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Found 8 papers, 2 papers with code
Neural Population Decoding and Imbalanced Multi-Omic Datasets For Cancer Subtype Diagnosis
Recent strides in the field of neural computation has seen the adoption of Winner Take All (WTA) circuits to facilitate the unification of hierarchical Bayesian inference and spiking neural networks as a neurobiologically plausible model of information processing.
Cancer Subtype Identification through Integrating Inter and Intra Dataset Relationships in Multi-Omics Data
This paper proposes a novel approach to identify cancer subtypes through the integration of multi-omics data for clustering.
Bayesian Integration of Information Using Top-Down Modulated WTA Networks
The results show that WTA circuits are capable of integrating the probabilistic information represented by other WTA networks, and that top down processes can improve a WTA network's inference and learning performance.
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics
The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems.
Motivating explanations in Bayesian networks using MAP-independence
In decision support systems the motivation and justification of the system's diagnosis or classification is crucial for the acceptance of the system by the human user.
On the computational power and complexity of Spiking Neural Networks
However, to date a comparison with more traditional computational architectures (particularly with respect to energy usage) is hampered by the lack of a formal machine model and a computational complexity theory for neuromorphic computation.
A spiking neural algorithm for the Network Flow problem
More in specific we show that a logspace-constrained Turing machine with access to an interactive neuromorphic oracle with linear space, time, and energy constraints can solve Max Network Flow.
Finding dissimilar explanations in Bayesian networks: Complexity results
Finding the most probable explanation for observed variables in a Bayesian network is a notoriously intractable problem, particularly if there are hidden variables in the network.
