Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
2007 3rd International IEEE/EMBS Conference on Neural Engineering : Kohala Coast, Hawaii, 2-5 May 2007
| 2007 3rd International IEEE/EMBS Conference on Neural Engineering : Kohala Coast, Hawaii, 2-5 May 2007 |
| Pubbl/distr/stampa | IEEE |
| Disciplina | 612.8 |
| Soggetto topico |
Neural networks (Neurobiology)
Neural networks (Computer science) Computational neuroscience |
| ISBN | 1-5090-8668-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
2007 3rd International IEEE/EMBS Conference on Neural Engineering
Computer Design |
| Record Nr. | UNISA-996280947903316 |
| IEEE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
2007 3rd International IEEE/EMBS Conference on Neural Engineering : Kohala Coast, Hawaii, 2-5 May 2007
| 2007 3rd International IEEE/EMBS Conference on Neural Engineering : Kohala Coast, Hawaii, 2-5 May 2007 |
| Pubbl/distr/stampa | IEEE |
| Disciplina | 612.8 |
| Soggetto topico |
Neural networks (Neurobiology)
Neural networks (Computer science) Computational neuroscience |
| ISBN |
9781509086689
1509086684 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
2007 3rd International IEEE/EMBS Conference on Neural Engineering
Computer Design |
| Record Nr. | UNINA-9910143014003321 |
| IEEE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Adaptive processing of brain signals [[electronic resource] /] / Saeid Sanei
| Adaptive processing of brain signals [[electronic resource] /] / Saeid Sanei |
| Autore | Sanei Saeid |
| Pubbl/distr/stampa | Chichester, West Sussex, : John Wiley & Sons Inc., c2013 |
| Descrizione fisica | 1 online resource (1039 p.) |
| Disciplina | 573.8/5 |
| Soggetto topico |
Brain - Physiology
Neural networks (Neurobiology) Signal processing - Digital techniques |
| ISBN |
1-118-62216-2
1-118-62214-6 1-118-62215-4 |
| Classificazione | SCI067000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Preface; Chapter 1 Brain Signals, Their Generation, Acquisition and Properties; 1.1 Introduction; 1.2 Historical Review of the Brain; 1.3 Neural Activities; 1.4 Action Potentials; 1.5 EEG Generation; 1.6 Brain Rhythms; 1.7 EEG Recording and Measurement; 1.8 Abnormal EEG Patterns; 1.9 Aging; 1.10 Mental Disorders; 1.11 Memory and Content Retrieval; 1.12 MEG Signals and Their Generation; 1.13 Conclusions; References; Chapter 2 Fundamentals of EEG Signal Processing; 2.1 Introduction; 2.2 Nonlinearity of the Medium; 2.3 Nonstationarity; 2.4 Signal Segmentation
2.5 Other Properties of Brain Signals2.6 Conclusions; References; Chapter 3 EEG Signal Modelling; 3.1 Physiological Modelling of EEG Generation; 3.2 Mathematical Models; 3.3 Generating EEG Signals Based on Modelling the Neuronal Activities; 3.4 Electronic Models; 3.5 Dynamic Modelling of the Neuron Action Potential Threshold; 3.6 Conclusions; References; Chapter 4 Signal Transforms and Joint Time-Frequency Analysis; 4.1 Introduction; 4.2 Parametric Spectrum Estimation and Z-Transform; 4.3 Time-Frequency Domain Transforms; 4.4 Ambiguity Function and the Wigner-Ville Distribution 4.5 Hermite Transform4.6 Conclusions; References; Chapter 5 Chaos and Dynamical Analysis; 5.1 Entropy; 5.2 Kolmogorov Entropy; 5.3 Lyapunov Exponents; 5.4 Plotting the Attractor Dimensions from Time Series; 5.5 Estimation of Lyapunov Exponents from Time Series; 5.6 Approximate Entropy; 5.7 Using Prediction Order; 5.8 Conclusions; References; Chapter 6 Classification and Clustering of Brain Signals; 6.1 Introduction; 6.2 Linear Discriminant Analysis; 6.3 Support Vector Machines; 6.4 k-Means Algorithm; 6.5 Common Spatial Patterns; 6.6 Conclusions; References