Computational Neuroscience

In the theaters of ancient Greece, and of Italy, the actors always wore a mask on the scene called Persona (πρóσωπον, [prósôpon]), in which the facial features were exaggerated in order to be better perceived by the spectators, and the mouth, was made so as to strengthen the sound of the voice to be heard louder, each Person was characterized by his own mask and...

NB: Out of Stock. The 2nd edition, entitled "Neurology: Self-Assessment for MRCP(UK) Neurology SCE", was published in 2023 and is found here: https://www.academia.edu/96771620/Neurology_Self_Assessment_for_MRCP_UK_Neurology_SCE
-----------------------------------------------------
You will find 792 questions of different formats, distributed into 3 chapters. The 215 multiple choice questions of chapter one will surf you onto a rapid review of information to consolidate your background knowledge of neuro-anatomy, neuro-physiology, neuro-pharmacology, neuro-pathology,…etc. In addition, testing the clinical aspects of various diseases is also included. Chapter two will self-assess you in terms of how to make a provisional diagnosis, what features refute or confirm your preliminary diagnosis, how to investigate, what to do next, the best treatment option, and the prognosis of various diseases. The photographic materials are found in chapter three. You will encounter various neurological complications/manifestations/signs of system diseases as well. Brain CT scan, MRI, MRV, and EEG images,…etc of common diseases will test your ability to interpret/read/analyze their diagnosis and management. About 490 pages, full-color, on white.

Coupled oscillator models use a single phase variable to approximate the voltage oscillation of each neuron during repetitive firing where the behavior of the model depends on the connectivity and the interaction function chosen to describe the coupling. We introduce a network model consisting of a continuum of these oscillators that includes the effects of spatially decaying coupling and axonal delay. We derive equations for determining the stability of solutions and analyze the network behavior for two different interaction functions. The first is a sine function, and the second is derived from a compartmental model of a pyramidal cell. In both cases, the system of coupled neural oscillators can undergo a bifurcation from synchronous oscillations to waves. The change in qualitative behavior is due to the axonal delay, which causes distant connections to encourage a phase shift between cells. We suggest that this mechanism could contribute to the behavior observed in several neurob...

This is one of the first books to study neural development using computational and mathematical modeling. Modeling provides precise and exact ways of expression, which allow us to go beyond the insights that intuitive or commonsense reasoning alone can yield. Most neural modeling focuses on information processing in the adult nervous system; Modeling Neural Development shows how models can be used to study the development of the nervous system at different levels of organization and at different phases of development, from molecule to system and from neurulation to cognition. The book's fourteen chapters follow loosely the chronology of neural development. Chapters 1 and 2 study the very early development of the nervous system, discussing gene networks, cell differentiation, and neural tube development. Chapters 3-5 examine neuronal morphogenesis and neurite outgrowth. Chapters 6-8 study different aspects of the self-organization of neurons into networks. Chapters 9-12 cover refinement of connectivity and the development of specific connectivity patterns. Chapters 13 and 14 focus on some of the functional implications of morphology and development. Each chapter contains an overview of the biology of the topic in question, a review of the modeling efforts in the field, a discussion in more detail of some of the models, and some perspectives on future theoretical and experimental work. The science of studying neural development by computational and mathematical modeling is relatively new; this book, as Dale Purves writes in the foreword, "serves as an important progress report" in the effort to understand the complexities of neural development.

Based on my PhD thesis published in 2003, the research reported in this work treats natural language as a physical system, subject to the same forces as other systems describable in physical terms and uses a computational approach to demonstrate.  The language of mechanical transformations, such as first-order phase transitions, is directly applicable to fundamental structural changes which occur during the spread of any given human natural language. 

While the research focus on the features emergent from the exchange dynamics of language evolution in human populations, it also highlights that similar features are mirrored within the language acquisition processes of individual human brains, from infant to adulthood. As such, language can be viewed as a window into neural functional activities which, in turn, can be described as a mechanical system. The study of evolution of natural language can thus be used to understand the dynamics of brain evolution and perhaps the emergence of what we like to call "consciousness".

