PsychRNN: 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 frameworkGeometry of neural computation unifies working memory and planning
Ehrlich DB, Murray JD. Geometry of neural computation unifies working memory and planning. Proceedings Of The National Academy Of Sciences Of The United States Of America 2022, 119: e2115610119. PMID: 36067286, PMCID: PMC9478653, DOI: 10.1073/pnas.2115610119.Peer-Reviewed Original ResearchMeSH KeywordsComputer SimulationHumansMemory, Short-TermModels, NeurologicalNeural Networks, ComputerPrefrontal CortexConceptsNeural dataPossible circuit mechanismReal-world tasksMemory taskUpcoming eventsSensory modelPrefrontal cortexCognitive functionRecurrent neural networkHuman behaviorNeurophysiological observationsMemoryCircuit mechanismsFalsifiable predictionsFuture behaviorTaskModular processRepresentational strategiesRepresentationNeural networkCortexBehaviorDistinct typesBrainFindings