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ARTICLE Communicated by Terrence Sejnowski Computing with a Canonical Neural Circuits Model with Pool Normalization and Modulating Feedback
"... Evidence suggests that the brain uses an operational set of canonical com-putations like normalization, input filtering, and response gain enhance-ment via reentrant feedback. Here, we propose a three-stage columnar architecture of cascaded model neurons to describe a core circuit com-bining signal ..."
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Evidence suggests that the brain uses an operational set of canonical com-putations like normalization, input filtering, and response gain enhance-ment via reentrant feedback. Here, we propose a three-stage columnar architecture of cascaded model neurons to describe a core circuit com-bining signal pathways of feedforward and feedback processing and the inhibitory pooling of neurons to normalize the activity.We present an an-alytical investigation of such a circuit by first reducing its detail through the lumping of initial feedforward response filtering and reentrant mod-ulating signal amplification. The resulting excitatory-inhibitory pair of neurons is analyzed in a 2D phase-space. The inhibitory pool activa-tion is treated as a separate mechanism exhibiting different effects. We analyze subtractive as well as divisive (shunting) interaction to imple-ment center-surround mechanisms that include normalization effects in the characteristics of real neurons. Different variants of a core model architecture are derived and analyzed—in particular, individual excita-
Formal Aspects of Computing
"... Abstract. Two important issues in computational modelling in cognitive neuroscience are: first, how to formally describe neuronal networks (i.e. biologically plausible models of the central nervous system), and second, how to analyse complexmodels, in particular, their dynamics and capacity to learn ..."
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Abstract. Two important issues in computational modelling in cognitive neuroscience are: first, how to formally describe neuronal networks (i.e. biologically plausible models of the central nervous system), and second, how to analyse complexmodels, in particular, their dynamics and capacity to learn.Wemake progress towards these goals by presenting a communicating automata perspective on neuronal networks. Specifically, we describe neuronal networks and their biological mechanisms using Data-rich Communicating Automata, which extend classic automata theory with rich data types and communication. We use two case studies to illustrate our approach. In the first case study, wemodel a number of learning frameworks, which vary in respect of their biological detail, for instance the Backpropagation (BP) and the Generalized Recirculation (GeneRec) learning algorithms. We then used the SPIN model checker to investigate a number of behavioral properties of the neural learning algorithms. SPIN is a well-known model checker for reactive distributed systems, which has been successfully applied to many non-trivial problems. The verification results show that the biologically plausible GeneRec learning is less stable than BP learning. In the second case study, we presented a large scale (cognitive-level) neuronal network, which models an attentional spotlight mechanism in the visual system. A set of properties of this model was verified using Uppaal, a popular real-time model checker. The results show that the asynchronous processing supported by concurrency theory is not only a more biologically plausible way to model neural systems, but also
Short Title: Learning grid cells and place cells
"... † Authorship in alphabetical order ..."
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ANOTHER ADAPTIVE APPROACH TO NOVELTY DETECTION IN TIME SERIES
"... This paper introduces a novel approach to novelty detection of every individual sample of data in a time series. The novelty detection is based on the knowledge learned by neural networks and the consistency of data with contemporary governing law. In particular, the relationship of prediction error ..."
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This paper introduces a novel approach to novelty detection of every individual sample of data in a time series. The novelty detection is based on the knowledge learned by neural networks and the consistency of data with contemporary governing law. In particular, the relationship of prediction error with the adaptive weight increments by gradient decent is shown, as the modification of the recently introduced adaptive approach of novelty detection. Static and dynamic neural network models are shown on theoretical data as well as on a real ECG signal.
ANNALS SPECIAL EDITION: THERAPEUTIC PROSPECTS New Innovations: Therapeutic Opportunities for Intellectual Disabilities
"... Intellectual disability is common and is associated with significant morbidity. Until the latter half of the 20th century, there were no efficacious treatments. Following initial breakthroughs associated with newborn screening and meta-bolic corrections, little progress was made until recently. With ..."
