Modeling the water uptake by chicken carcasses during cooling by immersion

In this study, water uptake by poultry carcasses during cooling by water immersion was modeled using artificial neural networks. Data from twenty-five independent variables and the final mass of the carcass were collected in an industrial plant to train and validate the model. Different network structures with one hidden layer were tested, and the Downhill Simplex method was used to optimize the synaptic weights. In order to accelerate the optimization calculus, Principal Component Analysis (PCA) was used to preprocess the input data. The obtained results were: i) PCA reduced the number of input variables from twenty-five to ten; ii) the neural network structure 4-6-1 was the one with the best result; iii) PCA gave the following order of importance: parameters of mass transfer, heat transfer, and initial characteristics of the carcass. The main contributions of this work were to provide an accurate model for predicting the final content of water in the carcasses and a better understanding of the variables involved.

Dynamic knowledge representation in connectionist systems

One of the most pervading concepts underlying computational models of information processing in the brain is linear input integration of rate coded uni-variate information by neurons. After a suitable learning process this results in neuronal structures that statically represent knowledge as a vector of real valued synaptic weights. Although this general framework has contributed to the many successes of connectionism, in this paper we argue that for all but the most basic of cognitive processes, a more complex, multi-variate dynamic neural coding mechanism is required - knowledge should not be spacially bound to a particular neuron or group of neurons. We conclude the paper with discussion of a simple experiment that illustrates dynamic knowledge representation in a spiking neuron connectionist system.

Precificação de opções de dólar no mercado brasileiro utilizando redes neurais e algoritmos genéticos

Esse trabalho comparou, para condições macroeconômicas usuais, a eficiência do modelo de Redes Neurais Artificiais (RNAs) otimizadas por Algoritmos Genéticos (AGs) na precificação de opções de Dólar à Vista aos seguintes modelos de precificação convencionais: Black-Scholes, Garman-Kohlhagen, Árvores Trinomiais e Simulações de Monte Carlo. As informações utilizadas nesta análise, compreendidas entre janeiro de 1999 e novembro de 2006, foram disponibilizadas pela Bolsa de Mercadorias e Futuros (BM&F) e pelo Federal Reserve americano. As comparações e avaliações foram realizadas com o software MATLAB, versão 7.0, e suas respectivas caixas de ferramentas que ofereceram o ambiente e as ferramentas necessárias à implementação e customização dos modelos mencionados acima. As análises do custo do delta-hedging para cada modelo indicaram que, apesar de mais complexa, a utilização dos Algoritmos Genéticos exclusivamente para otimização direta (binária) dos pesos sinápticos das Redes Neurais não produziu resultados significativamente superiores aos modelos convencionais.

Dinâmica da Plasticidade Sináptica em neurônios do hipocampo durante ciclos de sono: um estudo computacional

Several research lines show that sleep favors memory consolidation and learning. It has been proposed that the cognitive role of sleep is derived from a global scaling of synaptic weights, able to homeostatically restore the ability to learn new things, erasing memories overnight. This phenomenon is typical of slow-wave sleep (SWS) and characterized by non-Hebbian mechanisms, i.e., mechanisms independent of synchronous neuronal activity. Another view holds that sleep also triggers the specific enhancement of synaptic connections, carrying out the embossing of certain mnemonic traces within a lattice of synaptic weights rescaled each night. Such an embossing is understood as the combination of Hebbian and non-Hebbian mechanisms, capable of increasing and decreasing respectively the synaptic weights in complementary circuits, leading to selective memory improvement and a restructuring of synaptic configuration (SC) that can be crucial for the generation of new behaviors ( insights ). The empirical findings indicate that initiation of Hebbian plasticity during sleep occurs in the transition of the SWS to the stage of rapid eye movement (REM), possibly due to the significant differences between the firing rates regimes of the stages and the up-regulation of factors involved in longterm synaptic plasticity. In this study the theories of homeostasis and embossing were compared using an artificial neural network (ANN) fed with action potentials recorded in the hippocampus of rats during the sleep-wake cycle. In the simulation in which the ANN did not apply the long-term plasticity mechanisms during sleep (SWS-transition REM), the synaptic weights distribution was re-scaled inexorably, for its mean value proportional to the input firing rate, erasing the synaptic weights pattern that had been established initially. In contrast, when the long-term plasticity is modeled during the transition SWSREM, an increase of synaptic weights were observed in the range of initial/low values, redistributing effectively the weights in a way to reinforce a subset of synapses over time. The results suggest that a positive regulation coming from the long-term plasticity can completely change the role of sleep: its absence leads to forgetting; its presence leads to a positive mnemonic change

