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Patients affected by attention-deficit/hyperactivity disorder (ADHD) are characterized by impaired executive functioning and/or attentional deficits. Our study is aimed to determine whether the outcomes measured by the attentional network task (ANT), i.e., the reaction times (RT) to specific target and cueing conditions and alerting, orienting, and conflict effects, are affected by cognitive training with a dual N-Back task. We considered three groups of young adult participants: ADHD patients without medication, ADHD with medication (MADHD), and age/education-matched controls (CTL). Working memory training began the day after the pretest. Participants were asked to perform 20 trainings composed of 20 blocks during an entire month. They were told that they would have to practice the dual N-Back task for about 30 min per day during the week and to rest for two days in the weekend. Each experimental group was randomly assigned into two conditions, the first with a progressive level (PL) of difficulty training, while the second was blocked at the level 1 during the whole training phase (baseline training). We observed that PL training was beneficial with reduced RTs in all groups and reduced conflict effects. MADHD showed a positive effect already with baseline training, whereas ADHD showed no significant reduction of neither RTs nor conflict effect after baseline training. No group showed any effect of training on alerting and orienting effects.

Attention Deficit/Hyperactivity Disorder (ADHD) is a behavioral disorder of childhood and adolescence characterized by symptoms that include impulsiveness, inattention, hyperactivity, impaired decision making, and primary deficits of executive functions. In a vast proportion of the diagnosed adolescents, the clinical symptoms may persist into adulthood and ADHD patients are characterized by Working Memory (WM) impairment. In the present study, we analyze brain dynamics by EEG recordings during the dual n-back task in a population of young adults with ADHD and healthy controls. The WM capacity and attention span are tested by n-back task, and divided attention is tested by running the task in the visual and auditory modalities concurrently. We analyzed the event-related potentials (ERPs) triggered by the onset of the audio-visual stimuli. In ADHD the amplitude of N200 wave component was only slightly reduced and the peak latency was unaffected. The amplitude of P300 peak was reduced in ADHD with respect to controls at all sites along the midline. The latency of P300 peak in ADHD was reduced at Fz and Cz. In particular, at Fz the latency of ADHD was reduced after a response that required matching the visual cue 1 or 2 trials back in time. These results support the hypothesis that the P300 component, associated with a cognitive workload, peaked earlier in the ADHD than in controls and it may be used to follow the outcome of cognitive training.

We characterize the attractor-based expressive power of several models of recurrent neural networks.The deterministic rational-weighted networks are Muller Turing equivalent.The deterministic real-weighted and evolving networks recognize the class of B C ( ź 2 0 ) neural ω languages.The nondeterministic rational and real networks recognize the class of Σ 1 1 neural ω-languages. We provide a characterization of the expressive powers of several models of deterministic and nondeterministic first-order recurrent neural networks according to their attractor dynamics. The expressive power of neural nets is expressed as the topological complexity of their underlying neural ω-languages, and refers to the ability of the networks to perform more or less complicated classification tasks via the manifestation of specific attractor dynamics. In this context, we prove that most neural models under consideration are strictly more powerful than Muller Turing machines. These results provide new insights into the computational capabilities of recurrent neural networks.

We consider a simplified Boolean model of the basal ganglia-thalamocortical network, and study the effect of a spike-timing-dependent plasticity (STDP) rule on the stabilization of its attractor dynamics. More precisely, we introduce an adaptive STDP rule which constantly updates its learning rate based on the attractors that the network encounters during a window of past time steps. This so-called network memory is assumed to be dynamic: its duration is step-wise increased every time a trigger input pattern is detected, and is decreased otherwise. In this context, we show that well-adjusted trigger inputs can fine tune the network memory and its associated STDP rule in such a way to drive the network into stable and rich attractor dynamics. We discuss how this feature might be related to reward learning processes in the neurobiological context.