Chapter 7 Blind and Semi-Blind Source Separation7.1 Introduction; 7.2 Singular Spectrum Analysis; 7.3 Independent Component Analysis; 7.4 Instantaneous BSS; 7.5 Convolutive BSS; 7.6 Sparse Component Analysis; 7.7 Nonlinear BSS; 7.8 Constrained BSS; 7.9 Application of Constrained BSS; Example; 7.10 Nonstationary BSS; 7.11 Tensor Factorization for Underdetermined Source Separation; 7.12 Tensor Factorization for Separation of Convolutive Mixtures in the Time Domain; 7.13 Separation of Correlated Sources via Tensor Factorization; 7.14 Conclusions; References Chapter 8 Connectivity of Brain Regions8.1 Introduction; 8.2 Connectivity Through Coherency; 8.3 Phase-Slope Index; 8.4 Multivariate Directionality Estimation; 8.5 Modelling the Connectivity by Structural Equation Modelling; 8.6 EEG Hyper-Scanning and Inter-Subject Connectivity; 8.7 State-Space Model for Estimation of Cortical Interactions; 8.8 Application of Adaptive Filters; 8.9 Tensor Factorization Approach; 8.10 Conclusions; References; Chapter 9 Detection and Tracking of Event-Related Potentials; 9.1 ERP Generation and Types; 9.2 Detection, Separation, and Classification of P300 Signals 9.3 Brain Activity Assessment Using ERP |
| Record Nr. | UNINA-9910139054403321 |
Sanei Saeid
|
||
| Chichester, West Sussex, : John Wiley & Sons Inc., c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Adaptive processing of brain signals / / Saeid Sanei
| Adaptive processing of brain signals / / Saeid Sanei |
| Autore | Sanei Saeid |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, : John Wiley & Sons Inc., c2013 |
| Descrizione fisica | 1 online resource (1039 p.) |
| Disciplina | 573.8/5 |
| Soggetto topico |
Brain - Physiology
Neural networks (Neurobiology) Signal processing - Digital techniques |
| ISBN |
1-118-62216-2
1-118-62214-6 1-118-62215-4 |
| Classificazione | SCI067000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Preface; Chapter 1 Brain Signals, Their Generation, Acquisition and Properties; 1.1 Introduction; 1.2 Historical Review of the Brain; 1.3 Neural Activities; 1.4 Action Potentials; 1.5 EEG Generation; 1.6 Brain Rhythms; 1.7 EEG Recording and Measurement; 1.8 Abnormal EEG Patterns; 1.9 Aging; 1.10 Mental Disorders; 1.11 Memory and Content Retrieval; 1.12 MEG Signals and Their Generation; 1.13 Conclusions; References; Chapter 2 Fundamentals of EEG Signal Processing; 2.1 Introduction; 2.2 Nonlinearity of the Medium; 2.3 Nonstationarity; 2.4 Signal Segmentation
2.5 Other Properties of Brain Signals2.6 Conclusions; References; Chapter 3 EEG Signal Modelling; 3.1 Physiological Modelling of EEG Generation; 3.2 Mathematical Models; 3.3 Generating EEG Signals Based on Modelling the Neuronal Activities; 3.4 Electronic Models; 3.5 Dynamic Modelling of the Neuron Action Potential Threshold; 3.6 Conclusions; References; Chapter 4 Signal Transforms and Joint Time-Frequency Analysis; 4.1 Introduction; 4.2 Parametric Spectrum Estimation and Z-Transform; 4.3 Time-Frequency Domain Transforms; 4.4 Ambiguity Function and the Wigner-Ville Distribution 4.5 Hermite Transform4.6 Conclusions; References; Chapter 5 Chaos and Dynamical Analysis; 5.1 Entropy; 5.2 Kolmogorov Entropy; 5.3 Lyapunov Exponents; 5.4 Plotting the Attractor Dimensions from Time Series; 5.5 Estimation of Lyapunov Exponents from Time Series; 5.6 Approximate Entropy; 5.7 Using Prediction Order; 5.8 Conclusions; References; Chapter 6 Classification and Clustering of Brain Signals; 6.1 Introduction; 6.2 Linear Discriminant Analysis; 6.3 Support Vector Machines; 6.4 k-Means Algorithm; 6.5 Common Spatial Patterns; 6.6 Conclusions; References Chapter 7 Blind and