TY - CHAP M1 - Book, Section TI - Theoretical Approaches to Neuroscience: Examples from Single Neurons to Networks Y1 - 2014 N1 - T2 - Principles of Neural Science, Fifth Edition AB - Single-Neuron Models Allow Study of the Integration of Synaptic Inputs and Intrinsic ConductancesNeurons Show Sharp Threshold Sensitivity to the Number and Synchrony of Synaptic Inputs in Quiet Conditions Resembling In VitroNeurons Show Graded Sensitivity to the Number and Synchrony of Synaptic Inputs in Noisy Conditions Resembling In VivoNeuronal Messages Depend on Intrinsic Activity and Extrinsic SignalsNetwork Models Provide Insight into the Collective Dynamics of NeuronsBalanced Networks of Active Neurons Can Generate the Ongoing Noisy Activity Seen In VivoFeed-forward and Recurrent Networks Can Amplify or Integrate Inputs with Distinct DynamicsBalanced Recurrent Networks Can Behave Like Feed-forward NetworksParadoxical Effects in Balanced Recurrent Networks May Underlie Surround Suppression in the Visual CortexRecurrent Networks Can Model Decision-Making SN - PB - McGraw-Hill Education CY - New York, NY Y2 - 2024/03/28 UR - neurology.mhmedical.com/content.aspx?aid=1102061627 ER -