Multi-response Modelling of the Maillard reaction in a model cheese

International audienceProcessed cheese derives from a secondary milk processing step that involves mixing and heating dairy (cheese, butter and milk powders) and non-dairy products (emulsifiers). This processing yields a homogeneous product, usually spreadable, with a shelf-life often longer than 6 months. During cheese processing and storage, lipid oxidation, caramelization and Maillard reactions occur and produce odour-active compounds. In this study, a methodological approach was used in order to (i) identify odorants responsible for flavor attributes or compouds involved in the reaction chain, (ii) monitor the evolution of these markers during the heat treatment applied to the matrix, (iii) establish an observable reaction scheme and (iv) model and predict the evolution of these compounds during thermal operations. In this aim, a model cheese and a cooking cell were elaborated. Various couplings of gas chromatography with olfactometry were used to identify odorous compounds. Two-dimensional comprehensive chromatography allowed a semi-quantitation of trace and ultra-trace compounds, while precursors were quantitated by high performance liquid chromatography. An observable reaction scheme of the Maillard reaction was extracted from these data and makes the multi-response modeling step possible despite a partial quantitation of the volatile compounds. Finally, we obtained a formal model combining 19 components (including four odorants) connected by 14 stoechiometric balanced reactions. This model makes it possible to predict the evolution of these components depending on the initial content of lactose, galactose and according to the heat treatment applied to the cheese matrix. This work was carried out with the financial support of the ANR-Agence Nationale de la Recherche-The French National Research Agency under the Programme National de Recherche en Alimentation et nutrition humaine , project ANR-06-PNRA-023REACTIAL "Prediction and control of the appearance or disappearance of reactional markers during food process and conservation "

Modelling the Maillard reaction during the cooking of a model cheese

During processing and storage of industrial processed cheese, odorous compounds are formed. Some of them are potentially unwanted for the flavour of the product. To reduce the appearance of these compounds, a methodological approach was employed. It consists of: (i) the identification of the key compounds or precursors responsible for the off-flavour observed, (ii) the monitoring of these markers during the heat treatments applied to the cheese medium, (iii) the establishment of an observable reaction scheme adapted from a literature survey to the compounds identified in the heated cheese medium (iv) the multi-responses stoichiokinetic modelling of these reaction markers. Systematic two-dimensional gas chromatography time-of-flight mass spectrometry was used for the semi-quantitation of trace compounds. Precursors were quantitated by high-performance liquid chromatography. The experimental data obtained were fitted to the model with 14 elementary linked reactions forming a multi-response observable reaction scheme

Time Scale Hierarchies in the Functional Organization of Complex Behaviors

Traditional approaches to cognitive modelling generally portray cognitive events in terms of ‘discrete’ states (point attractor dynamics) rather than in terms of processes, thereby neglecting the time structure of cognition. In contrast, more recent approaches explicitly address this temporal dimension, but typically provide no entry points into cognitive categorization of events and experiences. With the aim to incorporate both these aspects, we propose a framework for functional architectures. Our approach is grounded in the notion that arbitrary complex (human) behaviour is decomposable into functional modes (elementary units), which we conceptualize as low-dimensional dynamical objects (structured flows on manifolds). The ensemble of modes at an agent’s disposal constitutes his/her functional repertoire. The modes may be subjected to additional dynamics (termed operational signals), in particular, instantaneous inputs, and a mechanism that sequentially selects a mode so that it temporarily dominates the functional dynamics. The inputs and selection mechanisms act on faster and slower time scales then that inherent to the modes, respectively. The dynamics across the three time scales are coupled via feedback, rendering the entire architecture autonomous. We illustrate the functional architecture in the context of serial behaviour, namely cursive handwriting. Subsequently, we investigate the possibility of recovering the contributions of functional modes and operational signals from the output, which appears to be possible only when examining the output phase flow (i.e., not from trajectories in phase space or time)

