title: Phase transitions in single neurons and neural populations: Critical slowing, anesthesia, and sleep cycles

authors: Steyn-Ross, D. Alistair

subjects: subthreshold oscillation

abstract: The firing of an action potential by a biological neuron represents a dramatic transition from small-scale linear stochastics (subthreshold voltage fluctuations) to gross-scale nonlinear dynamics (birth of a 1-ms voltage spike). In populations of neurons we see similar, but slower, switch-like there-and-back transitions between low-firing background states and high-firing activated states. These state transitions are controlled by varying levels of input current (single neuron), varying amounts of GABAergic drug (anesthesia), or varying concentrations of neuromodulators and neurotransmitters (natural sleep), and all occur within a milieu of unrelenting biological noise. By tracking the altering responsiveness of the excitable membrane to noisy stimulus, we can infer how close the neuronal system (single unit or entire population) is to switching threshold. We can quantify this “nearness to switching” in terms of the altering eigenvalue structure: the dominant eigenvalue approaches zero, leading to a growth in correlated, low-frequency power, with exaggerated responsiveness to small perturbations, the responses becoming larger and slower as the neural population approaches its critical point–-this is critical slowing. In this chapter we discuss phase-transition predictions for both single-neuron and neural-population models, comparing theory with laboratory and clinical measurement.

text: Phase transitions in single neurons and neural populations: Critical slowing, anesthesia, and sleep cycles The firing of an action potential by a biological neuron represents a dramatic transition from small-scale linear stochastics (subthreshold voltage fluctuations) to gross-scale nonlinear dynamics (birth of a 1-ms voltage spike). In populations of neurons we see similar, but slower, switch-like there-and-back transitions between low-firing background states and high-firing activated states. These state transitions are controlled by varying levels of input current (single neuron), varying amounts of GABAergic drug (anesthesia), or varying concentrations of neuromodulators and neurotransmitters (natural sleep), and all occur within a milieu of unrelenting biological noise. By tracking the altering responsiveness of the excitable membrane to noisy stimulus, we can infer how close the neuronal system (single unit or entire population) is to switching threshold. We can quantify this “nearness to switching” in terms of the altering eigenvalue structure: the dominant eigenvalue approaches zero, leading to a growth in correlated, low-frequency power, with exaggerated responsiveness to small perturbations, the responses becoming larger and slower as the neural population approaches its critical point–-this is critical slowing. In this chapter we discuss phase-transition predictions for both single-neuron and neural-population models, comparing theory with laboratory and clinical measurement.

year: 2010

title: Big data analytics role in shaping the work of accounting function and accounting professionals

authors: Mukherjee, Abhishek

abstract: Purpose – This paper aims to examine how big data can be applied in accounting and identifies the competences future accountants at organizational level need to possess to remain competitive. Design/methodology/approach – This study used the association of chartered certified accountants (ACCA’s) framework and the technological, organization and environmental (ST.O.E.TOE) theoretical framework to identify the skills and capabilities urgently needed by accounting professionals at the organizational level in the context of big data analytics. The authors conducted comprehensive content analysis of academic literature issues from professional accounting bodies, and reports of accounting practitioners to gain insights of competencies that accounting professionals at organizational level need to possess to be prominent in the big data era. Findings – The findings show that in the big data era accounting professionals should acquire the seven competences to be competitive. The competencies are (1) skills in data analytic techniques and the use of statistical models; (2) knowledge of the business; (3) skills to control the risk from the emerging technologies; (4) skills and knowledge in accounting profession; (5) skills to understand the insights from big data analytics; (6) skills to test the quality, veracity and integrity of data; and (7) skills to communicate with others. The study also found that these competences do not exist alone. These competences are closely related to each other and complement each other, which means that accounting professionals cannot expect to stand out in the future competition by mastering only one or two of these seven competences. Research limitations/implications – The application of big data analytics is still in its infancy and its effects have yet to be confirmed so that there is no reliable case to collect and analyse. One limitation is the absence of practical case in this paper. The findings will help accounting professionals to understand the future of work. Practical implications – The findings will help accounting professionals to acquire new skills and knowledge in related areas and remain competitive in the post big data era. Social implications – This study highlights skills necessary for accountants to thrive in a big data era, emphasizing the shift from traditional accounting functions to roles that integrate advanced data capabilities and strategic insight. Originality/value – This is one of the few papers that seek to explore the competencies required by accounting professional at organizational level in the big data era using the ACCA’s research of accountant’s professional quotients and ST.O.E.TOE theoretical framework. This paper seeks to contribute to the literature on accounting professionals’ competencies at organizational level in the big data era. Big data analytics in accounting is influencing the evolution of accounting professionals’ competencies and is a key interest in accounting change literature. The nature of accounting work will undergo fundamental changes due to big data. Yet we know little about this due to big data being in its infancy stage in the accounting literature.

text: Big data analytics role in shaping the work of accounting function and accounting professionals Purpose – This paper aims to examine how big data can be applied in accounting and identifies the competences future accountants at organizational level need to possess to remain competitive. Design/methodology/approach – This study used the association of chartered certified accountants (ACCA’s) framework and the technological, organization and environmental (ST.O.E.TOE) theoretical framework to identify the skills and capabilities urgently needed by accounting professionals at the organizational level in the context of big data analytics. The authors conducted comprehensive content analysis of academic literature issues from professional accounting bodies, and reports of accounting practitioners to gain insights of competencies that accounting professionals at organizational level need to possess to be prominent in the big data era. Findings – The findings show that in the big data era accounting professionals should acquire the seven competences to be competitive. The competencies are (1) skills in data analytic techniques and the use of statistical models; (2) knowledge of the business; (3) skills to control the risk from the emerging technologies; (4) skills and knowledge in accounting profession; (5) skills to understand the insights from big data analytics; (6) skills to test the quality, veracity and integrity of data; and (7) skills to communicate with others. The study also found that these competences do not exist alone. These competences are closely related to each other and complement each other, which means that accounting professionals cannot expect to stand out in the future competition by mastering only one or two of these seven competences. Research limitations/implications – The application of big data analytics is still in its infancy and its effects have yet to be confirmed so that there is no reliable case to collect and analyse. One limitation is the absence of practical case in this paper. The findings will help accounting professionals to understand the future of work. Practical implications – The findings will help accounting professionals to acquire new skills and knowledge in related areas and remain competitive in the post big data era. Social implications – This study highlights skills necessary for accountants to thrive in a big data era, emphasizing the shift from traditional accounting functions to roles that integrate advanced data capabilities and strategic insight. Originality/value – This is one of the few papers that seek to explore the competencies required by accounting professional at organizational level in the big data era using the ACCA’s research of accountant’s professional quotients and ST.O.E.TOE theoretical framework. This paper seeks to contribute to the literature on accounting professionals’ competencies at organizational level in the big data era. Big data analytics in accounting is influencing the evolution of accounting professionals’ competencies and is a key interest in accounting change literature. The nature of accounting work will undergo fundamental changes due to big data. Yet we know little about this due to big data being in its infancy stage in the accounting literature.

year: 2025

title: Te tukuihotanga o te reo Māori: He aronga motuhake – ngā mokopuna a Te Kapunga

authors: Dewes, Te Kuru o te Marama

subjects: Reo Māori

abstract: Ko te tāhū o tēnei rangahau ko te reo Māori. Ko te ariā matua ko te tukuihotanga o te reo Māori, arā, ko te tuku iho o te reo ki rō whānau mai i ngā mātua ki ngā tamariki, ā, tae rawa iho ki ngā mokopuna. Kia whakawhāiti i te kaupapa, he aronga motuhake tēnei kei te titiro hāngai atu ki ngā whānau e whā ki ngā tāone e toru o Te Whanganui-a-Tara, Tāmakimakaurau me Te Rotorua-Nui-a-Kahumatamomoe. Nā te hurahura i ngā āhuatanga reo i roto i ngā kāenga o ngā whānau e whā i kitea mehemea kei te tutuki tēnei tūāhua te tukuihotanga o te reo Māori. Tuia, he aha ngā momo āhuatanga kei te taunaki i tēnei mahi, ā, he aha hoki ngā ārai e whakahōtaetae ana i a ia.  piti atu, i noho ki te ketuketu i ngā whakaaro o ngā mātua e pā ana ki tēnei kaupapa te tuku iho i te reo ki ngā tamariki mokopuna. Kāre i aro ki te kounga o te reo, he kaute noa i ngā whakamahinga reo Māori me whakamahinga reo Pākehā. Kia whakaritea ngā āhuatanga reo o ngā whānau me ngā tamariki, i tū ngā mahi whakarongo ki ngā wāhi e toru, arā, ki rō kāenga, ki rō kura, ka mutu, i tū ki wāhi kē atu hoki. Nā runga i te whakaaro kei te mōrearea tonu te reo Māori, kua herea tēnei kaupapa ki te ahurea me te tuakiri Māori, te mana motuhake me te tino rangatiratanga.

text: Te tukuihotanga o te reo Māori: He aronga motuhake – ngā mokopuna a Te Kapunga Ko te tāhū o tēnei rangahau ko te reo Māori. Ko te ariā matua ko te tukuihotanga o te reo Māori, arā, ko te tuku iho o te reo ki rō whānau mai i ngā mātua ki ngā tamariki, ā, tae rawa iho ki ngā mokopuna. Kia whakawhāiti i te kaupapa, he aronga motuhake tēnei kei te titiro hāngai atu ki ngā whānau e whā ki ngā tāone e toru o Te Whanganui-a-Tara, Tāmakimakaurau me Te Rotorua-Nui-a-Kahumatamomoe. Nā te hurahura i ngā āhuatanga reo i roto i ngā kāenga o ngā whānau e whā i kitea mehemea kei te tutuki tēnei tūāhua te tukuihotanga o te reo Māori. Tuia, he aha ngā momo āhuatanga kei te taunaki i tēnei mahi, ā, he aha hoki ngā ārai e whakahōtaetae ana i a ia.  piti atu, i noho ki te ketuketu i ngā whakaaro o ngā mātua e pā ana ki tēnei kaupapa te tuku iho i te reo ki ngā tamariki mokopuna. Kāre i aro ki te kounga o te reo, he kaute noa i ngā whakamahinga reo Māori me whakamahinga reo Pākehā. Kia whakaritea ngā āhuatanga reo o ngā whānau me ngā tamariki, i tū ngā mahi whakarongo ki ngā wāhi e toru, arā, ki rō kāenga, ki rō kura, ka mutu, i tū ki wāhi kē atu hoki. Nā runga i te whakaaro kei te mōrearea tonu te reo Māori, kua herea tēnei kaupapa ki te ahurea me te tuakiri Māori, te mana motuhake me te tino rangatiratanga.

year: 2017