Description
Computational neuroscience is a relatively new but rapidly expanding area of research which is becoming increasingly influential in shaping the way scientists think about the brain. Computational approaches have been applied at all levels of analysis, from detailed models of single-channel function, transmembrane currents, single-cell electrical activity, and neural signaling to broad theories of sensory perception, memory, and cognition. This book provides a snapshot of this exciting new field by bringing together chapters on a diversity of topics from some of its most important contributors. This includes chapters on neural coding in single cells, in small networks, and across the entire cerebral cortex, visual processing from the retina to object recognition, neural processing of auditory, vestibular, and electromagnetic stimuli, pattern generation, voluntary movement and posture, motor learning, decision-making and cognition, and algorithms for pattern recognition. Each chapter provides a bridge between a body of data on neural function and a mathematical approach used to interpret and explain that data. These contributions demonstrate how computational approaches have become an essential tool which is integral in many aspects of brain science, from the interpretation of data to the design of new experiments, and to the growth of our understanding of neural function.
• Includes contributions by some of the most influential people in the field of computational neuro
Chapter
Chapter 3. The dynamics of visual responses in the primary visual cortex
pp.:
36 – 48
Chapter 4. A quantitative theory of immediate visual recognition
pp.:
48 – 72
Chapter 5. Attention in hierarchical models of object recognition
pp.:
72 – 94
Chapter 6. Towards a unified theory of neocortex: laminar cortical circuits for vision and cognition
pp.:
94 – 120
Chapter 7. Real-time neural coding of memory
pp.:
120 – 138
Chapter 8. Beyond timing in the auditory brainstem: intensity coding in the avian cochlear nucleus angularis
pp.:
138 – 150
Chapter 9. Neural strategies for optimal processing of sensory signals
pp.:
150 – 170
Chapter 10. Coordinate transformations and sensory integration in the detection of spatial orientation and self-motion: from models to experiments
pp.:
170 – 196
Chapter 11. Sensorimotor optimization in higher dimensions
pp.:
196 – 208
Chapter 12. How tightly tuned are network parameters? Insight from computational and experimental studies in small rhythmic motor networks
pp.:
208 – 216
Chapter 13. Spatial organization and state-dependent mechanisms for respiratory rhythm and pattern generation
pp.:
216 – 236
Chapter 14. Modeling a vertebrate motor system: pattern generation, steering and control of body orientation
pp.:
236 – 250
Chapter 15. Modeling the mammalian locomotor CPG: insights from mistakes and perturbations
pp.:
250 – 270
Chapter 16. The neuromechanical tuning hypothesis
pp.:
270 – 282
Chapter 17. Threshold position control and the principle of minimal interaction in motor actions
pp.:
282 – 298
Chapter 18. Modeling sensorimotor control of human upright stance
pp.:
298 – 314
Chapter 19. Dimensional reduction in sensorimotor systems: a framework for understanding muscle coordination of posture
pp.:
314 – 338
Chapter 20. Primitives, premotor drives, and pattern generation: a combined computational and neuroethological perspective
pp.:
338 – 362
Chapter 21. A multi-level approach to understanding upper limb function
pp.:
362 – 378
Chapter 22. How is somatosensory information used to adapt to changes in the mechanical environment?
pp.:
378 – 388
Chapter 23. Trial-by-trial motor adaptation: a window into elemental neural computation
pp.:
388 – 398
Chapter 24. Towards a computational neuropsychology of action
pp.:
398 – 410
Chapter 25. Motor control in a meta-network with attractor dynamics
pp.:
410 – 426
Chapter 26. Computing movement geometry: a step in sensory-motor transformations
pp.:
426 – 440
Chapter 27. Dynamics systems vs. optimal control „ a unifying view
pp.:
440 – 462
Chapter 28. The place of ’codes’ in nonlinear neurodynamics
pp.:
462 – 478
Chapter 29. From a representation of behavior to the concept of cognitive syntax: a theoretical framework
pp.:
478 – 490
Chapter 30. A parallel framework for interactive behavior
pp.:
490 – 508
Chapter 31. Statistical models for neural encoding, decoding, and optimal stimulus design
pp.:
508 – 524
Chapter 32. Probabilistic population codes and the exponential family of distributions
pp.:
524 – 536
Chapter 33. On the challenge of learning complex functions
pp.:
536 – 550
Chapter 34. To recognize shapes, first learn to generate images
pp.:
550 – 572