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11	Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model Gönner, Lorenz, Vitay, Julien, Hamker, Fred 23 November 2017 (has links) (PDF) Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, we propose a model of spatial learning and sequence generation as interdependent processes, integrating cortical contextual coding, synaptic plasticity and neuromodulatory mechanisms into a map-based approach. Following goal learning, sequential activity emerges from continuous attractor network dynamics biased by goal memory inputs. We apply Bayesian decoding on the resulting spike trains, allowing a direct comparison with experimental data. Simulations show that this model (1) explains the generation of never-experienced sequence trajectories in familiar environments, without requiring virtual self-motion signals, (2) accounts for the bias in place-cell sequences toward goal locations, (3) highlights their utility in flexible route planning, and (4) provides specific testable predictions. Neuronale Aktivitätssequenzen Lernen durch Belohnung Zielgedächtnis Kontexteinfluss Gedächtnisabruf Attraktornetzwerk Bayes-Dekodierung sequential activity reward-based learning goal memory contextual bias memory recall continuous attractor network Bayesian decoding ddc:004 ddc:150 Neuronales Modell Sequenz Lernen Belohnung Zielbildung Kontexteffekt Gedächtnis Netzwerkmodell
12	The taxonomy of Crowdfunding - An actualized overview of the development of internet crowdfunding models Tillberg, Fredrik January 2019 (has links) Crowdfunding challenges century long boundaries between the public, the industry andinnovation. In that respect the phenomenon holds the potential to decentralize and democratizethe way ventures are financed and realized. Crowdfunding has seen a lot of exitingdevelopments during the last few years, partly because of new crowdfunding platformsemerging on the internet, and partly because of new ground-breaking technology being used forfunding purposes. Meanwhile research has not quite catched up with the recent developments ofdifferent models for crowdfunding. This study’s aim is therefor to give an comprehensiveoverview of the different models of crowdfunding that are being utilized by crowdfundingplatforms on the internet today. A deductive content analysis has been made of 67 currentcrowdfunding platforms. The platforms have been analysed in order to determine what model ofcrowdfunding they utilize. The result has, apart from partly confirming prior studies, alsoproduced new exiting findings on what mechanisms constitute some of the crowdfundingmodels we see today. A new taxonomy of crowdfunding models is discussed and proposed. Theconclusion is that the need for a updated taxonomy, like the one this study provides, was wellneeded in order to understand the field. One important finding is that blockchain technology hasproduced a new form of crowdfunding through cryptocurrency: Initial coin offering. Thatparticular area will likely develop and continue to decentralize and democratise the economicalhuman interaction when it comes to financing. Crowdfunding crowdfunding platform campaign owner crowdfunder crowdfunding model taxonomy donation-based reward-based equity-based lending-based royalty-based hybrid model subscription-based bounty-based blockchain technology smart contracts dApps Initial coin offering ICO Engineering and Technology Teknik och teknologier
13	Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model Gönner, Lorenz, Vitay, Julien, Hamker, Fred January 2017 (has links) Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, we propose a model of spatial learning and sequence generation as interdependent processes, integrating cortical contextual coding, synaptic plasticity and neuromodulatory mechanisms into a map-based approach. Following goal learning, sequential activity emerges from continuous attractor network dynamics biased by goal memory inputs. We apply Bayesian decoding on the resulting spike trains, allowing a direct comparison with experimental data. Simulations show that this model (1) explains the generation of never-experienced sequence trajectories in familiar environments, without requiring virtual self-motion signals, (2) accounts for the bias in place-cell sequences toward goal locations, (3) highlights their utility in flexible route planning, and (4) provides specific testable predictions. info:eu-repo/classification/ddc/004 ddc:004 info:eu-repo/classification/ddc/150 ddc:150

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