Semi-Blind Source Separation7.1 Introduction; 7.2 Singular Spectrum Analysis; 7.3 Independent Component Analysis; 7.4 Instantaneous BSS; 7.5 Convolutive BSS; 7.6 Sparse Component Analysis; 7.7 Nonlinear BSS; 7.8 Constrained BSS; 7.9 Application of Constrained BSS; Example; 7.10 Nonstationary BSS; 7.11 Tensor Factorization for Underdetermined Source Separation; 7.12 Tensor Factorization for Separation of Convolutive Mixtures in the Time Domain; 7.13 Separation of Correlated Sources via Tensor Factorization; 7.14 Conclusions; References Chapter 8 Connectivity of Brain Regions8.1 Introduction; 8.2 Connectivity Through Coherency; 8.3 Phase-Slope Index; 8.4 Multivariate Directionality Estimation; 8.5 Modelling the Connectivity by Structural Equation Modelling; 8.6 EEG Hyper-Scanning and Inter-Subject Connectivity; 8.7 State-Space Model for Estimation of Cortical Interactions; 8.8 Application of Adaptive Filters; 8.9 Tensor Factorization Approach; 8.10 Conclusions; References; Chapter 9 Detection and Tracking of Event-Related Potentials; 9.1 ERP Generation and Types; 9.2 Detection, Separation, and Classification of P300 Signals 9.3 Brain Activity Assessment Using ERP |
| Record Nr. | UNINA-9910821926303321 |
|
Sanei Saeid
|
||
| Chichester, West Sussex, : John Wiley & Sons Inc., c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in brain, vision, and artificial intelligence : second international symposium, BVAI 2007, Naples, Italy, October 10-12, 2007 : proceedings / / Francesco Mele ... [et al.] (eds.)
| Advances in brain, vision, and artificial intelligence : second international symposium, BVAI 2007, Naples, Italy, October 10-12, 2007 : proceedings / / Francesco Mele ... [et al.] (eds.) |
| Edizione | [1st ed. 2007.] |
| Pubbl/distr/stampa | Berlin ; ; New York, : Springer, c2007 |
| Descrizione fisica | 1 online resource (XVI, 618 p.) |
| Disciplina | 006.3/7 |
| Altri autori (Persone) | MeleFrancesco |
| Collana |
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics
Lecture notes in computer science |
| Soggetto topico |
Neural networks (Neurobiology)
Neural networks (Computer science) Computer vision Optical pattern recognition Artificial intelligence |
| ISBN | 3-540-75555-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Basic models in visual sciences -- Cortical mechanism of vision -- Color processing in natural vision -- Action oriented vision -- Visual recognition and attentive modulation -- Biometric recognition -- Image segmentation and recognition -- Disparity calculation and noise analysis -- Signal identification in neural models -- Natural and artificial representation issues in artificial intelligence -- Meaning, interaction and emotion -- Robot navigation and control. |
| Record Nr. | UNINA-9910484295703321 |
| Berlin ; ; New York, : Springer, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Neural Signal Processing / / edited by Ramana Vinjamuri
| Advances in Neural Signal Processing / / edited by Ramana Vinjamuri |
| Pubbl/distr/stampa | London : , : IntechOpen, , 2020 |
| Descrizione fisica | 1 online resource (xi, 142 pages) : illustrations |
| Disciplina | 621.3822028563 |
| Soggetto topico |
Neural networks (Computer science)
Neural networks (Neurobiology) Signal processing |
| ISBN | 1-78984-114-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910424651003321 |
| London : , : IntechOpen, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial neural networks : formal models and their applications : ICANN 2005 : 15th International Conference, Warsaw, Poland, September 11-15, 2005 : proceedings, pt. II / / Wodzisaw Duch ... [et al.] (eds.)
