A flexible framework for simulating and fitting generalized drift-diffusion models
Shinn M, Lam NH, Murray JD. A flexible framework for simulating and fitting generalized drift-diffusion models. ELife 2020, 9: e56938. PMID: 32749218, PMCID: PMC7462609, DOI: 10.7554/elife.56938.Peer-Reviewed Original ResearchConceptsDrift-diffusion modelArbitrary user-defined functionsImportant decision-making modelFokker-Planck equationEfficient numerical methodDecision-making mechanismUser-defined functionsDrift diffusion model frameworkFlexible frameworkSoftware packageGDDMHuman datasetsNumerical methodDecision-making modelResponse time distributionsDecision-making taskLatest methodologiesModel formGood accuracyFrameworkMaximum likelihoodModel innovationTime distributionDDM parametersModel frameworkPsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks
Ehrlich DB, Stone JT, Brandfonbrener D, Atanasov A, Murray JD. PsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks. ENeuro 2020, 8: eneuro.0427-20.2020. PMID: 33328247, PMCID: PMC7814477, DOI: 10.1523/eneuro.0427-20.2020.Peer-Reviewed Original ResearchConceptsRecurrent neural networkCognitive tasksCognitive neurosciencePython packageTraining of animalsTraining recurrent neural networksNetwork modelArtificial recurrent neural networkDeep learning softwareDeep-learning methodsRecurrent neural network modelNeural network modelNeural representationCognitive computationsNeuroscience researchNeural networkRNN modelCurriculum learningNeuroscienceCircuit mechanismsAdditional customizationConnectivity patternsTaskSoftware packageComputational modeling framework