New Results in Neuroscience Based AI for Memory and Learning
In our own work on NeuroAI we aim to leverage knowledge about the brain — here the cortex — for structural learning, i.e. for learning that is fast, efficient and successful because it uses the pre-existing neuronal structures which have evolved over millions of years. In the terms of classical AI, what we have done Read more..
Read MoreThe convergence
There used to be the concept of a “singularity”. The idea was that computers would become smarter than humans and start to replace them. Even the idea that humanity would be substituted by silicon-based computing machines (robots) was suggested. Against that two years ago we set the concept of “The convergence”. This assumes that biological Read more..
Read MoreInterview with Gabriele Scheler: Neuro AI. Will it be the future?
Here is an interview concerning the current AI and generative AI waves, and their relation to neuroscience. We propose solutions based on new technology from neuroAI – which includes humans ability for reasoning, thought, logic, mathematics, proof etc. – and are therefore poorly modeled by data analysis on its own. Some of our work – Read more..
Read MoreIn Vivo Analysis of Heterogeneous Extracellular Vesicles Using a Red-Shifted Bioluminescence Resonance Energy Transfer Reporter Protein
Check out a new paper from scientific director Michael Bachmann, MD.
Read MoreAnother blog entry on medium
Another blog entry on medium: “Engineering the brain”. There was no intelligent design, and as a result, body organs do not resemble machines. Once we start building machines like body organs — with utility functions, self-organization and cells as building blocks, we can mesh engineering and evolutionary principles to arrive at better organisms.
Read MoreSketch of a novel approach to a neural model
We present a novel model of neuroplasticity in the form of a horizontal-vertical integration model. The horizontal plane consists of a network of neurons connected by adaptive transmission links. This fits with standard computational neuroscience approaches. Each individual neuron also has a vertical dimension with internal parameters steering the external membrane-expressed parameters. These determine neural Read more..
Read MoreDetermining optimal combination regimens for patients with multiple myeloma
Here is an interesting paper from our board member Dr. Helen Moore.
Read MoreCortical Models
We started a collaboration on the topic of cortical microcircuits with Fred Narcross with the goal of investigating functional principles outside of biological implementation. This contributed to a new paper on neural models.
Read MoreModels of Neural Plasticity
Our work on basing models of neural plasticity on cellular principles continues to advance. With the help of Michael Wheeldon, B.Sc., as the current recipient of a research scholarship we anticipate two publications at the beginning of the new year. One publication will focus on outlining a new type of memory model using both horizontal Read more..
Read MorePharmacodynamics: Problems and Pitfalls
A systematic overview of qualitative and quantitative model evaluation methods with many detailed references. This is applied and substantiated with case studies, and most interestingly, with an analysis of what can go wrong. Dynamic models are highly sensitive to uncertainties and we need to be aware of the difficulties that can arise from that. S. Read more..
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