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The Fisher information constitutes a natural measure for the sensitivity of a probability distribution with respect to a set of parameters. An implementation of the stationarity principle for synaptic learning in terms of the Fisher information results in a Hebbian self-limiting learning rule for synaptic plasticity. In the present work, we study the dependence of the solutions to this rule in terms of the moments of the input probability distribution and find a preference for non-Gaussian directions, making it a suitable candidate for independent component analysis (ICA). We confirm in a numerical experiment that a neuron trained under these rules is able to find the independent components in the non-linear bars problem. The specific form of the plasticity rule depends on the transfer function used, becoming a simple cubic polynomial of the membrane potential for the case of the rescaled error function. The cubic learning rule is also an excellent approximation for other transfer functions, as the standard sigmoidal, and can be used to show analytically that the proposed plasticity rules are selective for directions in the space of presynaptic neural activities characterized by a negative excess kurtosis.
We present an effective model for timing-dependent synaptic plasticity (STDP) in terms of two interacting traces, corresponding to the fraction of activated NMDA receptors and the concentration in the dendritic spine of the postsynaptic neuron. This model intends to bridge the worlds of existing simplistic phenomenological rules and highly detailed models, thus constituting a practical tool for the study of the interplay of neural activity and synaptic plasticity in extended spiking neural networks. For isolated pairs of pre- and postsynaptic spikes, the standard pairwise STDP rule is reproduced, with appropriate parameters determining the respective weights and timescales for the causal and the anticausal contributions. The model contains otherwise only three free parameters, which can be adjusted to reproduce triplet nonlinearities in hippocampal culture and cortical slices. We also investigate the transition from time-dependent to rate-dependent plasticity occurring for both correlated and uncorrelated spike patterns.
Generating functionals may guide the evolution of a dynamical system and constitute a possible route for handling the complexity of neural networks as relevant for computational intelligence.We propose and explore a new objective function, which allows to obtain plasticity rules for the afferent synaptic weights. The adaption rules are Hebbian, self-limiting, and result from the minimization of the Fisher information with respect to the synaptic flux. We perform a series of simulations examining the behavior of the new learning rules in various circumstances.The vector of synaptic weights aligns with the principal direction of input activities, whenever one is present. A linear discrimination is performed when there are two or more principal directions; directions having bimodal firing-rate distributions, being characterized by a negative excess kurtosis, are preferred. We find robust performance and full homeostatic adaption of the synaptic weights results as a by-product of the synaptic flux minimization. This self-limiting behavior allows for stable online learning for arbitrary durations.The neuron acquires new information when the statistics of input activities is changed at a certain point of the simulation, showing however, a distinct resilience to unlearn previously acquired knowledge. Learning is fast when starting with randomly drawn synaptic weights and substantially slower when the synaptic weights are already fully adapted.
Polo-like kinase 1 regulates the stability of the mitotic centromere-associated kinesin in mitosis
(2014)
Proper bi-orientation of chromosomes is critical for the accurate segregation of chromosomes in mitosis. A key regulator of this process is MCAK, the mitotic centromere-associated kinesin. During mitosis the activity and localization of MCAK are regulated by mitotic key kinases including Plk1 and Aurora B. We show here that S621 in the MCAK’s C-terminal domain is the major phosphorylation site for Plk1. This phosphorylation regulates MCAK’s stability and facilitates its recognition by the ubiquitin/proteasome dependent APC/CCdc20 pathway leading to its D-box dependent degradation in mitosis. While phosphorylation of S621 does not directly affect its microtubule depolymerising activity, loss of Plk1 phosphorylation on S621 indirectly enhances its depolymerization activity in vivo by stabilizing MCAK, leading to an increased level of protein. Interfering with phosphorylation at S621 causes spindle formation defects and chromosome misalignments. Therefore, this study suggests a new mechanism by which Plk1 regulates MCAK: by regulating its degradation and hence controlling its turnover in mitosis.
Global Governance ist in: Klingt neuartig, global ist eigentlich auch alles und wer will sich schon vorwerfen lassen, noch im „methodologischen Nationalismus“ verhaftet zu sein? Dies ist überspitzt formuliert, doch wie sich zeigte nicht ganz unberechtigt. Nichtsdestotrotz waren die OrganisatorInnen des ersten Weltkongresses der International Association of Political Science Students (IAPSS) wohl selbst überrascht als zur Tagung mit dem Titel „The Limits of Global Governance“ rund 200 Anmeldungen von Studierenden kamen. In Thessaloniki, der europäischen Jugendhauptstadt 2014, wurde vier Tage über Global Governance debattiert und in den insgesamt sieben studentischen Panels, zahlreichen Vorträgen von etablierten WissenschaftlerInnen und weiteren Veranstaltungen zeigte sich vor allem die Vielfalt und Breite des Themas. Wir werden daher im Folgenden Schlaglichter auf interessante Veranstaltungen und Inhalte werfen und abschließend ein kritisch-konstruktives Fazit der Tagung ziehen.