In nature, first-order phase transitions are not rare, and often give rise to noticeable transformative phenomena, such as water turning into ice, when temperatures reach a specific low. As heat is removed from an H2O system, less energy is shared between molecules. In turn, molecules resist giving up heat, which require much energy. When heat is not longer available, they then settle into a low energy state, in this case, a lattice, in a dramatic display of crystallization.

The acquisition of language, as it spreads through a population, show striking similarities to heat distribution through a molecular or atomic system. Language is an embodied system which require our relation to the world to generate neural effects when we hear the use of words and sentences. As language spreads through a population the use of individual words become less relatable to specific physical experiences and thus become more vague. Through this loss of "meaning" bits of languages undergo structural transformations that affect the whole use of language much like removing heat from a physical system.

The creation of concepts requires integrating information coming from different sensory modal- ities. In real world learning scenarios explicit labels are often not provided, and modelling the mechanisms underlying the formation of complex categories by aligning multi-sensory information without explicit supervision is a challenging task. Numerous methods have been proposed to model this alignment process. The vast majority of these methods are designed for information in the form of manifolds embedded in Euclidean space. However, knowledge networks, e.g. Wikipedia, and complex symbolic datasets often exhibit a latent tree-like hierarchical structure, which is not optimally embedded in Euclidean space as it does not allow to optimally capture hierarchy and similarity relations between the embedded elements. Moreover, tree-like hierarchical datasets ex- hibit a type of invariance named branch permutation, or node order invariance, given by a lack of consistent ordering of branches in the embeddings of a dataset obtained from different runs of the same algorithm. It has recently been proven that hyperbolic space represents a much more efficient metric when embedding hierarchical data. For this reason, we propose an algorithm for semi-supervised manifold alignment in hyperbolic space with the aim of leveraging the properties of hyperbolic metric for a more efficient conceptual systems alignment. We believe we opened the way for a class of graph alignment methods in hyperbolic space which have the potential to drastically improve state-of-the-art manifold alignment methods. This could in turn provide an alternative method for tackling tasks such as transfer learning, cross-lingual information re- trieval and more. Additionally, it could provide a better understanding of how the alignment of conceptual systems is performed in the brain.

The hippocampal formation presents a special opportunity for realistic neural modeling since its structure, connectivity, and physiology are better understood than that of other cortical components. A review of the quantitative neuroanatomy of the rodent dentate gyrus (DG) is presented in the context of the development of a computational model of its connectivity. The DG is a three-layered folded sheet of neural tissue. This sheet is represented as a rectangle, having a surface area of 37 mm2 and a septotemporal length of 12 mm. Points, representing cell somata, are distributed in the model rectangle in a roughly uniform fashion. Synaptic connectivity is generated by assigning each presynaptic cell a spatial zone representing its axonal arbor. For each postsynaptic cell, a list of potential presynaptic cells is compiled, based on which arbor zones the given postsynaptic cell falls within. An appropriate number of presynaptic inputs are then selected at random. The principal cells of the DG, the granule cells, are represented in the model, as are non-principal cells, including basket cells, chandelier cells, mossy cells, and GABAergic peptidergic polymorphic (GPP) cells. The neurons of layer II of the entorhinal cortex are included also. The DG receives its main extrinsic input from these cells via the perforant path. The basket cells, chandelier cells, and GPP cells receive perforant path and granule cell input and exert both feedforward and feedback inhibition onto the granule cells. Mossy cells receive converging input from granule cells and send their output back primarily to distant septotemporal levels, where they contact both granule cells and non-principal cells. To permit numerical simulations, the model must be scaled down while preserving its anatomical structure. A variety of methods for doing this exist. Hippocampal allometry provides valuable clues in this regard.

In this study, we reported the reliability and validity of Bedside version of Persian WAB (P-WAB-1) adapted from Western Aphasia Battery (WAB-R) (1,2). P-WAB-1 is a clinical linguistic measuring tool to determine severity and type of aphasia in brain damaged patients based on Aphasia Quotient (AQ) as a functional measure. For the purposes of a quick clinical screening of aphasia in Persian, we adapted the bedside version of WAB-R to assess the performance of Persian aphasic patients. The data we reported on adaptation, validity and reliability of P-WAB-1 are based on faithful translation and criterion validity ratio (CVR) taken from the expert panel and the performance of 60 consecutive brain damaged patients referred to different university clinics for rehabilitation and 30 healthy subjects as norms and 40 age-matched epileptic patients as the control group. Based on the results of this study, P-WAB-1 has internal consistency (a=0.71) and test-retest reliability (r=.65 P<0.001) ...

