Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition

Hybrid Neural Network Predictive-Wavelet Image Compression System

Scalable coding is potentially useful in content distribution over unreliable channels, as it enables meaningful reconstruction when the hierarchical bitstream is only partially received. However, its deployment in conjunction with predictive coding may result in considerable performance degradation when errors due to packet loss propagate through the prediction loop. Despite this, most existing predictive scalable coding techniques employ components whose design completely ignores the effects of unreliable channel conditions. This paper proposes an efficient design technique for predictive scalable coding systems, which effectively accounts for: i) all available information at a given layer by optimizing its prediction parameters within an estimation-theoretic framework; ii) the uncertainty due to packet loss via estimation and minimization of end-to-end distortion. It further leverages an asymptotic closed loop design technique for the predictor and quantizer modules, which provides the stability benefits of open-loop design, while ultimately optimizing the system for closed-loop operation. Experimental results provide compelling evidence for the effectiveness of the approach, with considerable performance gains over existing design techniques, in terms of end-to-end signal-to-noise ratio.

This paper does not deal with the topic of ‘the generosity of artificial languages from an Asian or a comparative perspective’. Rather, it is concerned with a particular case taken from a development in the Western tradition, when in the wake of the rise of formal logic at the end of the nineteenth and the beginning of the twentieth century people in philosophy and later in linguistics started to use formal languages in the study of the semantics of natural languages. This undertaking rests on certain philosophical assumptions and instantiates a particular methodology, that we want to examine critically. However, that in itself is still too broad a topic for a single paper, so we will focus on a particular aspect, viz., the distinction between grammatical form and logical form and the crucial role it plays in how the relationship between natural languages and formal languages is understood in this tradition. We will uncover two basic assumptions that underlie the standard view on the distinction between grammatical form and logical form, and discuss how they have contributed to the shaping of a particular methodology and a particular view on the status of semantics as a discipline. But before we turn to the topic at hand, a few more words on the general nature of the investigation are in order. Its general aim is to find out how semantics constructs its object, i.e., we are interested in what semanticists view as the proper object of study, how they think this object can best be approached, and how they view the relations between their own undertaking and neighbouring disciplines that deal with related, or even the same kind of phenomena, such as cognitive psychology, cognitive neuroscience, philosophy, anthropology. The background assumption is that, much like in other disciplines, semantics, too, does not have its object of investigation cut out for itself by nature, but constructs it in a complex process that involves empirical elements (‘facts’ being already too dangerous a term), philosophical assumptions, and borrowings

Recently, the exploration of human brain function was examined in cognitive neuroscience and brain science. Along with the development of engineering technology, such as measurement technology, information technology, and artificial intelligence, we can record the brain activity on the timeframe of milliseconds or at the level of a single neuron to examine basic visual function or high level brain function (e.g., memory, language, or attention). In this chapter, we introduce a new research field that combines engineering and cognitive neuroscience, which was named Neuromedical Engineering. We focused our research on five topics: 1. tactile perception and neurology (Yang et al., 2011a, 2011b, 2011c, 2010a, 2011; Wu et al., 2010a, 2011); 2. attention and cognitive brain function (Wu & Li, 2009) and multisensory integration (Li et al, 2009a, 2010a, 2010b; Touge et al., 2008; Wu & Kakura, 2010; Wu et al., 2009); 3. basic visual cognition (Li et al., 2009b; Wu et al., 2011; Yan et al., 2011); 4. language (Cai et al., 2007; Li et al., 2011a, 2011b; Wu et al., 2007); and 5. tactile perception and rehabilitation (Bai et al., 2010). Focusing on these five topics, we provide information on human brain function and neurology. Furthermore, using these cognitive and neurological methods, we are challenging the topic of “Early Detection of Alzheimer’s Disease with Cognitive Neuroscience Methods”. The earliest symptoms of Alzheimer’s disease (AD) involves learning, memory or planning problems. Currently, no medical tests are available to diagnose AD conclusively premortem. However, several studies have used cognitive tasks (i.e., visuospatial tasks and language tasks) to discover preclinical cognitive markers of AD. These studies demonstrated that the cognitive deficits of AD can possibly be detected during a preclinical period that spans several years. In addition, numerous neuropathological, electrophysiological and neuroimaging studies support the hypothesis that cognitive deficits in AD are related to a possible disconnection between cortical areas. In this chapter, we describe current studies and possible future experiments on the early diagnosis method using cognitive and functional imaging testing to help with the clinical diagnosis of AD. People with AD die an average of four to six years after diagnosis, but the duration of the disease can vary from three to 20 years. As shown in Fig. 1, the rate of cognitive decline in patients with AD was faster than healthy subjects after a specific point (before diagnosis). This specific point may be detectable using memory and planning tasks, such as visuospatial and language tasks. Recent studies have demonstrated that the cognitive deficits of AD can be detected using some simplex cognitive tests.