Type-specific dendritic integration in mouse retinal ganglion cells

Neural computation relies on the integration of synaptic inputs across a neuron’s dendritic arbour. However, it is far from understood how different cell types tune this process to establish cell-type specific computations. Here, using two-photon imaging of dendritic Ca2+ signals, electrical recordings of somatic voltage and biophysical modelling, we demonstrate that four morphologically distinct types of mouse retinal ganglion cells with overlapping excitatory synaptic input (transient Off alpha, transient Off mini, sustained Off, and F-mini Off) exhibit type-specific dendritic integration profiles: in contrast to the other types, dendrites of transient Off alpha cells were spatially independent, with little receptive field overlap. The temporal correlation of dendritic signals varied also extensively, with the highest and lowest correlation in transient Off mini and transient Off alpha cells, respectively. We show that differences between cell types can likely be explained by differences in backpropagation efficiency, arising from the specific combinations of dendritic morphology and ion channel densities

A novel glucagon-related peptide (GCRP) and its receptor GCRPR account for coevolution of their family members in vertebrates

The glucagon (GCG) peptide family consists of GCG, glucagon-like peptide 1 (GLP1), and GLP2, which are derived from a common GCG precursor, and the glucose-dependent insulinotropic polypeptide (GIP). These peptides interact with cognate receptors, GCGR, GLP1R, GLP2R, and GIPR, which belong to the secretin-like G protein-coupled receptor (GPCR) family. We used bioinformatics to identify genes encoding a novel GCG-related peptide (GCRP) and its cognate receptor, GCRPR. The GCRP and GCRPR genes were found in representative tetrapod taxa such as anole lizard, chicken, and Xenopus, and in teleosts including medaka, fugu, tetraodon, and stickleback. However, they were not present in mammals and zebrafish. Phylogenetic and genome synteny analyses showed that GCRP emerged through two rounds of whole genome duplication (2R) during early vertebrate evolution. GCRPR appears to have arisen by local tandem gene duplications from a common ancestor of GCRPR, GCGR, and GLP2R after 2R. Biochemical ligand-receptor interaction analyses revealed that GCRP had the highest affinity for GCRPR in comparison to other GCGR family members. Stimulation of chicken, Xenopus, and medaka GCRPRs activated Gαs-mediated signaling. In contrast to chicken and Xenopus GCRPRs, medaka GCRPR also induced Gαq/11-mediated signaling. Chimeric peptides and receptors showed that the K(16)M(17)K(18) and G(16)Q(17)A(18) motifs in GCRP and GLP1, respectively, may at least in part contribute to specific recognition of their cognate receptors through interaction with the receptor core domain. In conclusion, we present novel data demonstrating that GCRP and GCRPR evolved through gene/genome duplications followed by specific modifications that conferred selective recognition to this ligand-receptor pair

From Computer Metaphor to Computational Modeling: The Evolution of Computationalism

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

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

The effect of morphology upon electrophysiological responses of retinal ganglion cells: simulation results

Retinal ganglion cells (RGCs) display differences in their morphology and intrinsic electrophysiology. The goal of this study is to characterize the ionic currents that explain the behavior of ON and OFF RGCs and to explore if all morphological types of RGCs exhibit the phenomena described in electrophysiological data. We extend our previous single compartment cell models of ON and OFF RGCs to more biophysically realistic multicompartment cell models and investigate the effect of cell morphology on intrinsic electrophysiological properties. The membrane dynamics are described using the Hodgkin - Huxley type formalism. A subset of published patch-clamp data from isolated intact mouse retina is used to constrain the model and another subset is used to validate the model. Two hundred morphologically distinct ON and OFF RGCs are simulated with various densities of ionic currents in different morphological neuron compartments. Our model predicts that the differences between ON and OFF cells are explained by the presence of the low voltage activated calcium current in OFF cells and absence of such in ON cells. Our study shows through simulation that particular morphological types of RGCs are capable of exhibiting the full range of phenomena described in recent experiments. Comparisons of outputs from different cells indicate that the RGC morphologies that best describe recent experimental results are ones that have a larger ratio of soma to total surface area

Critical slowing down as a biomarker for seizure susceptibility.

The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system suggest that prior to a seizure, recorded brain signals may exhibit critical slowing down, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we monitored key signatures of critical slowing down prior to seizures. The metrics used to detect critical slowing down fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing down is a reliable indicator that could be used in seizure forecasting algorithms