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 frameworkMultitask representations in the human cortex transform along a sensory-to-motor hierarchy
Ito T, Murray J. Multitask representations in the human cortex transform along a sensory-to-motor hierarchy. Nature Neuroscience 2022, 26: 306-315. PMID: 36536240, DOI: 10.1038/s41593-022-01224-0.Peer-Reviewed Original ResearchConceptsComputational principlesFunctional magnetic resonanceHuman cortexRepresentational similarityCognitive tasksHuman cognitionNeural processesMotor hierarchyMotor representationsMultilayer neural network modelNeural network modelCortex transformFunctional architectureTraining regimesOptimized representationCognitionCortical patternsNetwork modelTaskCortexRepresentationSame individualArchitectureIndividualsCompression