Background Traumatic fractures of the thoracolumbar spine are common injuries, accounting for approximately 90% of all spinal traumas. Optimal management of these fractures still gives rises to much debate in the literature. Currently, one of the treatment options in young patients with stable traumatic vertebral fractures is conservative treatment using braces. Kyphoplasty as a minimally invasive procedure has been shown to be effective in stabilizing vertebral body fractures, resulting in immediate pain relief and improved physical function with early return to work activity. The aim of the study is to report VAS, ODI scores, and kyphosis correction following treatment. Methods This is a retrospective study to investigate the clinical and radiological results 10 years after percutaneous balloon kyphoplasty followed by cement augmentation with polymethylmethacrylate (PMMA) or calcium phosphate cements (CPC), according to age, in 85 consecutive patients affected by 91 AOSpine type A traumatic fractures of the thoracolumbar spine (A1, A2, and A3). Clinical follow-up was performed with the Visual Analogic Scale (VAS) at the preoperative visit and in the postoperative follow-up after 1 week, 1, 6, 12 months, and each year up to 10 years. Additionally, the Oswestry Disability Index (ODI) improvement was calculated as the difference between the ODI scores at the preoperative visit and at final follow-up. Finally, the Cobb angle from this cohort was assessed before surgery, immediately postoperatively, and at the end of follow-up. Results Kyphoplasty markedly improved pain and resulted in statistically significant vertebral height restoration and normalization of morphologic shape indexes that remained stable for at least 10 years following treatment. Conclusions The present study showed that kyphoplasty and cement augmentation are an effective method of treatment for selected type A fractures.

Background: The variety of symptoms and radiological findings in patients with Chiari malformation type I makes both the indication for surgery and the technical modality controversial. We report our 5-year experience, describing our technique and critically evaluating the clinical results. Methods: Between 2012 and 2016, 25 patients (15 female and 10 male; mean age 39.2 years) underwent posterior fossa decompression for Chiari malformation type I. Their clinical complaints included headache, nuchalgia, upper limb weakness or numbness, instability, dizziness and diplopia. Syringomyelia was present in 12 patients (48%). Suboccipital craniectomy was completed in all cases with C1 laminectomy and shrinkage of the cerebellar tonsils by bipolar coagulation; duraplasty was performed with a suturable dura substitute. Results: Gratifying results were observed in our series. Symptoms and signs were resolved in 52% of patients, and 20% of patients had an improvement in their preoperative deficits. The symptoms of six patients (24%) were essentially unchanged, and one patient (4%) deteriorated despite undergoing surgery. Generally, patients with syringomyelia on magnetic resonance imaging (MRI) showed less symptomatic improvement after surgery. The syrinx disappeared in seven of 12 patients, and complications occurred in three patients (12%). Conclusion: Cerebellar tonsil reduction and restoration of cerebrospinal fluid (CSF) circulation provided clinical improvement and a stable reduction in the syrinx size in the vast majority of treated patients, with a low rate of complications.

We introduce an analysis method for electroencephalography (EEG) data, focused on Event-Related Potentials (ERPs). Our approach is unsupervised and makes use of a fuzzy clustering algorithm based on the possibilistic framework, and includes a data-driven noise and artifact rejection phase. Our contribution provides a general analysis tool, applicable to any ERP data set, which can uncover the data set's internal structure. The fuzzy clustering algorithm is the core of our method, since its fine-grained membership grades how much a sample belongs to a given cluster, making the method applicable even when groups have a certain overlap. Prior to the clustering step, we apply weights to the feature vectors, optimizing them in order to enhance the variance within the dataset, and we extract time-window interval based features inspired by interval arithmetic. We apply the data processing workflow to the analysis a set of ERPs recorded during an emotional Go/NoGo task. We evaluate the performance of the unsupervised analysis by computing a measure based on the clusterization rate of trials in different experimental conditions. The results on the studied data set show that the proposed method obtains a difference of clusterization rate of 69% in Go vs. NoGo trials, when weights and interval-features are applied to the data, improving previous work not including weights and interval-features which had a rate of 31%. Furthermore, when compared with the standard Fuzzy c-means, our proposed possibilistic clustering algorithm outperforms it in terms of clusterization rate. We also examine the effect of pre-processing the data with Independent Component Analysis and removing noise-related components, and observe that this does not improve significantly the obtained results. These findings demonstrate that our proposed method provides a valuable data processing workflow robust to EEG artifacts and able to produce a clustering that is coherent with the experimental conditions represented in the ERP dataset.

Anaerobic digestion is a natural process that transforms organic substrates to methane and other products. Under controlled conditions the process has been widely applied to manage organic wastes. Improvements in process control are expected to lead to improvements in the technical and economic efficiency of the process. This paper presents and compares 3 different neural network model architectures for use as anaerobic digestion process predictive models. The models predict the future biogas production trend from measured physical and chemical parameters. The first model features an LSTM layer, the second model features a 1-D convolutional layer and the third model combines 2 separate inputs and parallel treatment using LSTM and 1-D convolutional layers followed by merging to produce a single prediction. The predictions can be used to adaptively adjust the substrate feeding rate in accordance with the transient state of the digestion process as defined by liquid feeding rate, the organic acid and ammonium ion concentrations and the pH of the digester liquid phase. The training and testing data were obtained during 1 year of continuous operation of a pilot-plant treating restaurant wastes. PLS regression and ICA were used to select the most relevant process parameters from the data. The 1-D Convolutional based model comprising 272 trainable parameters predicted the future biogas flow rate changes with accuracy as high as 89% and an average accuracy of 58% . The work-flow can be applied to optimize the control of the study digester and to control bioreactors in general.