| Artificial neural networks : formal models and their applications : ICANN 2005 : 15th International Conference, Warsaw, Poland, September 11-15, 2005 : proceedings, pt. II / / Wodzisaw Duch ... [et al.] (eds.) |
| Edizione | [1st ed. 2005.] |
| Pubbl/distr/stampa | Berlin, : Springer, 2005 |
| Descrizione fisica | 1 online resource (XXXII, 1045 p.) |
| Disciplina | 006.3 |
| Altri autori (Persone) | DuchWodzisaw |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Neural networks (Neurobiology)
Neural networks (Computer science) Computational neuroscience |
| ISBN | 3-540-28756-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | New Neural Network Models -- Supervised Learning Algorithms -- Ensemble-Based Learning -- Unsupervised Learning -- Recurrent Neural Networks -- Reinforcement Learning -- Bayesian Approaches to Learning -- Learning Theory -- Artificial Neural Networks for System Modeling, Decision Making, Optimalization and Control -- Special Session: Knowledge Extraction from Neural Networks Organizer and Chair: D. A. Elizondo -- Temporal Data Analysis, Prediction and Forecasting -- Support Vector Machines and Kernel-Based Methods -- Soft Computing Methods for Data Representation, Analysis and Processing -- Special Session: Data Fusion for Industrial, Medical and Environmental Applications Organizers and Chairs: D. Mandic, D. Obradovic -- Special Session: Non-linear Predictive Models for Speech Processing Organizers and Chairs: M. Chetouani, M. Faundez-Zanuy, B. Gas, A. Hussain -- Special Session: Intelligent Multimedia and Semantics Organizers and Chairs: Y. Avrithis, S. Kollias -- Applications to Natural Language Proceesing -- Various Applications -- Special Session: Computational Intelligence in Games Organizer and Chair: J. Ma´ndziuk -- Issues in Hardware Implementation -- Erratum. |
| Record Nr. | UNINA-9910483490203321 |
| Berlin, : Springer, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Biophysics of computation [[electronic resource] ] : information processing in single neurons / / Christof Koch
| Biophysics of computation [[electronic resource] ] : information processing in single neurons / / Christof Koch |
| Autore | Koch Christof <1956-> |
| Pubbl/distr/stampa | New York, : Oxford University Press, 1999 |
| Descrizione fisica | 1 online resource (587 p.) |
| Disciplina | 573.8/536 |
| Collana | Computational neuroscience |
| Soggetto topico |
Computational neuroscience
Neurons Neural networks (Neurobiology) Action potentials (Electrophysiology) Neural conduction |
| Soggetto genere / forma | Electronic books. |
| ISBN |
0-19-756233-7
1-280-59509-4 9786613624925 0-19-976055-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 |
| Record Nr. | UNINA-9910457836903321 |
|
Koch Christof <1956->
|
||
| New York, : Oxford University Press, 1999 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Biophysics of computation [[electronic resource] ] : information processing in single neurons / / Christof Koch
| Biophysics of computation [[electronic resource] ] : information processing in single neurons / / Christof Koch |
| Autore | Koch Christof <1956-> |
| Pubbl/distr/stampa | New York, : Oxford University Press, 1999 |
| Descrizione fisica | 1 online resource (587 p.) |
| Disciplina | 573.8/536 |
| Collana | Computational neuroscience |
| Soggetto topico |
Computational neuroscience
Neurons Neural networks (Neurobiology) Action potentials (Electrophysiology) Neural conduction |
| ISBN |
0-19-029285-7
0-19-756233-7 1-280-59509-4 9786613624925 0-19-976055-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 |
| Record Nr. | UNINA-9910779059703321 |
|
Koch Christof <1956->
|
||
| New York, : Oxford University Press, 1999 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Brain, vision, and artificial intelligence : first international symposium, BVAI 2005, Naples, Italy, October 19-21, 2005 : proceedings / / Massimo De Gregorio ... [et al.] (eds.)
