Sequence learning in Associative Neuronal-Astrocytic Network

The neuronal paradigm of studying the brain has left us with limitations in both our understanding of how neurons process information to achieve biological intelligence and how such knowledge may be translated into artificial intelligence and its most brain-derived branch, neuromorphic computing. Overturning our fundamental assumptions of how the brain works, the recent exploration of astrocytes is revealing that these long-neglected brain cells dynamically regulate learning by interacting with neuronal activity at the synaptic level. Following recent experimental evidence, we designed an associative, Hopfield-type, neuronal-astrocytic network and analyzed the dynamics of the interaction between neurons and astrocytes. We show that astrocytes were sufficient to trigger transitions between learned memories in the neuronal component of the network. Further, we mathematically derived the timing of the transitions that was governed by the dynamics of the calcium-dependent slow-currents in the astrocytic processes. Overall, we provide a brain-morphic mechanism for sequence learning that is inspired by, and aligns with, recent experimental findings. To evaluate our model, we emulated astrocytic atrophy and showed that memory recall becomes significantly impaired after a critical point of affected astrocytes was reached. This brain-inspired and brain-validated approach supports our ongoing efforts to incorporate non-neuronal computing elements in neuromorphic information processing.Comment: 8 pages, 5 figure

A brain-inspired cognitive system that mimics the dynamics of human thought

In recent years, some impressive AI systems have been built that can play games and answer questions about large quantities of data. However, we are still a very long way from AI systems that can think and learn in a human-like way. We have a great deal of information about how the brain works and can simulate networks of hundreds of millions of neurons. So it seems likely that we could use our neuroscientific knowledge to build brain-inspired artificial intelligence that acts like humans on similar timescales. This paper describes an AI system that we have built using a brain-inspired network of artificial spiking neurons. On a word recognition and colour naming task our system behaves like human subjects on a similar timescale. In the longer term, this type of AI technology could lead to more flexible general purpose artificial intelligence and to more natural human-computer interaction

Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

Background: Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients\u2019 clinical phenotypes and analyse the differential clinical course. Methods: We performed a hierarchical cluster analysis based on Ward\u2019s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results: A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients\u2019 prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P <.001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27\u20133.62; HR 3.42, 95%CI 2.72\u20134.31; HR 2.79, 95%CI 2.32\u20133.35), and Cluster 1 (HR 1.88, 95%CI 1.48\u20132.38; HR 2.50, 95%CI 1.98\u20133.15; HR 2.09, 95%CI 1.74\u20132.51) reported a higher risk for the three outcomes respectively. Conclusions: In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes