Computational Neuroscience

“Computational Neuroscience” is an essential guide to understanding the fascinating intersection of neuroscience and robotics. This book presents key concepts, models, and theories in computational neuroscience, exploring how biological processes inspire robotic and AI systems.

1: Computational neuroscience: Explore the interdisciplinary field of computational neuroscience, examining the role of mathematical models and simulations in understanding neural systems.

2: Neuroscience: Understand the fundamental principles of neuroscience, focusing on brain structure and function, and its relationship with robotics.

3: Bioinspired computing: Discover how biological processes inspire new computational models, contributing to the design of artificial intelligence systems.

4: Neuromorphic computing: Investigate neuromorphic computing, where computing systems are modeled after the brain’s architecture, enabling more efficient processing.

5: Behavioral neuroscience: Learn about how behavior is driven by neural systems, with a focus on decisionmaking and cognitive processes in robotics.

6: Binding problem: Delve into the binding problem, a challenge in neuroscience that addresses how the brain integrates disparate information into a cohesive experience.

7: Christof Koch: Explore the work of Christof Koch and his contributions to understanding consciousness and the brain’s neural processes.

8: Neural network (biology): Examine biological neural networks and their implications for artificial neural network models used in robotics and AI systems.

9: Metastability in the brain: Understand the concept of metastability, describing the brain's ability to remain in multiple states, aiding its adaptability.

10: Neural oscillation: Study neural oscillations and their role in coordinating brain activity, providing insight into brain wave interactions with robotics.

11: Neuroinformatics: Learn about neuroinformatics and its role in data management and analysis of brain activity to model neural processes.

12: David Heeger: Dive into the contributions of David Heeger in understanding brain processing and computational models used in neuroscience.

13: Brain simulation: Gain insights into brain simulation technologies that model the brain’s complexity and their applications in robotics.

14: Models of neural computation: Investigate various models of neural computation, exploring how algorithms mimic brain functions in robotic systems.

15: Dynamical neuroscience: Learn how dynamic systems theory applies to neuroscience, enhancing understanding of brain activity in robotics.

16: Dehaene–Changeux model: Explore the Dehaene–Changeux model of brain functioning, linking cognition with neural circuits in robots.

17: Nervous system network models: Understand how network models of the nervous system contribute to developing more efficient robotic systems.

18: Predictive coding: Discover predictive coding and its relevance in understanding perception, learning, and decisionmaking in both the brain and robotics.

19: Simon Stringer: Explore Simon Stringer’s research in computational neuroscience and its influence on developing braininspired robotic models.

20: Kanaka Rajan: Examine Kanaka Rajan’s work in applying computational neuroscience to develop more robust and adaptive robotic systems.

21: V1 Saliency Hypothesis: Delve into the V1 Saliency Hypothesis, which focuses on how the brain processes visual attention and its implications for robotics and AI.