| Brain, vision, and artificial intelligence : first international symposium, BVAI 2005, Naples, Italy, October 19-21, 2005 : proceedings / / Massimo De Gregorio ... [et al.] (eds.) |
| Edizione | [1st ed. 2005.] |
| Pubbl/distr/stampa | Berlin ; ; New York, : Springer, c2005 |
| Descrizione fisica | 1 online resource (XV, 556 p.) |
| Disciplina | 612.8/2 |
| Altri autori (Persone) | De GregorioMassimo |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Neural networks (Neurobiology)
Neural networks (Computer science) Computer vision Optical pattern recognition Artificial intelligence |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Brain Basics -- Towards a Dynamic Neuropharmacology: Integrating Network and Receptor Levels -- Cortical Architecture -- Unsupervised Recognition of Neuronal Discharge Waveforms for On-line Real-Time Operation -- Neural Connectivity and Dynamical Regimes of Neural Activity in a Network of Interactive Populations -- Signal Transmission and Synchrony Detection in a Network of Inhibitory Interneurons -- Stimulus-Driven Unsupervised Synaptic Pruning in Large Neural Networks -- Inverse First Passage Time Method in the Analysis of Neuronal Interspike Intervals of Neurons Characterized by Time Varying Dynamics -- Coding by Neural Population Oscillations? -- Neural Code and Irregular Spike Trains -- Natural Vision -- Basic Retinal Circuitry in Health and Disease -- Does a Plane Imitate a Bird? Does Computer Vision Have to Follow Biological Paradigms? -- A Neural Model of Human Object Recognition Development -- Photoelectric Response of Bacteriorhodopsin in Thin PVA Films and Its Model -- Non-image Forming Function of the Extraocular Photoreceptors in the Ganglion of the Sea Slug Onchidium -- A Population-Based Inference Framework for Feature-Based Attention in Natural Scenes -- Multi-modal Primitives as Functional Models of Hyper-columns and Their Use for Contextual Integration -- Three Dilemmas of Signal- and Symbol-Based Representations in Computer Vision -- An LGN Inspired Detect/Transmit Framework for High Fidelity Relay of Visual Information with Limited Bandwidth -- On Cognitive Dynamic Map and Its Use for Navigation in Space -- Visual Selection in Human Frontal Eye Fields -- Multi-scale Keypoints in V1 and Face Detection -- Limitation of Maintenance of Feature-Bound Objects in Visual Working Memory -- Molecular and Functional Diversity of Visual Pigments: Clues from the Photosensitive Opsin–Like Proteins of the Animal Model Hydra -- Learning Location Invariance for Object Recognition and Localization -- Artificial Inteligence -- Enacted Theories of Visual Awareness: A Neuromodelling Analysis -- Mind as an Anticipatory Device: For a Theory of Expectations -- A Cognitive Model for Autonomous Agents Based on Bayesian Programming -- A Structural Learning Algorithm and Its Application to Predictive Toxicology Evaluation -- Combining Rule-Based and Sample-Based Classifiers – Probabilistic Approach -- Mapping and Combining Combinatorial Problems into Energy Landscapes via Pseudo-Boolean Constraints -- Robust Ellipse-Specific Fitting for Real-Time Machine Vision -- A Feature-Based Model of Semantic Memory: The Importance of Being Chaotic -- Semantic Web Services with SOUL -- Artificial Vision -- Modeling the Brain’s Operating System -- A Bayesian Approach to Situated Vision -- A Fuzzy Scale-Space Approach to Feature-Based Image Representation and Retrieval -- Active Acquisition of 3D Map in Robot Brain by Combining Motion and Perceived Images -- Lateral Interaction in Accumulative Computation: Motion-Based Grouping Method -- Detecting and Ranking Foreground Regions in Gray-Level Images -- Incomplete Contour Representations and Shape Descriptors: ICR Test Studies -- Rotation and Scale Invariant Shape Description Using the Contour Segment Curvature -- A Study on Enhanced Dynamic Signature Verification for the Embedded System -- Spatio-temporal Attention Mechanism for More Complex Analysis to Track Multiple Objects -- Stereovision Disparity Analysis by Two-Dimensional Motion Charge Map Inspired in Neurobiology -- Image Analysis and Automatic Surface Identification by a Bi-level Multi-classifier -- Real Time Virtualized Real Object Manipulation in an Augmented Reality Environment -- Dominant Plane Detection Using Optical Flow and Independent Component Analysis -- Algorithm That Mimics Human Perceptual Grouping of Dot Patterns -- Heuristic Algorithms for Fast and Accurate Tracking of Moving Objects in Unrestricted Environments -- Single Image Estimation of Facial Albedo Maps -- Optical Flow Computation for Compound Eyes: Variational Analysis of Omni-Directional Views -- Latent Semantic Description of Iconic Scenes -- Pose-Invariant Face Recognition Using Deformation Analysis. |
| Altri titoli varianti | BVAI 2005 |
| Record Nr. | UNINA-9910484252003321 |
| Berlin ; ; New York, : Springer, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