Maximal safe resection represents the gold standard for surgery of malignant brain tumors. As regards gross-total resection, accurate localization and precise delineation of the tumor margins are required. Intraoperative diagnostic imaging (Intra-Operative Magnetic Resonance-IOMR, Intra-Operative Computed Tomography-IOCT, Intra-Operative Ultrasound-IOUS) and dyes (fluorescence) have become relevant in brain tumor surgery, allowing for a more radical and safer tumor resection. IOUS guidance for brain tumor surgery is accurate in distinguishing tumor from normal parenchyma, and it allows a real-time intraoperative visualization. We aim to evaluate the role of IOUS in gliomas surgery and to outline specific strategies to maximize its efficacy. We performed a literature research through the Pubmed database by selecting each article which was focused on the use of IOUS in brain tumor surgery, and in particular in glioma surgery, published in the last 15 years (from 2003 to 2018). We selected 39 papers concerning the use of IOUS in brain tumor surgery, including gliomas. IOUS exerts a notable attraction due to its low cost, minimal interruption of the operational flow, and lack of radiation exposure. Our literature review shows that increasing the use of ultrasound in brain tumors allows more radical resections, thus giving rise to increases in survival.

High-grade gliomas (HGGs) are the most frequent primary malignant brain tumors in adults, which lead to death within two years of diagnosis. Maximal safe resection of malignant gliomas as the first step of multimodal therapy is an accepted goal in malignant glioma surgery. Gross total resection has an important role in improving overall survival (OS) and progression-free survival (PFS), but identification of tumor borders is particularly difficult in HGGS. For this reason, imaging adjuncts, such as 5-aminolevulinic acid (5-ALA) or fluorescein sodium (FS) have been proposed as superior strategies for better defining the limits of surgical resection for HGG. 5-aminolevulinic acid (5-ALA) is implicated as precursor in the synthetic pathway of heme group. Protoporphyrin IX (PpIX) is an intermediate compound of heme metabolism, which produces fluorescence when excited by appropriate light wavelength. Malignant glioma cells have the capacity to selectively synthesize or accumulate 5-ALA-derived porphyrins after exogenous administration of 5-ALA. Fluorescein sodium (FS), on the other hand, is a fluorescent substance that is not specific to tumor cells but actually it is a marker for compromised blood-brain barrier (BBB) areas. Its effectiveness is confirmed by multicenter phase-II trial (FLUOGLIO) but lack of randomized phase III trial data. We conducted an analytic review of the literature with the objective of identifying the usefulness of 5-ALA and FS in HGG surgery in adult patients.

Purpose We report our experience with a novel surgical device for the treatment of lumbar degenerative microinstability. Facet Wedge (DePuy Synthes, Raynham, Massachusetts, United States) is a novel technique of intra-articular lumbar facet fixation that provides a minimally invasive alternative to standard posterior fixation. Materials and Methods From November 2014 to July 2015, 38 patients underwent single-level Facet Wedge implantation. The main surgical indications included herniated disk (18 patients), spinal canal and foraminal stenosis (14 patients), and Meyerding grade I degenerative spondylolisthesis (6 patients). All the patients showed radiologic signs of microinstability: hyperintensity in both facet joints (facet fluid signal) in T2-weighted magnetic resonance imaging and a black disk as a sign of degenerative disease. No slippage was evident at dynamic radiograph. After a period of conservative treatment (minimum of 6 months), surgery was performed. All patients' follow-up lasted over at least 12 months. Results The low back visual analog scale score decreased significantly after surgery (from an average of 8.2 to 3.1 at final follow-up). Postoperatively, the Oswestry Disability Index showed a significant reduction (14.7 on average). No slippage or signs of adjacent segment degeneration was detected in neuroimaging follow-up. Conclusion Facet Wedge allows facet fixation in lumbar degenerative microinstability. To the best of our knowledge, this is the first clinical series reported in the literature on this novel device.