—Mathematical and computational modellings are the necessary tools for reviewing, analysing, and predicting processes and events in the wide spectrum range of scientific fields. Therefore, in a field as rapidly developing as neuroscience, the combination of these two modellings can have a significant role in helping to guide the direction the field takes. The paper combined mathematical and computational modelling to prove a weakness in a very precious model in neuroscience. This paper is intended to analyse all-or-none principle in Hodgkin-Huxley mathematical model. By implementation the computational model of Hodgkin-Huxley model and applying the concept of all-or-none principle, an investigation on this mathematical model has been performed. The results clearly showed that the mathematical model of Hodgkin-Huxley does not observe this fundamental law in neurophysiology to generating action potentials. This study shows that further mathematical studies on the Hodgkin-Huxley model are needed in order to create a model without this weakness.

BACKGROUND:
In spite of its indusrial usefulness and varied daily uses, lead (Pb) pollution is a widespread ecological problem that faces the humans in the 21th century. Pb was found to produces a wide range of toxic effects including neurotoxicity especially to the developing and young offspring. Recently, the utilization of herbal plants has received a significant attention where there has been rising awareness in their therapeutic use; among these is the garlic.
AIM OF THE WORK:
In light of the above, the current study is designed experimentally in female pregnant rats in order to investigate the beneficial role of garlic extract in the protection from the maternal and fetal cerebellar damage that produced by administration of different doses of Pb during pregnancy.
MATERIALS AND METHODS:
Positively pregnant female rats were divided into five groups; one control group, two Pb-treated groups (exposed to 160 and 320 mg/kg b.wt. of Pb, respectively) and two groups treated with both Pb and garlic (exposed to Pb as previous groups together with 250 mg/ kg b.wt. /day of garlic extract). Treatments started from day 1 till day 20 of pregnancy, where the mother rats of different experimental groups were sacrified to obtain the fetuses. Pb level in the maternal nd fetal blood and cerebellum was estimated by spectrophotometry. Specimens of the cerebellum of different mother and fetal groups were processed to histological and immunohistochemical staining for microscopic examination.
RESULTS:
The results showed that administration of Pb to pregnant rats resulted in a dose-dependent toxicity for both mothers and fetuses in the form of decrease of maternal weight gain, placental and fetal weights, brain weight and diminished fetal growth parameters, which were prominent in rat's group treated with larger dose of Pb. In Pb-treated rats, Pb level in blood and cerebellum was high when compared to the control. The histopathological examination of the cerebellum of treated dams and fetuses showed marked alterations mainly in the form of Purkinje cell degeneration and lack of deveopmet of fetal cerebellum. Co-treatment of garlic extract along with Pb resulted in a significant decrease in Pb levels as compared with those treated with Pb alone with improvement of the histopathological changes.
CONCLUSIONS:
This study was useful in evaluating the hazardous effects of uncontrolled use of Pb in general and in assessing the developmental and neurotoxicity of fetuses due to exposure during pregnancy in particular. Co-administration of garlic has beneficial effects in amelioration of Pb-induced neurotoxicity and reversing the histopathological changes of the cerebellum of mother rats and fetuses.
KEYWORDS:
garlic; glial fibrillary acidic protein; lead; purkinje cells

This review considers some important landmarks in animal evolution, asking to what extent specialized action selection mechanisms play a role in the functional architecture of different nervous system plans, and looking for ‘forced moves’ or ‘good tricks’ (Dennett, 1995) that could possibly transfer to the design of robot control systems. A key conclusion is that while cnidarians (e.g. jellyfish) appear to have discovered some good tricks for the design of behavior-based control systems—largely lacking specialized selection mechanisms; the emergence of bilaterians may have forced the evolution of a central ganglion, or ‘archaic brain’, whose main function is to resolve conflicts between peripheral systems. Whilst vertebrates have many interesting selection substrates it is likely that here too, the evolution of centralized structures such as the medial reticular formation and the basal ganglia may have been a forced move due to the need to limit connection costs as brains increased in size.

