Estimating a Separably Markov Random Field from Binary Observations
Zhang Y, Malem-Shinitski N, Allsop S, Tye K, Ba D. Estimating a Separably Markov Random Field from Binary Observations. Neural Computation 2018, 30: 1046-1079. PMID: 29381446, DOI: 10.1162/neco_a_01059.Peer-Reviewed Original ResearchConceptsNeural spiking dataMarkov random fieldLimitation of current methodsLoss of informationSpike dataNeural spikesConditional intensity functionRandom fieldState sequenceRF modelLearning of fearLearningTrial-to-trial dynamicsConditioned stimulusEstimate state-space modelsNeuronsPrefrontal cortexNeural underpinningsAssociative learningBinary observationsComputational toolsNeural activityIntensity functionTrialsCurrent methods