The attractor-based complexity of a Boolean neural network refers to its ability to discriminate among the possible input streams, by means of alternations between meaningful and spurious attractor dynamics. The higher the complexity, the greater the computational power of the network. The fine tuning of the interactivity – the network’s feedback output combined with the current input stream – can maintain a high degree of complexity within stable domains of the parameters’ space. In addition, the attractor-based complexity of the network is related to the degree of discrimination of specific input streams. We present a novel supervised attractor-based learning procedure aimed at achieving a maximal discriminability degree of a selected input stream. With a predefined target value of discriminability degree and in the absence of changes in the internal connectivity matrix of the network, the learning procedure updates solely the weights of the feedback projections. In a Boolean model of the basal ganglia-thalamocortical circuit, we show how the learning trajectories starting from different configurations can converge to final configurations associated with same high discriminability degree. We discuss the possibility that the limbic system may play the role of the interactive feedback to the network studied here.

Recently models of physiological systems including single neuron or neural network models are get- ting larger in size and more complicated in accuracy. In addition, there are a wide range of models. To enhance sharing and reusing a whole or a part of a model is an e ective way to promote the computational neuroscience. A platform for model sharing, and for building multilevel models of physiological systems have been developed and presented in this article with a use case of a neural network modeling. On the platform, a model is represented as an aggregate of modules. We applied our new platform to im- plement the neural network model, and demonstrated the platform functions, especially focusing on a feature to cre- ate large scale models.

Neuroheuristic Research Group, University of LausanneQuartier Dorigny, CH-1015 Lausanne, Switzerland, Email: avilla@neuroheuristic.orgAbstract —The concept of interdependent communica-tions systems and Wiener's assertion that a machine thatchanges its responses based on feedback is a machine thatlearns, denes the brain as a cybernetic machine. Systemstheory has traditionally focused on the structure of systemsand their models, whereas cybernetics has focused on howsystems function, how they control their actions, how theycommunicate with other systems or with their own compo-nents. However, structure and function of a system cannotbe understood in separation and cybernetics and systemstheoryshouldbeviewedastwofacetsofasingleapproach,dened as the neuroheuristic approach.1. IntroductionNorbert Wiener, a mathematician, engineer and socialphilosopher, coinedtheword”cybernetics”fromtheGreekword meaning ”steersman”. He dened it as the science ofcontrol and communication in the animal and the machine[1]. Many other denitions have followed since then, butingeneralcyberneticstakesasitsdomainthedesignordis-coveryandapplicationofprinciplesofregulationandcom-munication. Early work sought to dene and apply prin-ciples by which systems may be controlled. More recentwork has attempted to understand how systems describethemselves, control themselves, and organize themselves.The cerebral cortex is not a single entity but an impres-sive network formed by an order of tens of millions of neu-rons, most of them excitatory, and by about ten times moreglial cells. Ninety percent of the inputs received by a cor-tical area come from other areas of the cerebral cortex. Asa whole, the cerebral cortex can be viewed as a machinetalking to itself and could be seen as one big feedback sys-temsubjecttotherelentlessadvanceofentropy,whichsub-verts the exchange of messages that is essential to contin-ued existence (Wiener, 1954). This concept of interdepen-dent communications systems, also known as systems the-ory, coupled with Wiener's assertion that a machine thatchanges its responses based on feedback is a machine thatlearns, denes the cerebral cortex as a cybernetic machine.Therefore, the focus of investigation is shifted from com-munication and control to interaction. Systems theory hastraditionally focused more on the structure of systems andtheir models, whereas cybernetics has focused more onhow systems function, that is to say how they control theiractions, how they communicate with other systems or withtheir own components. However, structure and function ofa system cannot be understood in separation and cybernet-ics and systems theory should be viewed as two facets of asingle approach, dened as “the neuroheuristic approach”.2. Classical and Interactive ComputationMcCulloch and Pitts [2] proposed a modelization of thenervous system as a nite interconnection of logical de-vices. For the rst time, neural networks were consid-ered as discrete abstract machines, and the issue of theircomputational capabilities investigated from the automata-theoretic perspective. Further developments of this per-spective opened up the way to the theoretical approach toneural computation [3, 4, 5].A Turingmachine (TM)consistsofainnitetape,aheadthat can read and write on this tape, and a nite programwhich, according to the current computational state of themachine and the current symbol read by the head, deter-mines the next symbol to be written by the head on thetape, the next move of the head (left or right), and the nextcomputational state of the machine. The classical Turingparadigmofcomputationcorrespondstothecomputationalscenario where a system receives a nite input, processesthis input, and either provides a corresponding output ornever halts. According to the Church-Turing Thesis, theTuring machine model is capable of capturing all possibleaspects of algorithmic computation [6].The concept of a Turing machine with advise (TM /A)provides a model of computation beyond the Turing lim-its. It consists of a classical Turing machine provided withan additional advise function : N ! f 0;1g

The attractor-based complexity of a Boolean neural network is a measure which refers to the ability of the network to perform more or less complicated classification tasks of its inputs via the manifestation of meaningful or spurious attractor dynamics. Here, we study the attractor-based complexity of a Boolean model of the basal ganglia-thalamocortical network. We show that the regulation of the interactive feedback is significantly involved in the maintenance of an optimal level of complexity. We also show that the complexity of the network depends sensitively on the values of its synaptic connections. These considerations support the general rationale that the synaptic plasticity and the interactive architecture play a crucial role in the computational and dynamical capabilities of biological neural networks.

