Energy landscape

The brain activity measured by functional magnetic resonance imaging (fMRI) is linked to the cognitive output. This type of activity is considered to be the top layer of processing and subsequently, it is considered to make up the ‘brain states’ that emerge from network interactions (McIntosh, 2000). Using empirical blood-oxygen-level-dependent (BOLD) fMRI data during rest (HCP dataset), this study investigates resting state (RS) dynamics. Our framework follows the hypothesis that there exists Structured Flows on the Manifold (SFM) that govern the dynamics of RS, akin to behavior dynamics (Pil

…cortex and flattening the whole brain ‘energy landscape’. In line with our hypothesis, shifts in the perceptual interpretation of an ambiguous image were associated with peaks in pupil diameter – a correlate of adrenergic tone. We trained a recurrent neural network (RNN) to perform an analog

…weights and update rules such that the energy landscape of the network forms attractors around the memories. How many stable, sufficiently-attracting memory patterns can we store in such a network using N neurons? The answer depends on the choice of weights and update rule. Inspired by setwise connectivity in biology, we extend Hopfield networks by adding setwise connections and embedding these connections in a simplicial complex. Simplicial complexes are

…The energy landscape of the model was derived by using the maximum entropy principle and relies on two experimentally derived inputs: a classification of loci into chromatin types and a catalog of the positions of chromatin loops. This model was generalized by u