Константин Владимирович Анохин (род. 3 октября 1957) — российский учёный, нейробиолог, профессор, член-корреспондент РАН и РАМН. Руководитель отдела нейронаук НИЦ «Курчатовский институт». Лауреат премий Ленинского комсомола, имени Девида Нидерландской академии наук, Президиума Российской академии медицинских наук и Национальной премии «Человек года» в номинации «Потенциал и перспектива в науке».
Окончил лечебный факультет 1-го Московского медицинского института им. И. М. Сеченова в 1980 г. В 1984 закончил аспирантуру при НИИ нормальной физиологии им. П. К. Анохина РАМН, защитив кандидатскую диссертацию «Роль холецистокинина в механизмах пищевого насыщения». В 1992 состоялась защита докторской диссертации на тему «Ранние гены в механизмах обучения и памяти». Получил звание профессора. В 2002 был избран членом-корреспондентом РАМН по специальности «нейробиология», а в 2008 — членом-корреспондентом РАН по специальности «нанобиотехнология».
Когнитом: гиперсетевая модель мозга. Несмотря на впечатляющие успехи нейронауки, природа высших функций мозга все еще ускользает от нашего
удовлетворительного понимания. Эта ситуация, известная как «провал в объяснении» (explanatory gap), требует поиска новых объяснительных моделей и принципов. В настоящем докладе будет предложена модель организации мозга как когнитивной гиперсети – К-сети. Вершинами К-сети (когнитома), являются КОГи (когнитивные группы), представляющие собой подмножества вершин нижележащей N-сети (коннектома), объединенные единым когнитивным опытом. Ребра между k-вершинами в когнитоме формируются как совокупности ребер между образующими их подмножествами n-вершин в коннектоме.
В понятиях алгебраической топологии КОГ представляет собой реляционный симплекс или гиперсимплекс, основанием которого служит симплекс из вершин опорной N-сети, одновременно выступающий вершиной с новым качеством в К-сети более высокого
уровня. Формализм гиперсетей обобщает сети и гиперграфы, давая аппарат, необходимый для отображения феноменов эмерджентности в многоуровневых системах, и позволяя моделировать гораздо более сложные структуры, чем сети и гиперграфы. В докладе будут рассмотрены некоторые нетривиальные следствия двусторонних отношений N- и K-сетей, лежащие в основе гиперсетевой теории мозга (ГCТМ).
https://youtu.be/LZmw7EQNKSQм
http://neuroinfo.ru/index.php/ru/mcont

Previous research showed that employing results from meta-analyses of relevant previous fMRI studies can improve the performance of voxelwise Bayesian second-level fMRI analysis. In this process, prior distributions for Bayesian analysis can be determined by information acquired from the meta-analyses. However, only image-based meta-analysis, which is not widely accessible to fMRI researchers due to the lack of shared statistical images, was tested in the previous study, so the applicability of the prior determination method proposed by the previous study might be limited. In the present study, whether determining prior distributions based on coordinate-based meta-analysis, which is widely accessible to researchers, can also improve the performance of Bayesian analysis, was examined. Three different types of coordinate-based meta-analyses, BrainMap and Ginger ALE, and NeuroQuery, were tested as information sources for prior determination. Five different datasets addressing three task conditions, i.e., working memory, speech, and face processing, were analyzed via Bayesian analysis with a meta-analysis informed prior distribution, Bayesian analysis with a default Cauchy prior adjusted for multiple comparisons, and frequentist analysis with familywise error correction. The findings from the aforementioned analyses suggest that use of coordinate-based meta-analysis also significantly enhanced performance of Bayesian analysis as did image-based meta-analysis.

