RT Book, Section
SR Print(0)
ID 1102061627
T1 Theoretical Approaches to Neuroscience: Examples from Single Neurons to Networks
T2 Principles of Neural Science, Fifth Edition
YR 2014
FD 2014
PB McGraw-Hill Education
PP New York, NY
SN 978-0-07-139011-8
LK accessbiomedicalscience.mhmedical.com/content.aspx?aid=1102061627
RD 2024/04/19
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