LEADER 05389nam 2200781 a 450 001 9910972234703321 005 20250527123340.0 010 $a0-19-029285-7 010 $a0-19-756233-7 010 $a1-280-59509-4 010 $a9786613624925 010 $a0-19-976055-1 035 $a(CKB)2550000000087876 035 $a(EBL)886673 035 $a(OCoLC)778073952 035 $a(SSID)ssj0000632654 035 $a(PQKBManifestationID)12251700 035 $a(PQKBTitleCode)TC0000632654 035 $a(PQKBWorkID)10610616 035 $a(PQKB)11388711 035 $a(StDuBDS)EDZ0002340885 035 $a(Au-PeEL)EBL886673 035 $a(CaPaEBR)ebr10531081 035 $a(CaONFJC)MIL362492 035 $a(MiAaPQ)EBC886673 035 $a(EXLCZ)992550000000087876 100 $a19971211d1999 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBiophysics of computation $einformation processing in single neurons /$fChristof Koch 205 $a1st ed. 210 $aNew York $cOxford University Press$d1999 215 $a1 online resource (587 p.) 225 1 $aComputational neuroscience 300 $aPreviously issued in print: 1999. 311 08$a0-19-518199-9 320 $aIncludes bibliographical references (p. 503-552) and index. 327 $aCover; Contents; Preface; List of Symbols; Introduction; 1 The Membrane Equation; 1.1 Structure of the Passive Neuronal Membrane; 1.1.1 Resting Potential; 1.1.2 Membrane Capacity; 1.1.3 Membrane Resistance; 1.2 A Simple RC Circuit; 1.3 RC Circuits as Linear Systems; 1.3.1 Filtering by RC Circuits; 1.4 Synaptic Input; 1.5 Synaptic Input Is Nonlinear; 1.5.1 Synaptic Input, Saturation, and the Membrane Time Constant; 1.5.2 Synaptic Interactions among Excitation and Shunting Inhibition; 1.5.3 Gain Normalization in Visual Cortex and Synaptic Input; 1.6 Recapitulation; 2 Linear Cable Theory 327 $a2.1 Basic Assumptions Underlying One-Dimensional Cable Theory2.1.1 Linear Cable Equation; 2.2 Steady-State Solutions; 2.2.1 Infinite Cable; 2.2.2 Finite Cable; 2.3 Time-Dependent Solutions; 2.3.1 Infinite Cable; 2.3.2 Finite Cable; 2.4 Neuronal Delays and Propagation Velocity; 2.5 Recapitulation; 3 Passive Dendritic Trees; 3.1 Branched Cables; 3.1.1 What Happens at Branch Points?; 3.2 Equivalent Cylinder; 3.3 Solving the Linear Cable Equation for Branched Structures; 3.3.1 Exact Methods; 3.3.2 Compartmental Modeling; 3.4 Transfer Resistances; 3.4.1 General Definition; 3.4.2 An Example 327 $a3.4.3 Properties of K[sub(ij)]3.4.4 Transfer Resistances in a Pyramidal Cell; 3.5 Measures of Synaptic Efficiency; 3.5.1 Electrotonic Distance; 3.5.2 Voltage Attenuation; 3.5.3 Charge Attenuation; 3.5.4 Graphical Morphoelectrotonic Transforms; 3.6 Signal Delays in Dendritic Trees; 3.6.1 Experimental Determination of T[sub(m)]; 3.6.2 Local and Propagation Delays in Dendritic Trees; 3.6.3 Dependence of Fast Synaptic Inputs on Cable Parameters; 3.7 Recapitulation; 4 Synaptic Input; 4.1 Neuronal and Synaptic Packing Densities; 4.2 Synaptic Transmission Is Stochastic 327 $a4.2.1 Probability of Synaptic Release p4.2.2 What Is the Synaptic Weight?; 4.3 Neurotransmitters; 4.4 Synaptic Receptors; 4.5 Synaptic Input as Conductance Change; 4.5.1 Synaptic Reversal Potential in Series with an Increase in Conductance; 4.5.2 Conductance Decreasing Synapses; 4.6 Excitatory NMDA and Non-NMDA Synaptic Input; 4.7 Inhibitory GABAergic Synaptic Input; 4.8 Postsynaptic Potential; 4.8.1 Stationary Synaptic Input; 4.8.2 Transient Synaptic Input; 4.8.3 Infinitely Fast Synaptic Input; 4.9 Visibility of Synaptic Inputs; 4.9.1 Input Impedance in the Presence of Synaptic Input 327 $a4.10 Electrical Gap Junctions4.11 Recapitulation; 5 Synaptic Interactions in a Passive Dendritic Tree; 5.1 Nonlinear Interaction among Excitation and Inhibition; 5.1.1 Absolute versus Relative Suppression; 5.1.2 General Analysis of Synaptic Interaction in a Passive Tree; 5.1.3 Location of the Inhibitory Synapse; 5.1.4 Shunting Inhibition Implements a ""Dirty"" Multiplication; 5.1.5 Hyperpolarizing Inhibition Acts Like a Linear Subtraction; 5.1.6 Functional Interpretation of the Synaptic Architecture and Dendritic Morphology: AND-NOT Gates 327 $a5.1.7 Retinal Directional Selectivity and Synaptic Logic 330 8 $aIn this volume, Koch shows how individual nerve cells can multiply, integrate, or delay synaptic inputs, and how information is encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes. 410 0$aComputational neuroscience. 606 $aComputational neuroscience 606 $aNeurons 606 $aNeural networks (Neurobiology) 606 $aAction potentials (Electrophysiology) 606 $aNeural conduction 615 0$aComputational neuroscience. 615 0$aNeurons. 615 0$aNeural networks (Neurobiology) 615 0$aAction potentials (Electrophysiology) 615 0$aNeural conduction. 676 $a573.8/536 676 $a573.8536 700 $aKoch$b Christof$f1956-$0600176 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910972234703321 996 $aBiophysics of computation$94382450 997 $aUNINA LEADER 02164nam0 22003251i 450 001 UON00182875 005 20231205103139.938 010 $a88-7104-274-3 100 $a20030730d1991 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| 1|||| 200 1 $aCodice Civile annotato con la dottrina e la giurisprudenza$fa cura di Pietro Perlingieri 205 $a2. ed 210 $aNapoli$aBologna$cE.S.I. ; Zanichelli$d1991 215 $a6 v. in 7 tomi (XLI, 6000 p. compl.)$d24 cm 300 $aI: Delle persone e della famigli a. 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