What is a digital state?

There is much discussion about whether the human mind is a computer, whether the human brain could be emulated on a computer, and whether at all physical entities are computers (pancomputationalism). These discussions, and others, require criteria for what is digital. I propose that a state is digital if and only if it is a token of a type that serves a particular function - typically a representational function for the system. This proposal is made on a syntactic level, assuming three levels of description (physical, syntactic, semantic). It suggests that being digital is a matter of discovery or rather a matter of how we wish to describe the world, if a functional description can be assumed. Given the criterion provided and the necessary empirical research, we should be in a position to decide on a given system (e.g. the human brain) whether it is a digital system and can thus be reproduced in a different digital system (since digital systems allow multiple realization)

To what extent is digit patterning a Turing System?

Building precise, robust patterns and structures from an initially homogeneous state is fundamental to developmental biology. Digit patterning is a representative example of a periodic pattern in development. Previous studies have shown that a reaction–diffusion (Turing) system, in which diffusible activators and inhibitors interact, is the most likely explanation of how the spatial pattern of the digits is formed. Although self-organisation mechanisms such as the Turing system successfully recapitulate many aspects of digit patterning, critical questions remain regarding its timing and behaviour. First I addressed the question of timing, or how long reaction-diffusion plays a role in the developing digits. I perturbed the digit patterning process of embryonic limbs by inserting beads that contain morphogens involved in the reaction-diffusion mechanism. Then I quantified the degree of pattern change, or plasticity of the patterning, from limbs harvested at different developmental timing throughout the digit patterning stage. For quantification, I developed a custom image analytic pipeline that extracts relevant topology and represents the difference between perturbed and unperturbed patterns. Modelling the plasticity profile over the digit patterning process, through extensive interplay of experiments and modelling, revealed that plasticity during digit patterning decreases in a sigmoidal manner. Transcriptomics analysis that matches with the sigmoidal decrease observed in expression patterns further identified gene candidates that could be critical to the digit patterning. Further, the timing of reaction-diffusion is discussed in the context of the tissue movements, revealing that Sox9 digit patterning happens significantly earlier than cell density changes. The second part aims at improving our understanding about which pathways and components of the pathways are involved in the digit forming Turing network. Previously identified digit patterning Turing network, such as BSW model, abstracts the entire Wnt and Bmp signalling pathways’ activities into each node. Thus there is insufficient knowledge on the mechanistic role of Wnt signalling mediated Sox9 repression. To further clarify detailed mechanisms of the Turing network, I used an unbiased screening approach to systematically perturb digit patterning using small molecule inhibitors, ligands, and peptides at different doses in systems such as limb culture and micromass. Out of multiple steps critical to Wnt signalling, including Wnt production, Wnt receptor interaction, Wnt canonical pathway cytosolic interactions, and Wnt canonical pathway transcriptional interactions, I identified that inhibition of Wnt production and Wnt transcriptional component inhibition category most effectively disrupt digit patterning. I also identified candidate ligands such as sFRP1 and Dkk1 as potential extracellular Wnt inhibitors that effectively change digit patterning upon application. These results provide the first quantitative insight into the duration of the reaction-diffusion based mechanism in a biological system, and how a screening approach complemented with data driven modelling can complement and clarify workings of a reaction diffusion based system. Further work in improving our knowledge on the Turing system with tissue growth, cell movements, and ectodermal-mesenchymal interaction will eventually allow generation of a complete organogenesis simulation model

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Violent video games and morality: a meta-ethical approach

This paper considers what it is about violent video games that leads one reasonably minded person to declare "That is immoral" while another denies it. Three interpretations of video game content a re discussed: reductionist, narrow, and broad. It is argued that a broad interpretation is required for a moral objection to be justified. It is further argued that understanding the meaning of moral utterances – like "x is immoral" – is important to an understanding of why there is a lack of moral consensus when it comes to the content of violent video games. Constructive ecumenical expressivism is presented as a means of explaining what it is that we are doing when we make moral pronouncements and why, when it comes to video game content, differing moral attitudes abound. Constructive ecumenical expressivism is also presented as a means of illuminating what would be required for moral consensus to be achieved

Representing an Object by Interchanging What with Where

Exploring representations is a fundamental step towards understanding vision. The visual system carries two types of information along separate pathways: One is about what it is and the other is about where it is. Initially, the what is represented by a pattern of activity that is distributed across millions of photoreceptors, whereas the where is 'implicitly' given as their retinotopic positions. Many computational theories of object recognition rely on such pixel-based representations, but they are insufficient to learn spatial information such as position and size due to the implicit encoding of the where information. 
Here we try transforming a retinal image of an object into its internal image via interchanging the what with the where, which means that patterns of intensity in internal image describe the spatial information rather than the object information. To be concrete, the retinal image of an object is deformed and turned over into a negative image, in which light areas appear dark and vice versa, and the object's spatial information is quantified with levels of intensity on borders of that image. 
Interestingly, the inner part excluding the borders of the internal image shows the position and scale invariance. In order to further understand how the internal image associates the what and where, we examined the internal image of a face which moves or is scaled on the retina. As a result, we found that the internal images form a linear vector space under the object translation and scaling. 
In conclusion, these results show that the what-where interchangeability might play an important role for organizing those two into internal representation of brain

Normality: Part Descriptive, part prescriptive

People’s beliefs about normality play an important role in many aspects of cognition and life (e.g., causal cognition, linguistic semantics, cooperative behavior). But how do people determine what sorts of things are normal in the first place? Past research has studied both people’s representations of statistical norms (e.g., the average) and their representations of prescriptive norms (e.g., the ideal). Four studies suggest that people’s notion of normality incorporates both of these types of norms. In particular, people’s representations of what is normal were found to be influenced both by what they believed to be descriptively average and by what they believed to be prescriptively ideal. This is shown across three domains: people’s use of the word ‘‘normal” (Study 1), their use of gradable adjectives (Study 2), and their judgments of concept prototypicality (Study 3). A final study investigated the learning of normality for a novel category, showing that people actively combine statistical and prescriptive information they have learned into an undifferentiated notion of what is normal (Study 4). Taken together, these findings may help to explain how moral norms impact the acquisition of normality and, conversely, how normality impacts the acquisition of moral norms