Advances in Artificial Intelligence and robotics are currently questioning the ethical framework of their applications to deal with potential drifts, as well as the way in which these algorithms learn because they will have a strong impact on the behavior of robots and the type of robots. interactions with people. We would like to highlight some principles and ideas from cognitive neuroscience and development sciences based on the importance of the body for intelligence, contrary to the theory of the all-brain or all- algorithm, to represent the world and interacting with others, and their current applications in embodied AI and developmental robotics to propose models of architectures and mechanisms for agency, representation of the body, recognition of the intention of others, predictive coding, active inference, the role of feedback and error, imitation, artificial curiosity and contextual learning. We will explain how these are important for the design of autonomous systems and beyond what they can tell us for the ethics of systems.

A crucial question within science and academia, and cognitive science specifically, is whether there is gender disparity in opportunity and advancement over the professional life span (e.g., Ceci, Ginther, Kahn, & Williams, 2014; Geraci, Balsis, & Busch, 2015; Valian, 1998). To investigate this question, we analyzed gender distributions in publicly available federal funding data from the Natural Sciences and Engineering Research Council (NSERC) of Canada that are specific to cognitive psychology and cognitive neuroscience. There were three key results. First, the proportion of women cognitive scientists progressively diminished at each career stage, particularly at the transition between graduate and postdoctoral studies. Second, female principal investigators (PI) received smaller average Discovery Grant amounts and were less likely to receive Discovery Accelerator Supplements as a proportion of all Discovery Grants funded. Finally, at the PI level, gender differences were relatively smaller for institution-initiated grants (i.e., Canada Research Chairs) versus investigator-initiated grants (i.e., Discovery Grants). It is our hope that presentation of such data, in concert with other recent reports for our field (e.g., Klatzky, Holt, & Behrmann, 2015; Peelle, 2016; Vaid & Geraci, 2016), continues to raise awareness that gender parity issues remain a concern that deserves ongoing attention within the field of cognitive science in Canada. (PsycINFO Database Record

We study the effects of electrical and chemical autapse on the temporal coherence or firing regularity of single stochastic Hodgkin-Huxley neurons and scale-free neuronal networks. Also, we study the effects of chemical autapse on the occurrence of spatial synchronization in scale-free neuronal networks. Irrespective of the type of autapse, we observe autaptic time delay induced multiple coherence resonance for appropriately tuned autaptic conductance levels in single neurons. More precisely, we show that in the presence of an electrical autapse, there is an optimal intensity of channel noise inducing the multiple coherence resonance, whereas in the presence of chemical autapse the occurrence of multiple coherence resonance is less sensitive to the channel noise intensity. At the network level, we find autaptic time delay induced multiple coherence resonance and synchronization transitions, occurring at approximately the same delay lengths. We show that these two phenomena can arise only at a specific range of the coupling strength, and that they can be observed independently of the average degree of the network.

Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.