The adult brain is not as hard-wired as traditionally thought. By modifying their small- or large-scale morphology, neurons can make new synaptic connections or break existing ones (structural plasticity). Structural changes accompany memory formation and learning, and are induced by neurogenesis, neurodegeneration and brain injury such as stroke. Exploring the role of structural plasticity in the brain can be greatly assisted by mathematical and computational models, as they enable us to bridge the gap between system-level dynamics and lower level cellular and molecular processes. However, most traditional neural network models have fixed neuronal morphologies and a static connectivity pattern, with plasticity merely arising from changes in the strength of existing synapses (synaptic plasticity). In The Rewiring Brain, the editors bring together, for the first time, contemporary modeling studies that investigate the implications of structural plasticity for brain function and pathology. Starting with an experimental background on structural plasticity in the adult brain, the book covers computational studies on homeostatic structural plasticity, the impact of structural plasticity on cognition and cortical connectivity, the interaction between synaptic and structural plasticity, neurogenesis-related structural plasticity, and structural plasticity in neurological disorders. Structural plasticity adds a whole new dimension to brain plasticity, and The Rewiring Brain shows how computational approaches may help to gain a better understanding of the full adaptive potential of the adult brain. The book is written for both computational and experimental neuroscientists. Key features: • Reviews the current state of knowledge of structural plasticity in the adult brain. • Gives a comprehensive overview of computational studies on structural plasticity. • Provides insights into the potential driving forces of structural plasticity and the functional implications of structural plasticity for learning and memory. • Serves as inspiration for developing novel treatment strategies for stimulating functional repair after brain damage. For a preview and to order the book, go to: https://www.elsevier.com/books/the-rewiring-brain/van-ooyen/978-0-12-803784-3

In this study, the aim is to determine the proficiency of special education teachers in terms<br> of using instructional technologies in the TRNC. A total of 80 special education teachers from<br> rehabilitation institutes in Famagusta, Kyrenia, and Nicosia participated in this study in the 2016 –<br> 2017 education year, of whom 45 were women and 45 were men. The quantitative research method<br> was used and, in terms of collection tools, a demographic information form along with an opinion<br> survey aimed at defining their usage and skill levels of special education teachers in the TRNC<br> related to the use of instructional technologies were used. The data were analysed with the SPSS<br> 20.00 program. Based on the results of the analysis, it was found that the level of using educational<br> technologies was low. According to the data which was obtained from teachers' opinions, the<br> special education teachers were in ...

Humans and monkeys easily recognize objects in scenes. This ability is known to be supported by a network of hierarchically interconnected brain areas. However, understanding neurons in higher levels of this hierarchy has long remained a major challenge in visual systems neuroscience. We use computational techniques to identify a neural network model that matches human performance on challenging object categorization tasks. Although not explicitly constrained to match neural data, this model turns out to be highly predictive of neural responses in both the V4 and inferior temporal cortex, the top two layers of the ventral visual hierarchy. In addition to yielding greatly improved models of visual cortex, these results suggest that a process of biological performance optimization directly shaped neural mechanisms.

This presentation gives a broad introduction to the exciting concept of predictive processing as the unifying theory of the brain and mind, in particular the Bayesian brain, predictive coding and free energy principle. I show how these theories aim to explain cognitive phenomena like perception, learning, belief formation, attention, motor control, the self or schizophrenia. Finally, I attempt to compare these new ideas with behaviorism, cognitivism, embodied and enactive cognition.

This paper is aiming to investigate the physical substrates of conscious process. It will argue: How does the conscious process establish relations between their external stimuli and internal stimuli in order to create reality? Within this framework, the paper will also attempt to locate the origins of consciousness in the brain.

