Sleep-like slow oscillations improve visual classification through synaptic homeostasis and memory association in a thalamo-cortical model

The occurrence of sleep passed through the evolutionary sieve and is widespread in animal species. Sleep is known to be beneficial to cognitive and mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the importance of the phenomenon, a complete understanding of its functions and underlying mechanisms is still lacking. In this paper, we show interesting effects of deep-sleep-like slow oscillation activity on a simplified thalamo-cortical model which is trained to encode, retrieve and classify images of handwritten digits. During slow oscillations, spike-timing-dependent-plasticity (STDP) produces a differential homeostatic process. It is characterized by both a specific unsupervised enhancement of connections among groups of neurons associated to instances of the same class (digit) and a simultaneous down-regulation of stronger synapses created by the training. This hierarchical organization of post-sleep internal representations favours higher performances in retrieval and classification tasks. The mechanism is based on the interaction between top-down cortico-thalamic predictions and bottom-up thalamo-cortical projections during deep-sleep-like slow oscillations. Indeed, when learned patterns are replayed during sleep, cortico-thalamo-cortical connections favour the activation of other neurons coding for similar thalamic inputs, promoting their association. Such mechanism hints at possible applications to artificial learning systems.Comment: 11 pages, 5 figures, v5 is the final version published on Scientific Reports journa

A modular theoretical framework for learning through structural plasticity

It is known that, during learning, modifications in synaptic transmission and, eventually, structural changes of the connectivity take place in our brain. This can be achieved through a mechanism known as structural plasticity. In this work, starting from a simple phenomenological model, we exploit a mean-field approach to develop a modular theoretical framework of learning through this kind of plasticity, capable of taking into account several features of the connectivity and pattern of activity of biological neural networks, including probability distributions of neuron firing rates, selectivity of the responses of single neurons to multiple stimuli, probabilistic connection rules and noisy stimuli. More importantly, it describes the effects of consolidation, pruning and reorganization of synaptic connections. This framework will be used to compute the values of some relevant quantities used to characterize the learning and memory capabilities of the neuronal network in a training and validation procedure as the number of training patterns and other model parameters vary. The results will then be compared with those obtained through simulations with firing-rate-based neuronal network models

New Techniques in Diagnostic X-ray Imaging: A Simulation Tool and Experimental Findings

Abstract Absorption X-ray imaging is a well-established technique. However it is still a challenging task in its search for a compromise between the need for high spatial resolution and high contrast and the request to keep the dose delivered to the patient within acceptable values. New imaging techniques are under investigation, like the use of new X-ray sources, phase contrast imaging or K-edge imaging. Monte Carlo or analytic simulations are often the best way to test and predict the effectiveness of these techniques. A new simulation tool for X-ray imaging will be presented together with some applications to the characterization of new X-ray sources, in-line phase contrast effect and angiographic K-edge imaging. Simulation results will be compared also with experimental dat

Monte Carlo’s Core and Tests for Application Developers: Geant4 and XRMC Comparison and Validation

In this chapter, the Monte Carlo (MC) core is presented, particularly its cross-sectional libraries and random generators. The main idea is to introduce validation and reliability of MC applications and to explore its limitations. As an example, a comparison between two MC toolkits, namely XRMC (version 6.5.0–2) and Geant4 (version 10.02.p02), and a validation between each of them and experimental data applied to mammography (external dosimetry) are presented. The simulated quantities compared are exposure, kerma, half-value layer, and backscattering. Limitations, advantages, and disadvantages of using a general and specific MC toolkit are commented too

Editorial: Language Representation and Learning in Cognitive and Artificial Intelligence Systems

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Conceptual development from the perspective of a brain-inspired robotic architecture

Concepts are central to reasoning and intelligent behaviour. Scientific evidence shows that conceptual development is fundamental for the emergence of high-cognitive phenomena. Here, we model such phenomena in a brain-inspired cognitive robotic model and examine how the robot can learn, categorise, and abstract concepts to voluntary control behaviour. The paper argues that such competence arises with sufficient conceptual content from physical and social experience. Hence, senses, motor abilities and language, all contribute to a robot's intelligent behaviour. To this aim, we devised a method for attaining concepts, which computationally reproduces the steps of the inductive thinking strategy of the Concept Attainment Model (CAM). Initially, the robot is tutor-guided through socio-centric cues to attain concepts and is then tested consistently to use these concepts to solve complex tasks. We demonstrate how the robot uses language to create new categories by abstraction in response to human language-directed instructions. Linguistic stimuli also change the representations of the robot's experiences and generate more complex representations for further concepts. Most notably, this work shows that this competence emerges by the robot's ability to understand the concepts similarly to human understanding. Such understanding was also maintained when concepts were expressed in multilingual lexicalisations showing that labels represent concepts that allowed the model to adapt to unfamiliar contingencies in which it did not have directly related experiences. The work concludes that language is an essential component of conceptual development, which scaffolds the cognitive continuum of a robot from low-to-high cognitive skills, including its skill to understand