The forest for the trees: Comment on “Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition” by W.T. Fitch

Brains are not just neurons: Comment on “Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition” by Fitch

Predictive coding of video has traditionally used information from previous video frames to help construct an estimate of the current frame. The difference between the original and estimated signal can then be transmitted to allow the receiver to fully reconstruct the original video frame. In this paper, we explore a new algorithm for use in coding the shape of a person’s lips in a head-and-shoulder video sequence. This algorithm uses the same predictive coding loop, but instead of forming an estimate of the lip image using motion compensation and previous video frames, it forms an estimate from the associated acoustic data. Since the acoustic data is also transmitted, the receiver is able to reconstruct the video with very little side information. In this paper, we will describe our predictive coding system and analyze methods for converting from the acoustic data to visual estimates.

This chapter discusses the concepts from the general philosophy of science for those interested in cognitive neuroscience. It covers several major topics in the philosophy of science: scientific explanation and underdetermination, reductionism and levels, and scientific realism. The debates in philosophy of science were largely set in the early-to-mid-1900s by a group of philosophers known as the logical positivists. Logical positivists criticized philosophical questions that could be solved by empirical evidence, so claims about strangers' inner minds could not be a proper subject of science. Logical positivism was largely abandoned by philosophers of science who came to doubt their commitments. Instead of this “layer cake” model, many philosophers now prefer the view of sciences as investigating overlapping subject matter according to their own principles, theories, or methods. Cognitive science, neuroscience, psychology, and related disciplines may all treat different aspects of the same phenomenon or overlapping phenomena.

In this article, we bring together recent findings from developmental science and cognitive neuroscience to argue that perception-action coupling constitutes the fundamental mechanism of motor cognition. A variety of empirical evidence suggests that observed and executed actions are coded in a common cognitive and neural framework, enabling individuals to construct shared representations of self and other actions. We review work to suggest that such shared representations support action anticipation, organization, and imitation. These processes, along with additional computational mechanisms for determining a sense of agency and behavioral regulation, form the fabric of socialinteraction. In addition, humans possess the capacity to move beyond these basic aspects of action analysis to interpret behavior at a deeper level, an ability that may be outside the scope of the mirror system. Understanding the nature of shared representations from the vantage point of developmental and cognitive science and neuroscience has the potential to inform a range of motor and social processes. This perspective also elucidates intriguing new directions and research questions and generates specific hypotheses regarding the impact of early disorders (e.g., developmental movement disorders) on subsequent action processing.

You know the story. You have a few intuitions. You propose a few theories that fit them. It's a living. Of course, things are more complicated than this. We are sensitive to counterexamples raised by others and wish to accommodate or explain away an ever-wider base of intuitive starting points. A great deal of the action occurs in rational reflection that can alter what is intuitive, and in theorizing that overturns formerly justified beliefs and moves us to new justified beliefs. Details aside, this method in ethics and elsewhere—of first relying on intuitions to form justified beliefs, and subsequently using best-fit (or reflective equilibrium) theorizing on all justified beliefs to move to other justified beliefs—has received a lot of critical attention lately. But it is not a bad method. It is a good method caught in a bad relationship. For its presumptive meta-ethical companion, realism, would have us believe that intuitions support beliefs about real, stance-independent facts of the matter. That strikes many as dubious. After sorting through some relevant concerns in this vicinity, I argue that the solution is not to reject intuitional methods but to embrace quasi-realism.

Evo-devo is an approach that integrates knowledge on evolution and development. Cognitive science is a research field that tries to unravel the functioning of the mind and the underlying processes. In this chapter, the main subfields within cognitive science that have contributed to a better understanding of the evolution and development of the mind are discussed. Highlighted are the subfields of evolutionary cognitive science, developmental systems theory, genes × environment interaction research, epigenetics, comparative cognitive science, and cognitive neuroscience. Finally, the question what cognitive scientist can learn from research in evolutionary developmental biology is addressed. Many evo-devo biologists study morphogenesis, which is relevant for cognitive development, but it is not always straightforward how to apply their knowledge to cognitive science research. Interdisciplinary research is strongly recommended, so scholars from different fields such as morphology, genetics, neuroscience, primatology, and psychology can learn from each other and contribute to the unraveling of the working of the mind.