Biophysics of computation : information processing in single neurons / / Christof Koch

• Record Nr.: UNINA9910972234703321
• Autore: Koch Christof <1956->
• Pubbl/distr/stampa: New York, : Oxford University Press, 1999
• ISBN: 0-19-029285-7; 0-19-756233-7; 1-280-59509-4; 9786613624925; 0-19-976055-1
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (587 p.)
• Collana: Computational neuroscience
• Disciplina: 573.8/536; 573.8536
• Soggetti: Computational neuroscience; Neurons; Neural networks (Neurobiology); Action potentials (Electrophysiology); Neural conduction
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Previously issued in print: 1999.
• Nota di bibliografia: Includes bibliographical references (p. 503-552) and index.
• Nota di contenuto: Cover; 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; 2.1 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; 3.4.3 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; 4.2.1 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; 4.10 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; 5.1.7 Retinal Directional Selectivity and Synaptic Logic
• Sommario/riassunto: In 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.