A New automatic system of cell colony counting

The counting process of cell colonies is always a long and laborious process that is dependent on the judgment and ability of the operator. The judgment of the operator in counting can vary in relation to fatigue. Moreover, since this activity is time consuming it can limit the usable number of dishes for each experiment. For these purposes, it is necessary that an automatic system of cell colony counting is used. This article introduces a new automatic system of counting based on the elaboration of the digital images of cellular colonies grown on petri dishes. This system is mainly based on the algorithms of region-growing for the recognition of the regions of interest (ROI) in the image and a Sanger neural net for the characterization of such regions. The better final classification is supplied from a Feed-Forward Neural Net (FF-NN) and confronted with the K-Nearest Neighbour (K-NN) and a Linear Discriminative Function (LDF). The preliminary results are shown

Molecular simulations of SSTR2 dynamics and interaction with ligands

The cyclic peptide hormone somatostatin regulates physiological processes involved in growth and metabolism, through its binding to G-protein coupled somatostatin receptors. The isoform 2 (SSTR2) is of particular relevance for the therapy of neuroendocrine tumours for which different analogues to somatostatin are currently in clinical use. We present an extensive and systematic computational study on the dynamics of SSTR2 in three different states: active agonist-bound, inactive antagonist-bound and apo inactive. We exploited the recent burst of SSTR2 experimental structures to perform μs-long multi-copy molecular dynamics simulations to sample conformational changes of the receptor and rationalize its binding to different ligands (the agonists somatostatin and octreotide, and the antagonist CYN154806). Our findings suggest that the apo form is more flexible compared to the holo ones, and confirm that the extracellular loop 2 closes upon the agonist octreotide but not upon the antagonist CYN154806. Based on interaction fingerprint analyses and free energy calculations, we found that all peptides similarly interact with residues buried into the binding pocket. Conversely, specific patterns of interactions are found with residues located in the external portion of the pocket, at the basis of the extracellular loops, particularly distinguishing the agonists from the antagonist. This study will help in the design of new somatostatin-based compounds for theranostics of neuroendocrine tumours

Heterogeneity of Antiphospholipid Syndrome (APS) as Characterized by Brain Perfusion Techniques. Towards New Ways of Syndrome Characterization

Antiphospholipid Syndrome (APS) syndrome is an autoimmune condition that affects the way that blood cells in humans bind together. Though the cause of APS is unclear, researchers believe that many factors have an impact on developing this pathological condition. Phospholipids (PLs) play numerous central roles in biological systems, and processes of biological systems regulation act through the liberation of a vast amount of different signalling molecules, which are also involved in the modulation of cell proliferation, inflammation, oxidative stress, neurotransmission and many other processes.A global landmark, holistic, is required to evaluate different phenotypes in APS. All thecriteria validated for the APS diagnosislead to an extremely heterogeneous landmark of the pathology and related to several manifestations in different systems. Heterogeneity also characterizes the SPECT acquisition of the patients and it is connected to several neurological and underestimates symptoms of the pathology. We present some examples to highlight the connection between heterogeneity in SPECT and APS markers of pathology indicating the needs to approach to the Syndrome in a holistic way. At the end of the paper we suggested the multidisciplinary approach that we are tuning for our analysis, approach based on Imaging, Metabolomic and Clinical evaluation, including mental health test for a neuropsychiatric characterization of the pathology

Phase-contrast breast CT: the effect of propagation distance

X-ray phase imaging has the potential to dramatically improve soft tissue contrast sensitivity, which is a crucial requirement in many diagnostic applications such as breast imaging. In this context, a program devoted to perform in-vivo phase-contrast synchrotron radiation breast computed tomography is ongoing at the Elettra facility (Trieste, Italy). The used phase-contrast technique is the propagation-based configuration, which requires a spatially coherent source and a sufficient object-to-detector distance. In this work the effect of this distance on image quality is quantitatively investigated scanning a large breast surgical specimen at 3 object-to-detector distances (1.6, 3, 9 m) and comparing the images both before and after applying the phase-retrieval procedure. The sample is imaged at 30 keV with a 60 \ub5m pixel pitch CdTe single-photon-counting detector, positioned at a fixed distance of 31.6~m from the source. The detector fluence is kept constant for all acquisitions. The study shows that, at the largest distance, a 20-fold SNR increase can be obtained by applying the phase-retrieval procedure. Moreover, it is shown that, for phase-retrieved images, changing the object-to-detector distance does not affect spatial resolution while boosting SNR (4-fold increase going from the shortest to the largest distance). The experimental results are supported by a theoretical model proposed by other authors, whose salient results are presented in this paper