Search Results for author:
Found 16 papers, 0 papers with code
Multi-Modal Cognitive Maps based on Neural Networks trained on Successor Representations
Cognitive maps, as represented by the entorhinal-hippocampal complex in the brain, organize and retrieve context from memories, suggesting that large language models (LLMs) like ChatGPT could harness similar architectures to function as a high-level processing center, akin to how the hippocampus operates within the cortex hierarchy.
Beyond Labels: Advancing Cluster Analysis with the Entropy of Distance Distribution (EDD)
In the evolving landscape of data science, the accurate quantification of clustering in high-dimensional data sets remains a significant challenge, especially in the absence of predefined labels.
Conceptual Cognitive Maps Formation with Neural Successor Networks and Word Embeddings
The human brain possesses the extraordinary capability to contextualize the information it receives from our environment.
Word class representations spontaneously emerge in a deep neural network trained on next word prediction
Strikingly, we find that the internal representations of nine-word input sequences cluster according to the word class of the tenth word to be predicted as output, even though the neural network did not receive any explicit information about syntactic rules or word classes during training.
Quantifying and maximizing the information flux in recurrent neural networks
We find that in networks with moderately strong connections, the mutual information $I$ is approximately a monotonic transformation of the root-mean-square averaged Pearson correlations between neuron-pairs, a quantity that can be efficiently computed even in large systems.
Extracting continuous sleep depth from EEG data without machine learning
The human sleep-cycle has been divided into discrete sleep stages that can be recognized in electroencephalographic (EEG) and other bio-signals by trained specialists or machine learning systems.
Neural Network based Formation of Cognitive Maps of Semantic Spaces and the Emergence of Abstract Concepts
The neural network successfully learns the similarities between different animal species, and constructs a cognitive map of 'animal space' based on the principle of successor representations with an accuracy of around 30% which is near to the theoretical maximum regarding the fact that all animal species have more than one possible successor, i. e. nearest neighbor in feature space.
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity
Remarkably, the accuracy limit is not affected by applying non-linear transformations to the data, even if these transformations are non-reversible and drastically reduce the information content of the input data.
Predictive coding and stochastic resonance as fundamental principles of auditory perception
How is information processed in the brain during perception?
Neural Network based Successor Representations of Space and Language
We conclude that cognitive maps and neural network-based successor representations of structured knowledge provide a promising way to overcome some of the short comings of deep learning towards artificial general intelligence.
Known Operator Learning and Hybrid Machine Learning in Medical Imaging --- A Review of the Past, the Present, and the Future
In this article, we perform a review of the state-of-the-art of hybrid machine learning in medical imaging.
Dynamical Phases and Resonance Phenomena in Information-Processing Recurrent Neural Networks
Moreover, we find a completely new type of resonance phenomenon, called 'Import Resonance' (IR), where the information import shows a maximum, i. e. a peak-like dependence on the coupling strength between the RNN and its input.
Towards a Cognitive Computational Neuroscience of Auditory Phantom Perceptions
In order to gain a mechanistic understanding of how tinnitus emerges in the brain, we must build biologically plausible computational models that mimic both tinnitus development and perception, and test the tentative models with brain and behavioral experiments.
Will we ever have Conscious Machines?
The question of whether artificial beings or machines could become self-aware or consciousness has been a philosophical question for centuries.
Sparsity through evolutionary pruning prevents neuronal networks from overfitting
We conclude that sparsity is a central property of neural networks and should be considered for modern Machine learning approaches.
How deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networks
Deep neural networks typically outperform more traditional machine learning models in their ability to classify complex data, and yet is not clear how the individual hidden layers of a deep network contribute to the overall classification performance.