Oral cancer remains one of the most common and challenging malignancies of the head and neck region. Articles were retrieved from Pubmed using specific keywords. Recent data showed that the incidence and mortality have increased over the last decades. It has poor prognosis and ranks among all cancer mortality. Tobacco smoking, alcohol, betel quid and areca nut chewing have been associated with a higher risk for oral cancers. This review summarizes the incidence, prevalence and mortality rates associated with oral cancer. We also investigated the strategies for early diagnosis, the molecular pathway of carcinogenesis, and the current treatment modalities available for oral cancer.

We study the attractor dynamics of a Boolean model of the basal ganglia-thalamocortical network as a function of its interactive synaptic connections and global threshold. We show that the regulation of the interactive feedback and global threshold are significantly involved in the maintenance and robustness of the attractor basin. These results support the hypothesis that, beyond mere structural architecture, global plasticity and interactivity play a crucial role in the computational and dynamical capabilities of biological neural networks.

Precise spatiotemporal sequences of neuronal discharges (i.e., intervals between epochs repeating more often than expected by chance), have been observed in a large set of experimental electrophysiological recordings. Sensitivity to temporal information, by itself, does not demonstrate that dynamics embedded in spike trains can be transmitted through a neural network. This study analyzes how synaptic transmission through three archetypical types of neurons (regular-spiking, thalamo-cortical and resonator), simulated by a simple spiking model, can affect the transmission of precise timings generated by a nonlinear deterministic system (i.e., the Zaslavskii mapping in the present study). The results show that cells with subthreshold oscillations (resonators) are very sensitive to stochastic inputs, and are not a good candidate for transmitting temporally coded information. Thalamo-cortical neurons may transmit very well temporal patterns in the absence of background activity, but jitter accumulates along the synaptic chain. Conversely, we observed that cortical regular-spiking neurons can propagate filtered temporal information in a reliable way through the network, and with high temporal accuracy. We discuss the results in the general framework of neural dynamics and brain theories.

We consider a model of so-called hybrid recurrent neural networks composed with Boolean input and output cells as well as sigmoid internal cells. When subjected to some infinite binary input stream, the Boolean output cells necessarily exhibit some attractor dynamics, which is assumed to be of two possible kinds, namely either meaningful or spurious, and which underlies the arising of spatiotemporal patterns of output discharges. In this context, we show that rational-weighted neural networks are computationally equivalent to deterministic Muller Turing machines, whereas all other models of real-weighted or evolving neural networks are equivalent to each other, and strictly more powerful than deterministic Muller Turing machines. In this precise sense, the analog and evolving neural networks are super-Turing. We further provide some precise mathematical characterization of the expressive powers of all these neural models. These results constitute a generalization to the current computational context of those obtained in the cases of classical as well as interactive computations. They support the idea that recurrent neural networks represent a natural model of computation beyond the Turing limits.

Neurons in the brain are known to operate under a careful balance of excitation and inhibition, which maintains neural microcircuits within the proper operational range. How this balance is played out at the mesoscopic level of neuronal populations is, however, less clear. In order to address this issue, here we use a coupled neural mass model to study computationally the dynamics of a network of cortical macrocolumns operating in a partially synchronized, irregular regime. The topology of the network is heterogeneous, with a few of the nodes acting as connector hubs while the rest are relatively poorly connected. Our results show that in this type of mesoscopic network excitation and inhibition spontaneously segregate, with some columns acting mainly in an excitatory manner while some others have predominantly an inhibitory effect on their neighbors. We characterize the conditions under which this segregation arises, and relate the character of the different columns with their topological role within the network. In particular, we show that the connector hubs are preferentially inhibitory, the more so the larger the node's connectivity. These results suggest a potential mesoscale organization of the excitation-inhibition balance in brain networks., Author Summary One of the salient characteristics of neurons is their ability to either excite or inhibit other neurons, depending on the type of neurotransmitter they use to act upon them. In fact, a careful balance between excitation and inhibition is required for the brain to operate in a sustained state, away from both epileptic activity (which would arise if excitation dominated over inhibition) and complete quiescence (which would emerge if inhibition prevailed). While much work has been devoted to study how excitation and inhibition are organized at the level of neural microcircuits, little is known about how these two effects are structured at larger scales. Here we are interested in the scale of cortical macrocolumns, large collectives of neurons that can be described computationally by population models known as neural mass models. These models are routinely used to describe the brain rhythms observed with non-invasive techniques such as EEG. In this paper we show, using a neural mass model, that a collection of coupled cortical macrocolumns self-organizes spontaneously into a dynamical state in which excitation and inhibition are segregated at the mesoscopic level, with some cortical columns (the most connected ones) being inhibitory, and the rest being excitatory.