Algoritmos genéticos e processamento paralelo aplicados à definição e treinamento de redes neurais perceptron de múltiplas camadas

ln this work, it was deveIoped a parallel cooperative genetic algorithm with different evolution behaviors to train and to define architectures for MuItiIayer Perceptron neural networks. MuItiIayer Perceptron neural networks are very powerful tools and had their use extended vastIy due to their abiIity of providing great resuIts to a broad range of appIications. The combination of genetic algorithms and parallel processing can be very powerful when applied to the Iearning process of the neural network, as well as to the definition of its architecture since this procedure can be very slow, usually requiring a lot of computational time. AIso, research work combining and appIying evolutionary computation into the design of neural networks is very useful since most of the Iearning algorithms deveIoped to train neural networks only adjust their synaptic weights, not considering the design of the networks architecture. Furthermore, the use of cooperation in the genetic algorithm allows the interaction of different populations, avoiding local minima and helping in the search of a promising solution, acceIerating the evolutionary process. Finally, individuaIs and evolution behavior can be exclusive on each copy of the genetic algorithm running in each task enhancing the diversity of populations

Experience-dependent upregulation of multiple plasticity factors in the hippocampus during early REM sleep

Sleep is beneficial to learning, but the underlying mechanisms remain controversial. The synaptic homeostasis hypothesis (SHY) proposes that the cognitive function of sleep is related to a generalized rescaling of synaptic weights to intermediate levels, due to a passive downregulation of plasticity mechanisms. A competing hypothesis proposes that the active upscaling and downscaling of synaptic weights during sleep embosses memories in circuits respectively activated or deactivated during prior waking experience, leading to memory changes beyond rescaling. Both theories have empirical support but the experimental designs underlying the conflicting studies are not congruent, therefore a consensus is yet to be reached. To advance this issue, we used real-time PCR and electrophysiological recordings to assess gene expression related to synaptic plasticity in the hippocampus and primary somatosensory cortex of rats exposed to novel objects, then kept awake (WK) for 60 min and finally killed after a 30 min period rich in WK, slow-wave sleep (SWS) or rapid-eye-movement sleep (REM). Animals similarly treated but not exposed to novel objects were used as controls. We found that the mRNA levels of Arc, Egr1, Fos, Ppp2ca and Ppp2r2d were significantly increased in the hippocampus of exposed animals allowed to enter REM, in comparison with control animals. Experience-dependent changes during sleep were not significant in the hippocampus for Bdnf, Camk4, Creb1, and Nr4a1, and no differences were detected between exposed and control SWS groups for any of the genes tested. No significant changes in gene expression were detected in the primary somatosensory cortex during sleep, in contrast with previous studies using longer post-stimulation intervals (>180 min). The experience-dependent induction of multiple plasticity-related genes in the hippocampus during early REM adds experimental support to the synaptic embossing theory.

Emergence of sensory selection mechanisms in Artificial Life simulations

Background The evolutionary advantages of selective attention are unclear. Since the study of selective attention began, it has been suggested that the nervous system only processes the most relevant stimuli because of its limited capacity [1]. An alternative proposal is that action planning requires the inhibition of irrelevant stimuli, which forces the nervous system to limit its processing [2]. An evolutionary approach might provide additional clues to clarify the role of selective attention. Methods We developed Artificial Life simulations wherein animals were repeatedly presented two objects, "left" and "right", each of which could be "food" or "non-food." The animals' neural networks (multilayer perceptrons) had two input nodes, one for each object, and two output nodes to determine if the animal ate each of the objects. The neural networks also had a variable number of hidden nodes, which determined whether or not it had enough capacity to process both stimuli (Table 1). The evolutionary relevance of the left and the right food objects could also vary depending on how much the animal's fitness was increased when ingesting them (Table 1). We compared sensory processing in animals with or without limited capacity, which evolved in simulations in which the objects had the same or different relevances. Table 1. Nine sets of simulations were performed, varying the values of food objects and the number of hidden nodes in the neural networks. The values of left and right food were swapped during the second half of the simulations. Non-food objects were always worth -3. The evolution of neural networks was simulated by a simple genetic algorithm. Fitness was a function of the number of food and non-food objects each animal ate and the chromosomes determined the node biases and synaptic weights. During each simulation, 10 populations of 20 individuals each evolved in parallel for 20,000 generations, then the relevance of food objects was swapped and the simulation was run again for another 20,000 generations. The neural networks were evaluated by their ability to identify the two objects correctly. The detectability (d') for the left and the right objects was calculated using Signal Detection Theory [3]. Results and conclusion When both stimuli were equally relevant, networks with two hidden nodes only processed one stimulus and ignored the other. With four or eight hidden nodes, they could correctly identify both stimuli. When the stimuli had different relevances, the d' for the most relevant stimulus was higher than the d' for the least relevant stimulus, even when the networks had four or eight hidden nodes. We conclude that selection mechanisms arose in our simulations depending not only on the size of the neuron networks but also on the stimuli's relevance for action.

