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Principles Of Development Wolpert Pdf Free


Principles of developmental biology Wolpert pdf has 14 different chapters. Chapter 1 provides a brief history of embryology and an introduction to some of the main general principles and processes involved. Chapter 2 considers the process of pattern formation in laying down the body plan in the Drosophila. This small fly has played and still plays, a central role in elucidating developmental mechanisms.




principles of development wolpert pdf free



The mechanisms involved in pattern formation in the early development of our vertebrate model organisms are considered in Chapters 4 and 5. These chapters are organized as in the previous edition, with the process of laying down the early body plan being first described in its entirety in Xenopus (Chapter 4), the vertebrate in which the general principles were discovered. This is followed by comparisons with the process in the zebrafish (Chapter 4), and in chick and mouse (Chapter 5). Chapter 5 also considers how the body plan is completed, which mainly rests on studies in chick and mouse embryos.


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Principles of Development is designed for undergraduates and the emphasis is on principles and key concepts. Central to our approach is that development can be best understood by understanding how genes control cell behavior. We have assumed that the students have some very basic familiarity with cell biology and genetics, but all key concepts, such as the control of gene activity, are explained in the text.


We have resisted the temptation to cover every aspect of development and have, instead, focused on those systems that best illuminate common principles. Indeed, a theme that runs throughout the book is that universal principles govern the process of development. At all stages, what we included has been guided by what we believe undergraduates should know about development.


The brain attenuates its responses to self-produced exteroceptions (e.g., we cannot tickle ourselves). Is this phenomenon, known as sensory attenuation, enabled innately, or acquired through learning? Here, our simulation study using a multimodal hierarchical recurrent neural network model, based on variational free-energy minimization, shows that a mechanism for sensory attenuation can develop through learning of two distinct types of sensorimotor experience, involving self-produced or externally produced exteroceptions. For each sensorimotor context, a particular free-energy state emerged through interaction between top-down prediction with precision and bottom-up sensory prediction error from each sensory area. The executive area in the network served as an information hub. Consequently, shifts between the two sensorimotor contexts triggered transitions from one free-energy state to another in the network via executive control, which caused shifts between attenuating and amplifying prediction-error-induced responses in the sensory areas. This study situates emergence of sensory attenuation (or self-other distinction) in development of distinct free-energy states in the dynamic hierarchical neural system.


Furthermore, we analyzed how attenuation of neural responses in sensory areas developed during the learning process. The RNN first increased sensory-level posterior responses to reconstruct target sensorimotor sequences (Fig. 4c). Then, sensory-level posterior responses in the self-produced context were gradually attenuated in both exteroceptive and proprioceptive areas. The sensory-level prior sigma diminished more in the self-produced context than in the externally produced context through the learning process (Fig. 4d). We confirmed that posterior responses in the association area were similar in self-produced and externally produced contexts (Supplementary Fig. S3a,b), indicating reduced total neural response in the self-produced context. This sensory attenuation was accompanied by recognition of both contexts in the executive area (Supplementary Fig. S3c,d). These results demonstrate emergence of sensory attenuation through a learning process via free-energy minimization. Additional analyses indicated that development of sensory attenuation was diminished by removing neurons in the association or executive area (Supplementary Fig. S4), suggesting the importance of a higher-level representation of sensorimotor correlation.


Furthermore, the error induced by the change of the sensorimotor context flowed bottom-up to the executive area and determined the posterior in the executive area with its prior set to a neutral value. It triggered the transition from one free-energy state to another, i.e., from sensory attenuation to sensory amplification and verse-versa. In short, precision structures for sensory attenuation in self-produced contexts and sensory amplification in externally produced contexts were self-organized in one hierarchical RNN and were switched via executive control. This suggests that the hierarchical RNN developed a type of functionality of switching between quasi-deterministic and highly stochastic dynamics in each local area, in which sensory attenuation was characterized by quasi-deterministic processing (nearly 0 sigma) in the sensory areas and highly stochastic processing in the association area, while sensory amplification was characterized by highly stochastic processing in both the sensory areas and the association area. This sort of development and transitions of distinct free-energy states provides insights into how perceptual phenomena emerge from dynamic brain-body-environment interaction in the face of uncertainty.


There have been prior model proposals to account for sensory attenuation. One proposal13 postulates that sensory attenuation is caused by reducing the precision of the prediction error bottom-up to the sensory area during movement by following the free-energy principle. This model, however, does not explain the involvement of the higher executive area, as evidenced by19,20. We confirmed that removal of the executive area diminished the development of sensory attenuation (Supplementary Fig. S4b), emphasizing the contribution of the frontal function. This is consistent with biological studies suggesting that signals from the frontal area, such as the supplementary motor area, predictably control the relative precision or intensity of sensations. Their disruption causes diminished sensory attenuation19,26. Furthermore, the previous model intermixes two phenomena: sensory attenuation and sensory gating. Sensory attenuation compares the intensity of self-produced sensations with externally produced sensations, or the distinction between the self and others. On the other hand, sensory gating refers to a suppression process in which exteroceptions feel weaker during movement than at rest27. In our learning experiment, movements of the robot in self-produced contexts and externally produced contexts were the same, avoiding the confusion with sensory gating. In this sense, our model considered only sensory attenuation (self-other distinction).


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