10 papers
Convergent Evolution in Neural Representation Space: Emergent Order in Deep Belief Networks
Patrick Krauss, Achim Schilling, Andreas Maier +2
Deep Belief Networks (DBNs) learn hierarchical generative models without class supervision. Here, we ask whether this purely unsupervised process nevertheless organizes internal re…
Convergent Evolution in Algorithmic Space
Patrick Krauss, Achim Schilling, Andreas Maier +4
In evolutionary biology, unrelated organisms can independently evolve similar structures when exposed to similar functional demands. Here we ask whether an analogous form of conver…
Convergent Representations of Linguistic Constructions in Human and Artificial Neural Systems
Pegah Ramezani, Thomas Kinfe, Andreas Maier +2
Understanding how the brain processes linguistic constructions is a central challenge in cognitive neuroscience and linguistics. Recent computational studies show that artificial n…
Are cortical microcircuits optimized for information flux? -- A simulation-based reverse engineering study
Claus Metzner, Ali Ghebleh, Karin Prebeck +4
A sufficiently large information flux in recurrent neural networks, quantified by the mutual information between successive network states, is considered a prerequisite for rich in…
Structural and dynamical strategies to prevent runaway excitation in reservoir computing
Claus Metzner, Achim Schilling, Andreas Maier +2
Reservoirs, typically implemented as recurrent neural networks with fixed random connection weights, can be combined with a simple trained readout layer to perform a wide range of…
Deep Reinforcement Learning for Optimum Order Execution: Mitigating Risk and Maximizing Returns
Khabbab Zakaria, Jayapaulraj Jerinsh, Andreas Maier +3
Optimal Order Execution is a well-established problem in finance that pertains to the flawless execution of a trade (buy or sell) for a given volume within a specified time frame.…