Cellular mechanisms of burst firing-mediated long-term depression in rat neocortical pyramidal cells

During wakefulness and sleep, neurons in the neocortex emit action potentials tonically or in rhythmic bursts, respectively. However, the role of synchronized discharge patterns is largely unknown. We have recently shown that pairings of excitatory postsynaptic potentials (EPSPs) and action potential bursts or single spikes lead to long-term depression (burst-LTD) or long-term potentiation, respectively. In this study, we elucidate the cellular mechanisms of burst-LTD and characterize its functional properties. Whole-cell patch-clamp recordings were obtained from layer V pyramidal cells in somatosensory cortex of juvenile rats in vitro and composite EPSPs and EPSCs were evoked extracellularly in layers II/III. Repetitive burst-pairings led to a long-lasting depression of EPSPs and EPSCs that was blocked by inhibitors of metabotropic glutamate group 1 receptors, phospholipase C, protein kinase C (PKC) and calcium release from the endoplasmic reticulum, and that required an intact machinery for endocytosis. Thus, burst-LTD is induced via a Ca2+- and phosphatidylinositol-dependent activation of PKC and expressed through phosphorylation-triggered endocytosis of AMPA receptors. Functionally, burst-LTD is inversely related to EPSP size and bursts dominate single spikes in determining the sign of synaptic plasticity. Thus burst-firing constitutes a signal by which coincident synaptic inputs are proportionally downsized. Overall, our data thus suggest a mechanism by which synaptic weights can be reconfigured during non-rapid eye movement sleep.

Developmental self-construction and -configuration of functional neocortical neuronal networks.

The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative ('winner-take-all', WTA) network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data.

A model of long-term memory storage in the cerebellar cortex: A possible role for plasticity at parallel fiber synapses onto stellate/basket interneurons

By evoking changes in climbing fiber activity, movement errors are thought to modify synapses from parallel fibers onto Purkinje cells (pf*Pkj) so as to improve subsequent motor performance. Theoretical arguments suggest there is an intrinsic tradeoff, however, between motor adaptation and long-term storage. Assuming a baseline rate of motor errors is always present, then repeated performance of any learned movement will generate a series of climbing fiber-mediated corrections. By reshuffling the synaptic weights responsible for any given movement, such corrections will degrade the memories for other learned movements stored in overlapping sets of synapses. The present paper shows that long-term storage can be accomplished by a second site of plasticity at synapses from parallel fibers onto stellate/basket interneurons (pf*St/Bk). Plasticity at pf*St/Bk synapses can be insulated from ongoing fluctuations in climbing fiber activity by assuming that changes in pf*St/Bk synapses occur only after changes in pf*Pkj synapses have built up to a threshold level. Although climbing fiber-dependent plasticity at pf*Pkj synapses allows for the exploration of novel motor strategies in response to changing environmental conditions, plasticity at pf*St/Bk synapses transfers successful strategies to stable long-term storage. To quantify this hypothesis, both sites of plasticity are incorporated into a dynamical model of the cerebellar cortex and its interactions with the inferior olive. When used to simulate idealized motor conditioning trials, the model predicts that plasticity develops first at pf*Pkj synapses, but with additional training is transferred to pf*St/Bk synapses for long-term storage.

Adaptive agents in changing environments, the role of modularity

We explored the role of modularity as a means to improve evolvability in populations of adaptive agents. We performed two sets of artificial life experiments. In the first, the adaptive agents were neural networks controlling the behavior of simulated garbage collecting robots, where modularity referred to the networks architectural organization and evolvability to the capacity of the population to adapt to environmental changes measured by the agents performance. In the second, the agents were programs that control the changes in network's synaptic weights (learning algorithms), the modules were emerged clusters of symbols with a well defined function and evolvability was measured through the level of symbol diversity across programs. We found that the presence of modularity (either imposed by construction or as an emergent property in a favorable environment) is strongly correlated to the presence of very fit agents adapting effectively to environmental changes. In the case of learning algorithms we also observed that character diversity and modularity are also strongly correlated quantities. © 2014 Springer Science+Business Media New York.

