Title page -- Preface -- A Note on the Illustrations -- Contents -- Understanding the Brain Language -- The importance of asking the right questions -- Does neuroscience ask the right questions? -- Cognition, Computation and Dynamical Systems -- Myths, Controversies and Current Challenges -- The inadequacies of temporal coding -- The cable myth -- The dilemma of synapses -- The Hebbian approach of connectivity -- The myth of neurotransmitters -- The myth of molecular biology -- Spike Timing - Current Challenges -- Is the neuron an integrator of incoming inputs - Temporal integration or coincidence detection? -- Firing rate or ISI? Which technique is better to quantify neuronal activity? -- Do irregular spikes convey more or less information than the regular ones? -- Are synchrony and oscillations important? -- Across-fiber pattern, grandmother cell, or labeled-line -- Optimal efficiency or a high metabolic cost of information transmission? -- Issues of Brain Computation -- Brain Computations - Classical or Quantum? -- Brain computation and AI -- Is computation in the brain closed to a reversible process? -- Is 'noise' useful for brain computations? -- What is the role of active dendrites and metabolic subunits within the neuron from a computational perspective? -- How can Information be Represented in the Brain -- Is postsynaptic activity required for dendritic development? -- How and where are memories stored? -- The key of the topographic map -- Interdisciplinary Formalism -- Sets and functions -- Dynamical Systems -- The laws of thermodynamics -- Conclusion -- Imaging Spikes - Challenges for Brain Computations -- Methods, Models and Techniques -- Tetrodes -- The Necessity for a New Model -- The Model of Charges in Movement -- The Triangulation technique -- Charges and Electric potential -- The Projective Field: Spatial Directivity of Charges.

What is "Spike Directivity"? -- Triangulation and Independent Component Analysis Together -- From Spikes to Behavior -- Behavioral procedures -- Spike sorting and unit classification -- Computing and Analyzing Spike Directivity -- Upcoming Choices, Decision and Spike Directivity -- Learning: One Spike Seems Enough -- Information and Computation -- What is Computation? -- Coding and decoding -- Are Turing Machines Universal Models of Computation? -- Von Neumann Architectures -- Reversible Computation -- Models of Brain Computation -- Building the Internal Model -- Neurons as information engines -- Maximum Entropy Principle -- Mutual Information -- Information transfer -- Efficiency of Information Transfer -- The Nature of Things - Functional Density -- Charge Distribution and Probability Density -- The Laws of Physics -- Minimum Description Length -- Is Hypercomputation a Myth? -- Models of Brain Computation -- Dynamics Based Computation - The Power of Charges -- Space and hidden dimensions -- Minimum Path Description - The Principle of Least Action -- Natural Computation - Abstract Physical Machines -- Back to Building Brains -- Charge Movement Model - A Computational Approach -- Properties of Computation with Charges -- Quantum Model - Combining Many Worlds -- Many Worlds in a Single Spike -- Spike Models - A Quantum Formalism -- Quantum models -- Brain the Physical Computer -- The Thermodynamic Model of Computation -- The Maxwell Daemon -- Computation in a Thermodynamic Engine -- Entropy in Neuronal Spike -- Thermodynamic Entropy and Information -- Synaptic spikes -- Spikes as Thermodynamic Engines -- From Brain Language to Artificial Intelligence -- How are memories stored? -- Are proteins the key? -- What sleep is for? -- Spike Timing - An Incomplete Description -- Models of Auditory Processing -- Models of Schizophrenia -- Models of Learning.

Models of Parkinson's disease and Dyskinesia -- Brain Computer Interfaces -- Moving from Spike Timing to NeuroElectroDynamics -- Computation, Cognition and Artificial intelligence -- Instead of Discussion.