We present a complete overview of the computational power of recurrent neural networks involved in an interactive bio-inspired computational paradigm. More precisely, we recall the results stating that interactive rational- and realweighted neural networks are Turing-equivalent and super-Turing, respectively.We further prove that interactive evolving neural networks are super-Turing, irrespective of whether their synaptic weights are modeled by rational or real numbers. These results show that the computational powers of neural nets involved in a classical or in an interactive computational framework follow similar patterns of characterization. They suggest that some intrinsic computational capabilities of the brain might lie beyond the scope of Turing-equivalentmodels of computation, hence surpass the potentialities every current standard artificial models of computation.

One of the major obstacles found when trying to construct artefacts derived from principles observed in living beings is the lack of actual dynamic hardware with autonomous capabilities. Even if programmable devices offer the possibility of modifying the functionality implemented in the device, they rely on external hardware and software elements to provide its physical configuration. In this paper we present a new family of electronic devices, called POEtic, whose architecture has been derived from the basic properties that can be extracted from the three major organization principles present in living beings: phylogenesis, ontogenesis and epigenesis. We will demonstrate that the capabilities present in these new programmable devices make them an ideal candidate for the real-time emulation of large-scale biologically inspired spiking neural network models.

In this paper, we provide a historical survey of the most significant results concerning the computational power of neural models. We distinguish three important periods: first, the early works from McCulloch and Pitts, Kleene, and Minky, where the computational equivalence between Boolean recurrent neural networks and finite state automata is established. Secondly, the two breakthroughs by Siegelmann and Sontag showing the Turing universality of rational-weighted neural networks, and the super-Turing capabilities of analog recurrent neural networks. Thirdly, the recent results by Cabessa, Siegelmann and Villa revealing the super-Turing computational potentialities of interactive and evolving recurrent neural networks.

We studied characteristics of the EEG activity and psychophysiological indices in healthy persons and patients with opiate addiction (in the states of abstinence and remission) before and after peroral introduction of 200 mg sulpiride. In the initial state, spectral characteristics of EEG in patients with opiate addiction differed from those in the control (in healthy tested subjects) by higher relative powers of low- and high-frequency components (delta and beta waves) and a considerable depression of the alpha rhythm. Treatment with sulpiride evoked changes in the spectral characteristics of EEG, which showed a significant intergroup specificity; intensification of alpha oscillations was a general effect in all groups. We conclude that the effects of sulpiride on the EEG activity comprised components typical of both neuroleptics and antidepressants; in the group of patients in the abstinence state, the pattern of effects of sulpiride was close in its profile to the effect of anxiolytics. Dynamics of the indices of psychophysiological testing after sulpiride treatment demonstrated that the drug exerts mostly positive regulating effects on the state of higher nervous functions in patients with opiate addiction.

Understanding the dynamical and computational capabilities of neural models represents an issue of central importance. In this context, recent results show that interactive evolving recurrent neural networks are super-Turing, irrespective of whether their synaptic weights are rational or real. We extend these results by showing that interactive evolving recurrent neural networks are not only super-Turing, but also capable of simulating any other possible interactive deterministic system. In this sense, interactive evolving recurrent neural networks represents a super-Turing universal model of computation, irrespective of whether their synaptic weights are rational or real.

The molecular weight and electrotopological E-state indices were used to estimate by Artificial Neural Networks aqueous solubility for a diverse set of 1291 organic compounds. The neural network with 33-4-1 neurons provided highly predictive results with r(2) = 0.91 and RMS = 0.62. The used parameters included several combinations of E-state indices with similar properties. The calculated results were similar to those published for these data by Huuskonen (2000). However, in the current study only E-state indices were used without need of additional indices (the molecular connectivity, shape, flexibility and indicator indices) also considered in the previous study. In addition, the present neural network contained three times less hidden neurons. Smaller neural networks and use of one homogeneous set of parameters provides a more robust model for prediction of aqueous solubility of chemical compounds. Limitations of the developed method for prediction of large compounds are discussed. The developed approach is available online at http://www.lnh.unil.ch/~itetko/logp.