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Intellectual disability is common and is associated with significant morbidity. Until the latter half of the 20th century, there were no efficacious treatments. Following initial breakthroughs associated with newborn screening and meta-bolic corrections, little progress was made until recently. With improved understanding of genetic and cellular mech-anisms, novel treatment options are beginning to appear for a number of specific conditions. Fragile X and tuberous sclerosis offer paradigms for the development of targeted therapeutics, but advances in understanding of other dis-orders such as Down syndrome and Rett syndrome, for example, are also resulting in promising treatment directions. In addition, better understanding of the underlying neurobiology is leading to novel developments in enzyme replacement for storage disorders and adjunctive therapies for metabolic disorders, as well as potentially more gen-eralizable approaches that target dysfunctional cell regulation via RNA and chromatin. Physiologic therapies, includ-ing deep brain stimulation and transcranial magnetic stimulation, offer yet another direction to enhance cognitive functioning. Current options and evolving opportunities for the intellectually disabled are reviewed and exemplified.
A Unified Quantitative Model of Vision and Audition
"... Abstract: We have put forwards a unified quantitative framework of vision and audition, based on existing data and theories. According to this model, the retina is a feedforward network self-adaptive to inputs in a specific period. After fully grown, cells become specialized detectors based on stati ..."
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Abstract: We have put forwards a unified quantitative framework of vision and audition, based on existing data and theories. According to this model, the retina is a feedforward network self-adaptive to inputs in a specific period. After fully grown, cells become specialized detectors based on statistics of stimulus history. This model has provided explanations for perception mechanisms of colour, shape, depth and motion. Moreover, based on this ground we have put forwards a bold conjecture that single ear can detect sound’s direction. This is complementary to existing theories and has provided better explanations for sound localization. Great strides have been made in vision and audition research 1-6. Nevertheless, there remain substantial gaps between physiological evidences and existing models 6-11. We wish to put forwards a unified quantitative model on the cell scale. It has provided explanations for possible biological perception mechanisms of shape 8, colour
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"... Abstract: The coding mechanism of sensory memory on the neuron scale is one of the most important questions in neuroscience. We have put forward a quantitative neural network model, which is self-organized, self-similar, and self-adaptive, just like an ecosystem following Darwin's theory. Accor ..."
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Abstract: The coding mechanism of sensory memory on the neuron scale is one of the most important questions in neuroscience. We have put forward a quantitative neural network model, which is self-organized, self-similar, and self-adaptive, just like an ecosystem following Darwin's theory. According to this model, neural coding is a “mult-to-one ” mapping from objects to neurons. And the whole cerebrum is a real-time statistical Turing Machine, with powerful representing and learning ability. This model can reconcile some important disputations, such as: temporal coding versus rate-based coding, grandmother cell versus population coding, and decay theory versus interference theory. And it has also provided explanations for some key questions such as memory consolidation, episodic memory, consciousness, and sentiment. Philosophical significance is indicated at last. Main Text: Great strides have been made in neuroscience and cognitive science (1-8). Until now however, the coding mechanism of sensory memory on the neuron scale is still unclear. A gap exists between the molecular and whole brain research (9). We wish to bridge this gap through a quantitative coding model. Inspired by the “self-organization ” idea (10, 11), we only make local rules about neuron and synapse based on existing data and theories. Then the hierarchical neural
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"... Abstract—Conspiracy theories, or in general seriously dis-torted beliefs, are widespread. How and why are they formed in the brain is still more a matter of speculation rather than sci-ence. In this paper one plausible mechanisms is investigated: rapid freezing of high neuroplasticity (RFHN). Emotio ..."
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Abstract—Conspiracy theories, or in general seriously dis-torted beliefs, are widespread. How and why are they formed in the brain is still more a matter of speculation rather than sci-ence. In this paper one plausible mechanisms is investigated: rapid freezing of high neuroplasticity (RFHN). Emotional arousal increases neuroplasticity and leads to creation of new pathways spreading neural activation. Using the language of neurodynamics a meme is defined as quasi-stable associative memory attractor state. Depending on the temporal character-istics of the incoming information and the plasticity of the net-work, memory may self-organize creating memes with large attractor basins linking many unrelated input patterns. Memes with fake rich associations distort relations between memory states. Simulations of various neural network models trained with competitive Hebbian learning (CHL) on stationary and non-stationary data lead to the same conclusion: short learning with high plasticity followed by rapid decrease of plasticity leads to memes with large attraction basins distorting input pattern representations in associative memory. Such system-level mod-els may be used to understand creation of distorted beliefs and formation of conspiracy memes, understood as strong attractor states of the neurodynamics. I.
