Now showing items 1-6 of 6
Dendritic Morphology Predicts Pattern Recognition Performance in Multi-compartmental Model Neurons with and without Active Conductances
In this paper we examine how a neuron’s dendritic morphology can affect its pattern recognition performance. We use two different algorithms to systematically explore the space of dendritic morphologies: an algorithm that ...
Integrating genomic binding site predictions using real-valued meta classifiers
Currently the best algorithms for predicting transcription factor binding sites in DNA sequences are severely limited in accuracy. There is good reason to believe that predictions from different classes of algorithms could ...
Using sampling methods to improve binding site predictions
Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. In previous work we combine random selection under-sampling into SMOTE over-sampling technique, working with ...
The development of iHARP: a multiple instruction issue processor chip
During the last decade RISC ideas on processor architecture have become widely accepted. RISC architectures achieve significant performance advantages over CISC architectures by striving to execute one instruction per ...