A critical feature of brain theories is whether neurons convey a noisy rate code or a precise temporal code. One of most valuable ways to test these theories consists in collecting the electrophysiological activity of cell assembles under several experimental conditions. The sequences of cell discharges—the spike trains—form time series whose dynamics is strongly related to the information processing carried out in the brain areas under study. Our purpose is to provide a user-friendly framework of a ‘Virtual Laboratory’ where computational neuroscience analyses and display of results can be distributed over a computer network, like Internet.

The existence of precise temporal relations in sequences of spike intervals, referred to as “spatiotemporal patterns”, is suggested by brain theories that emphasize the role of temporal coding. A pattern grouping algorithm was designed to identify and to evaluate the statistical significance of such patterns, particularly for data generated according to stationary Poisson processes. The experimental time series, however, can be characterized by considerable deviations from independent stationary Poisson processes. This article describes a filtering method that de-convolute time series according to their correlation functions and makes possible an application of the pattern grouping algorithm for such data too.

This article presents a self-organising multilayered iterative algorithm that provides linear and non-linear polynomial regression models thus allowing the user to control the number and the power of the terms in the models. The accuracy of the algorithm is compared to the partial least squares (PLS) algorithm using fourteen data sets in quantitative-structure activity relationship studies. The calculated data show that the proposed method is able to select simple models characterized by a high prediction ability and thus provides a considerable interest in quantitative-structure activity relationship studies. The software is developed using client-server protocol (Java and C++ languages) and is available for world-wide users on the Web site of the authors.

Precise and repeated spike-train timings within and across neurons define spatiotemporal patterns of activity. Although the existence of these patterns in the brain is well established in several species, there has been no direct evidence of their influence on behavioral output. To address this question, up to 15 neurons were recorded simultaneously in the auditory cortex of freely moving rats while animals waited for acoustic cues in a Go/NoGo task. A total of 235 significant patterns were detected during this interval from an analysis of 13 hr of recording involving over 1 million spikes. Of particular interest were 129 (55%) patterns that were significantly associated with the type of response the animal made later, independent of whether the response was that prompted by the cue because the response occurred later and the cue was chosen randomly. Of these behavior-predicting patterns, half (59/129) were associated with an enhanced tendency to go in response to the stimulus, and for 11 patterns of this subset, trials including the pattern were followed by significantly faster reaction time than those lacking the pattern. The remaining behavior-predicting patterns were associated with an enhanced NoGo tendency. Overall mean discharge rates did not vary across trials. Hence, these data demonstrate that particular spatiotemporal patterns predict future behavioral responses. Such presignal activity could form templates for extracting specific sensory information, motor programs prespecifying preference for a particular act, and/or some intermediate, associative brain process.

Pruning methods for feed-forward artificial neural networks trained by the cascade-correlation learning algorithm are proposed. The cascade-correlation algorithm starts with a small network and dyn...

This study investigates the emerging possibilities of combining unsupervised and supervised learning in neural network ensembles. Such strategy is used to get an efficient partition of a noisy input data set in order to focus the training of neural networks on the most complex and informative domains of the data set and accelerate the learning phase. The proposed algorithm provides a good prediction accuracy using fewer cases from non-informative domains according to a correlative measure of dependency between cases of the training set. This measure takes into account internal relationships amid analyzed data and can be used to cluster neighbor cases in a multidimensional space and to filter out the outliers. The possible relation of the proposed algorithm to brain processing occurring in the thalamo-cortical pathway is discussed.

This study presents two efficient algorithms – combinatorial and probabilistic combinatorial methods (CM and PCM) – for estimation of a number of precise patterns of discharges that occur by chance in records of multiple single-unit spike trains. The confidence limits estimated by these methods are in good agreement with different sets of simulated test data as well as with the ad-hoc method. Both combinatorial methods provided a better accuracy than the bootstrap algorithm and in most cases of nonstationary data PCM provided better estimations than the ad-hoc method. Introduction of a jitter for searching patterns with a precision of a few milliseconds and burst filtering may introduce biases in the estimations. Comparison of a new filtering procedure based upon a filtering frequency with previously described schemes of filtering indicates the possibility of using a simple setting which remains accurate over a wide range of parameters. We aim to implement a combination of PCM for estimations of the number of patterns formed by three to seven spikes and CM for higher-order complexities for estimations during experiments in progress.

