Wir zeigen, dass Nanostimulation auch angewendet werden kann, um sensorische Reaktionen in identifizierbaren Neuronen selektiv zu inhibieren. Als nächstes haben wir untersucht wie sich die Frequenz und Anzahl der AP sowie die Regelmäßigkeit der Pulsfolge auf die Detektion von Einzelzell-Stimulationen im somatosensorischen Kortex von Ratten auswirken. Für mutmaßlichen erregende regular-spiking Neuronen erhöhte sich die Nachweisbarkeit mit abnehmender Frequenz und Anzahl der AP. Die Stimulation einzelner, mutmaßlichen inhibitorischer und schnell feuernder Neuronen führte zu wesentlich stärkeren sensorischen Effekten, die unabhängig von Frequenz und Anzahl der AP waren. Außerdem fanden wir heraus, dass Unregelmäßigkeiten der Pulsfolge die sensorischen Effekte von putativ erregenden Neuronen stark erhöhten. Diese Unregelmäßigkeiten wurden in durchschnittlich 8% der Durchgänge festgestellt. Unsere Daten deuten darauf hin, dass das es auf Verhaltnisebene eine große Sensivität für kortikale AP und deren zeitlichen Abfolge gibt. / The action potential (AP) activity of single cortical neurons can evoke measurable sensory effects, but it is not known how spiking parameters and specific neuronal subtypes affect the evoked sensations. Here we applied a reverse physiology approach to investigate the relationship between single neuron activity and sensation. First, we provide a detailed description of the procedures involved in nanostimulation, a single-cell stimulation method derived from the juxtacellular labeling technique. Nanostimulation is easy to apply and can be directed to a wide variety of identifiable neurons in anesthetized and awake animals. We describe the recording approach and the parameters of the electric configuration underlying nanostimulation. While exact AP timing has not been achieved, AP frequency and AP number can be parametrically controlled. We demonstrate that nanostimulation can also be used to selectively inhibit sensory responses in identifiable neurons. Next, we examined the effects of AP frequency, AP number and spike train regularity on the detectability of single-cell stimulation in rat somatosensory cortex. For putative excitatory, regular spiking neurons detectability increased with decreasing AP frequencies and decreasing AP numbers. Stimulation of single putative inhibitory, fast spiking neurons led to much larger sensory effects that were not dependent on AP frequency and AP number. In addition, we found that spike train irregularity greatly increased the sensory effects of putative excitatory neurons, with irregular spike trains being detected in on average 8% of trials. Our data suggest that the behaving animal is extremely sensitive to cortical APs and their temporal patterning.