Uncovering the encoding and transmission of behaviourally relevant information in neural activity

Most cognitive functions require the encoding and routing of information across distributed networks of brain regions. Information propagation is typically attributed to physical connections existing between brain regions, and contributes to the formation of spatially correlated activity patterns, known as functional connectivity. While structural connectivity provides the anatomical foundation for neural interactions, the exact manner in which it shapes functional connectivity is complex and not yet fully understood. Additionally, traditional measures of directed functional connectivity only capture the overall correlation between neural activity, and provide no insight on the content of transmitted information, limiting their ability in understanding neural computations underlying the distributed processing of behaviorally-relevant variables. In this work, we first study the relationship between structural and functional connectivity in simulated recurrent spiking neural networks with spike timing dependent plasticity. We use established measures of time-lagged correlation and overall information propagation to infer the temporal evolution of synaptic weights, showing that measures of dynamic functional connectivity can be used to reliably reconstruct the evolution of structural properties of the network. Then, we extend current methods of directed causal communication between brain areas, by deriving an information-theoretic measure of Feature-specific Information Transfer (FIT) quantifying the amount, content and direction of information flow. We test FIT on simulated data, showing its key properties and advantages over traditional measures of overall propagated information. We show applications of FIT to several neural datasets obtained with different recording methods (magneto and electro-encephalography, spiking activity, local field potentials) during various cognitive functions, ranging from sensory perception to decision making and motor learning. Overall, these analyses demonstrate the ability of FIT to advance the investigation of communication between brain regions, uncovering the previously unaddressed content of directed information flow.

Synaptic compensation on Hopfield network: implications for memory rehabilitation

The discrete-time neural network proposed by Hopfield can be used for storing and recognizing binary patterns. Here, we investigate how the performance of this network on pattern recognition task is altered when neurons are removed and the weights of the synapses corresponding to these deleted neurons are divided among the remaining synapses. Five distinct ways of distributing such weights are evaluated. We speculate how this numerical work about synaptic compensation may help to guide experimental studies on memory rehabilitation interventions.

Impact Of Pregabalin Treatment On Synaptic Plasticity And Glial Reactivity During The Course Of Experimental Autoimmune Encephalomyelitis.

Multiple sclerosis (MS) is an autoimmune and neurodegenerative disease that affects young adults. It is characterized by generating a chronic demyelinating autoimmune inflammation in the central nervous system. An experimental model for studying MS is the experimental autoimmune encephalomyelitis (EAE), induced by immunization with antigenic proteins from myelin. The present study investigated the evolution of EAE in pregabalin treated animals up to the remission phase. The results demonstrated a delay in the onset of the disease with statistical differences at the 10th and the 16th day after immunization. Additionally, the walking track test (CatWalk) was used to evaluate different parameters related to motor function. Although no difference between groups was obtained for the foot print pressure, the regularity index was improved post treatment, indicating a better motor coordination. The immunohistochemical analysis of putative synapse preservation and glial reactivity revealed that pregabalin treatment improved the overall morphology of the spinal cord. A preservation of circuits was depicted and the glial reaction was downregulated during the course of the disease. qRT-PCR data did not show immunomodulatory effects of pregabalin, indicating that the positive effects were restricted to the CNS environment. Overall, the present data indicate that pregabalin is efficient for reducing the seriousness of EAE, delaying its course as well as reducing synaptic loss and astroglial reaction.

Synaptic Rearrangement Following Axonal Injury: Old And New Players.

Following axotomy, the contact between motoneurons and muscle fibers is disrupted, triggering a retrograde reaction at the neuron cell body within the spinal cord. Together with chromatolysis, a hallmark of such response to injury is the elimination of presynaptic terminals apposing to the soma and proximal dendrites of the injured neuron. Excitatory inputs are preferentially eliminated, leaving the cells under an inhibitory influence during the repair process. This is particularly important to avoid glutamate excitotoxicity. Such shift from transmission to a regeneration state is also reflected by deep metabolic changes, seen by the regulation of several genes related to cell survival and axonal growth. It is unclear, however, how exactly synaptic stripping occurs, but there is substantial evidence that glial cells play an active role in this process. In one hand, immune molecules, such as the major histocompatibility complex (MHC) class I, members of the complement family and Toll-like receptors are actively involved in the elimination/reapposition of presynaptic boutons. On the other hand, plastic changes that involve sprouting might be negatively regulated by extracellular matrix proteins such as Nogo-A, MAG and scar-related chondroitin sulfate proteoglycans. Also, neurotrophins, stem cells, physical exercise and several drugs seem to improve synaptic stability, leading to functional recovery after lesion.