We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of ω-automata, and then translating the most refined classification of ω-automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits.

Understanding the dynamical and computational capabilities of neural models represents an issue of central importance. Here, we consider a model of first-order recurrent neural networks provided with the possibility to evolve over time and involved in a basic interactive and memory active computational paradigm. In this context, we prove that the so-called interactive evolving recurrent neural networks are computationally equivalent to interactive Turing machines with advice, hence capable of super-Turing potentialities. We further provide a precise characterisation of the ω-translations realised by these networks. Therefore, the consideration of evolving capabilities in a first-order neural model provides the potentiality to break the Turing barrier.

Neuroheuristics, or Neuristics, is a term issued from the Greek terms neuron (nerve) and heuriskein (to find, to discover). It refers to that branch of Science aimed at exploring the Neurosciences through an ongoing process continuously renewed at each successive step of its advancement towards understanding the brain in its entirety. This chapter presents a neuroheuristic approach to the decision making process, firstly in an animal experiment, in an attempt to investigate the basic processes away from an anthropological perspective, and secondly in a classical neuroeconomic paradigm, the Ultimatum Game (UG). Multiple electrodes for multiple neuronal recordings were chronically implanted in cerebral cortical areas of freely-moving rats trained in a response choice task. Invariant preferred firing sequences appeared in association with the response predicted by the subject or in association with specific errors of decision. We recorded EEG and analyzed event-related potentials of subjects in a two conditions variant of UG where human players acted either as proposers with computer-controlled virtual partners or as responders to offers made by a virtual proposer. A proposer, in contrast to a responder, has to store the future proposed value in short-term memory and engage retrieval processes after getting the responder’s reaction. Our EEG results support the hypothesis that while playing the role of proposers human subjects engage in a specific retrieval process while performing UG.

We investigated the effect of reciprocal connections in a network of modules of simulated spiking neurons. The neural activity is recorded by means of virtual electrodes and EEG-like signals, called electrochipograms (EChG), are analyzed by time- and frequency-domain methods. Bio-inspired processes in the circuits drive the build-up of auto-associative links within each module, which generate an areal activity, recorded by EChG, that reflect the changes in the corresponding functional connectivity within and between neuronal modules. We found that circuits with short inter-layer reciprocal projections exhibited enhanced response as to the stimulus, as to the inner-activity and long inter-layer projections make circuit exhibit non-coherent behavior. We show evidence that all networks of modules are able to process and maintain patterns of activity associated with the stimulus after its offset.

International audience; We provide a refined hierarchical classification of first-order recurrent neural networks made up of McCulloch and Pitts cells. The classification is achieved by first proving the equivalence between the expressive powers of such neural networks and Muller automata, and then translating the Wadge classification theory from the automata-theoretic to the neural network context. The obtained hierarchical classification of neural networks consists of a decidable pre-well ordering of width 2 and height !!, and a decidability procedure of this hierarchy is provided. Notably, this classification is shown to be intimately related to the attractive properties of the networks, and hence provides a new refined measurement of the computational power of these networks in terms of their attractive behaviours.

A ten layers feedforward network characterized by diverging/ converging patterns of projection between successive layers is activated by an external spatio-temporal input pattern fed to layer 1 in presence of stochastic background activities fed to all layers. We used three dynamical systems to derive the external input spike trains including the temporal information, and two types of neuron models for the network, i.e. either a simple spiking neuron (SSN) or a multiple-timescale adaptive threshold neuron (MAT). We observed an unimodal integration effect as a function of the order of the layers and confirmed that the MAT model is likely to be more efficient in integrating and transmitting the temporal structure embedded in the external input.

We simulated the activity of hierarchically organized spiking neural networks characterized by an initial developmental phase featuring cell death followed by spike timing dependent synaptic plasticity in presence of background noise. Upstream networks receiving spatiotemporally organized external inputs projected to downstream networks disconnected from external inputs. The observation of precise firing sequences, formed by recurrent patterns of spikes intervals above chance levels, suggested the build-up of an unsupervised connectivity able to sustain and preserve temporal information processing.

The present study investigates an application of artificial neural networks (ANNs) for use in pharmaceutical fingerprinting. Several pruning algorithms were applied to decrease the dimension of the input parameter data set. A localized fingerprint region was identified within the original input parameter space from which a subset of input parameters was extracted leading to enhanced ANN performance. The present results confirm that ANNs can provide a fast, accurate, and consistent methodology applicable to pharmaceutical fingerprinting.

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