RESUMEN
El presente artículo se propone dar cuenta del elusivo fenómeno de la conciencia desde la original perspectiva de la neurofenomenología de Francisco Varela, quien a partir de nociones tales como neuroplasticidad, enacción y emergencia, explica, cómo ocurren los procesos cerebrales que fundan la conciencia y la “unidad” de la vivencia. Describiendo como la conciencia aparece en el vivir encarnado: en la regulación con el cuerpo entero; en sus relaciones sensorio-motoras con el mundo y en una red ínter-subjetiva de acciones y de lenguaje.
Dr. Adolfo Vásquez Rocca
Keywords:  Neurociencias, ciencias cognitivas, ética, yo, cuerpo, neuroplasticidad, enacción, acciones corporeizadas,  biología cultural,  saber-cómo.
Neuroscience, cognitive science, ethics, self, body, neuroplasticity, enaction, actions embodied experiences, cultural biology,  know-how.
-Francisco Varela: neurophenomenology, enactive approach to cognition, minds without me and the elusive phenomenon of consciousness.
DR. ADOLFO VÁSQUEZ ROCCA

The timing of events can influence spatial segmentation. Neighbouring regions flickering asynchronously at a high temporal frequency appear identical, but the visual system signals a 'phantom contour' between them. Using psychophysical procedures, we determined the spatial limitations of this form of temporal segmentation across the visual field. These limitations correspond closely to receptive field diameters of neurons in primary visual cortex (V1), and are also consistent with the spatial limitations of motion detection. Here, we propose a computational model in which the neural signal for phantom contours originates from individual neurons with receptive fields that are space-time separable, like those mapped in cat and macaque V1. Similar units are also employed in the first stages of models for the perception of local motion. The correspondence between theoretical models of temporal segmentation and motion perception in the human visual system, as well as psychophysical evidence that they have similar spatial limitations, support the notion that these processes are mediated by a common neural substrate situated in early in visual cortex.

This thesis presents a novel hierarchical learning framework, Reinforcement Learning Optimal Control, for controlling nonlinear dynamical systems with continuous states and actions. The adapted approach mimics the neural computations that allow our brain to bridge across the divide between symbolic action-selection and low-level actuation control by operating at two levels of abstraction. First, current findings demonstrate that at the level of limb coordination human behaviour is explained by linear optimal feedback control theory, where cost functions match energy and timing constraints of tasks. Second, humans learn cognitive tasks involving learning symbolic level action selection, in terms of both model-free and model-based reinforcement learning algorithms. We postulate that the ease with which humans learn complex nonlinear tasks arises from combining these two levels of abstraction. The Reinforcement Learning Optimal Control framework learns the local task dynamics from naive experience using an expectation maximization algorithm for estimation of linear dynamical systems and forms locally optimal Linear Quadratic Regulators, producing continuous low-level control. A high-level reinforcement learning agent uses these available controllers as actions and learns how to combine them in state space, while maximizing a long term reward. The optimal control costs form training signals for high-level symbolic learner. The algorithm demonstrates that a small number of locally optimal linear controllers can be combined in a smart way to solve global nonlinear control problems and forms a proof-of-principle to how the brain may bridge the divide between low-level continuous control and high-level symbolic action selection. It competes in terms of computational cost and solution quality with state-of-the-art control, which is illustrated with solutions to benchmark problems.

This paper deals with the question: which notion of computation (if any) is essential for explaining cognition? Five answers are discussed in the paper.
1. The classicist answer: symbolic (digital) computation is required for explaining cognition.
2. The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition.
3. The connectionist answer: sub-symbolic computation is required for explaining cognition.
4. The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition.
5. The extreme dynamicist answer: computation is not required for explaining cognition.
The first four answers are only accurate to a first approximation. But the “devil” is in the details. The last answer cashes in on the parenthetical “if any” in the question above. The classicist argues that cognition is symbolic computation. But digital computationalism need not be equated with classicism. Indeed, computationalism can, in principle, range from digital (and analogue) computationalism through (the weaker thesis of) generic computationalism to (the even weaker thesis of) digital (or analogue) pancomputationalism. Connectionism, which has traditionally been criticised by classicists for being non-computational, can be plausibly construed as being either analogue or digital computationalism (depending on the type of connectionist networks used). Computational neuroscience invokes the notion of neural computation that may (possibly) be interpreted as a sui generis type of computation. The extreme dynamicist argues that the time has come for a post-computational cognitive science. This paper is an attempt to shed some light on this debate by examining various conceptions and misconceptions of (particularly digital) computation.

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary rea‑ sons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compel‑ ling way. Next, I contrast two computational models of working memory to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evi‑ denced by the way in which models are empirically validated. Moreover, the meth‑ odological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deserv‑ edly successful in cognitive (neuro)science. Its successes are related to deep concep‑ tual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year.

