Network self-organization explains the statistics and dynamics of synaptic connection strengths in cortex

The information processing abilities of neural circuits arise from their synaptic connection patterns. Understanding the laws governing these connectivity patterns is essential for understanding brain function. The overall distribution of synaptic strengths of local excitatory connections in cortex and hippocampus is long-tailed, exhibiting a small number of synaptic connections of very large efficacy. At the same time, new synaptic connections are constantly being created and individual synaptic connection strengths show substantial fluctuations across time. It remains unclear through what mechanisms these properties of neural circuits arise and how they contribute to learning and memory. In this study we show that fundamental characteristics of excitatory synaptic connections in cortex and hippocampus can be explained as a consequence of self-organization in a recurrent network combining spike-timing-dependent plasticity (STDP), structural plasticity and different forms of homeostatic plasticity. In the network, associative synaptic plasticity in the form of STDP induces a rich-get-richer dynamics among synapses, while homeostatic mechanisms induce competition. Under distinctly different initial conditions, the ensuing self-organization produces long-tailed synaptic strength distributions matching experimental findings. We show that this self-organization can take place with a purely additive STDP mechanism and that multiplicative weight dynamics emerge as a consequence of network interactions. The observed patterns of fluctuation of synaptic strengths, including elimination and generation of synaptic connections and long-term persistence of strong connections, are consistent with the dynamics of dendritic spines found in rat hippocampus. Beyond this, the model predicts an approximately power-law scaling of the lifetimes of newly established synaptic connection strengths during development. Our results suggest that the combined action of multiple forms of neuronal plasticity plays an essential role in the formation and maintenance of cortical circuits

Вищі карбонові кислоти олії насіння чорниці звичайної (Vaccinium myrtillus L.)

The bilberry bush is a relative of the blueberry and is native to many areas, including the Rocky Mountains and regions of Europe and Asia.  Its berries and leaves have been used for medicinal purposes since the Middle Ages for a variety of conditions. Today, bilberry is used as a dietary supplement for cardiovascular conditions, diarrhea, urinary tract infections, eye problems, diabetes, and other conditions. Researchers are interested in bilberry in large part because its berries have a high concentration of antioxidants called anthocyanins, which some studies suggest may have health benefits. However there are other important substances contained in this plant. In these studies, we used blueberries collected in the Volyn region near the village of Krichevichi. From pure bilberry seeds (V. myrtillus), using n-hexane extraction, an oil of light yellow color with a refractive index of 1,4742 was obtained. The method of gas-liquid chromatography determined the fatty acid composition of the oil of bilberry seed. It has been established that the oil under study contains a high content of oleic (23,7 %), linoleic (38,1 %) and linolenic (31,1 %) acids. In minor quantities, blueberries contain palmitic (5,3 %), stearic (1,0 %) and myristic (0,7 %) acids.Із насіння чорниці звичайної (Vaccinium myrtillus L.) методом вичерпної екстракції н-гексаном отримано олію світло-жовтого кольору з показником заломлення 1,4742. Вихід станвить 18 %. Методом газорідинної хроматографії визначено жирнокислотний склад олії насіння чорниці звичайної. Установлено, що досліджувана олія складається високої кількості олеїнової (23,7 %), лінолевої (38,1 %) та ліноленової (31,1 %) кислот. У мінорних кількостях олія чорниці містить пальмітинову (5,3 %), стеаринову (1,0 %) та міристинову (0,7 %) кислоти

Final report key contents: main results accomplished by the EU-Funded project IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots

This document has the goal of presenting the main scientific and technological achievements of the project IM-CLeVeR. The document is organised as follows: 1. Project executive summary: a brief overview of the project vision, objectives and keywords. 2. Beneficiaries of the project and contacts: list of Teams (partners) of the project, Team Leaders and contacts. 3. Project context and objectives: the vision of the project and its overall objectives 4. Overview of work performed and main results achieved: a one page overview of the main results of the project 5. Overview of main results per partner: a bullet-point list of main results per partners 6. Main achievements in detail, per partner: a throughout explanation of the main results per partner (but including collaboration work), with also reference to the main publications supporting them

Formation of Structure in Cortical Networks through Spike Timing-Dependent Plasticity

The connectivity of mammalian brains exhibits structure at a wide variety of spatial scales, from the broad (which brain areas connect to which) to the extremely fine (where synapses form on the morphology of individual neurons). Two striking features of the neuron-to- neuron connectivity are 1) the strong over-representation of multi-synapse connectivity pat- terns compared to simple random-network models and 2) a strong relationship between neurons’ local connectivity and their stimulus preferences, so that local network structure plays a large role in the computations neurons perform. A central question in systems neu- roscience is how such structures emerge. Answers to this question are confounded by the mutual interactions of neuronal activity and neural network structure. Patterns of synaptic connectivity influence neurons’ joint activity, while the synapses between neurons are plastic and strengthen or weaken depending on the activity of the pre- and postsynaptic neurons. In this thesis, I develop a self-consistent framework for the coevolution of network struc- ture and spiking activity. Subsequent chapters leverage this to develop low-dimensional sets of equations that directly describe the plasticity of connectivity patterns in large spiking networks. I examine plasticity during spontaneous activity and then how the structure of external stimuli can shape network structure and subsequent spontaneous plasticity. These studies provide a step towards understanding how the structure of neuronal networks and neurons’ joint activity interact to allow network computations