The psychologist J. J. Gibson, and later the enactivists, espoused a view of perception that emphasized active sensory exploration, and the biological functions perception serves.  They tended to neglect the internal complexity of perceptual systems.  Neuroscientists and computer vision researchers, on the other hand, focus on the complex structure and inner workings of perceptual systems, to the neglect of biological and behavioral context.  Here I will formulate a version of ecological realism which reconciles and critiques these seemingly disparate approaches.

I argue that the objects of perception are relational invariant structures preserved within the changing flux of perceptual input.  The function of perception is to enable appropriate behavior with respect to affordances, which are objective three-way relations between worldly features, animal abilities, and animal needs.  The invariant relationships perceived tend to be those which signify affordance relationships for the species and individual in question.

The perception-action cycle is but one example of the circular dynamics of perceptual systems.  The neural portions of such systems are also in a state of constant feed-forward and feedback dynamical interaction with one another.  These dynamics confer an active autonomy on perceptual systems as manifested by phenomena like dreams, hallucinations, and perceptual illusions.  Metaphorically, such systems may function to constantly formulate and test hypotheses about affordances based on perceptual evidence and prior categorical experience.  Hierarchical predictive models of perception, in which perceptual systems consist of a hierarchy of Bayesian statistical predictors, represent a possible means by which this metaphor might be crafted into a testable scientific hypothesis.  Perception, even if it involves actively autonomous perceptual systems coping with ambiguous input, is epistemically reliable most of the time, because it is constantly tested by action.  Perceptual states are true or valid if they bear an appropriate relationship to objective affordance relationships, and false or invalid if they do not.  These views require a reformulation of the venerable distinction between ‘direct’ and ‘indirect’ perception.  Perception is ontologically direct in the sense of dealing in objective relationships in the world, but justificationally indirect in the sense of requiring an argument that perceptual beliefs are generally epistemically reliable.

In many philosophical discussions, it is assumed that the computational explanation of the mind implies that it is being explained as a Universal Turing Machine (UTM). The reason why it is being proposed as a model of the mind is that it is the standard model of computation, and that is a universal machine – i.e., any other digital computer might be simulated by the UTM. So if the mind is a computer that is not able to compute everything that the UTM can, the UTM might still simulate it.�In recent years, several criticisms to such a proposal have been raised. First, it was argued that the UTM requires an infinite tape, which is something that is impossible physically (or impossible as a physical part of the brain). Second, it was argued that the UTM is not a good candidate for explaining the intricacies of the human mind as it has a completely different architecture: so, while the set of functions computed by the machine could be the same as the one computed by the mind, it would differ dramatically in terms of speed and space requirements. Third, some have claimed that there are physical systems capable of hyper-Turing computation, so the UTM might not be the strongest model of computation available. I focus on the epistemic question: how could one tell that a physical system is a UTM? I will distinguish two senses in which one could say that a physical system “is” a UTM: (1) when a physical system has a function that could be simulated with a UTM (functional sense); (2) when a physical system is a mechanism best described as a UTM (mechanistic sense). I will enumerate conditions that must be fulfilled to qualify a physical system to be a UTM in both senses and show differences between the two. It will turn out that in the functional sense, many physical systems might be UTMs, whereas in the mechanistic sense, the set will be much restricted. I will also discuss the problem of finiteness: it seems that for many algorithms, the tape length is restricted anyway (especially if the restriction of the input value range is the part of the algorithm), so the finiteness might not be the biggest problem. Moreover, in the mechanistic sense, one does not select the model of computation simply by picking the strongest model possible, so the hyper-Turing models must also match the mental mechanisms as strictly as any other descriptions of mechanisms that we posit in science.�The mind does not seem to be a UTM in the mechanistic sense at all because of its architecture; and proper computational explanations in cognitive science require that the architecture is matched strictly. I will discuss the problem of the required level of detail in mechanistic computational explanations: it transpires that the philosophically popular UTM is not a good candidate for a scientific model of the mind, even if we accept the standard computational theory of mind.