Synergy, redundancy and unnormalized Granger causality

We analyze by means of Granger causality the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. Whilst fully conditioned Granger causality is not affected by synergy, the pairwise analysis fails to put in evidence synergetic effects. We show that maximization of the total Granger causality to a given target, over all the possible partitions of the set of driving variables, puts in evidence redundant multiplets of variables influencing the target, provided that an unnormalized definition of Granger causality is adopted. Along the same lines we also introduce a pairwise index of synergy (w.r.t. to information flow to a third variable) which is zero when two independent sources additively influence a common target; thus, this definition differs from previous definitions of synergy.

Dynamics and robustness of familiarity memory

When presented with an item or a face, one might have a sense of recognition without the ability to recall when or where the stimulus has been encountered before. This sense of recognition is called familiarity memory. Following previous computational studies of familiarity memory, we investigate the dynamical properties of familiarity discrimination and contrast two different familiarity discriminators: one based on the energy of the neural network and the other based on the time derivative of the energy. We show how the familiarity signal decays rapidly after stimulus presentation. For both discriminators, we calculate the capacity using mean field analysis. Compared to recall capacity (the classical associative memory in Hopfield nets), both the energy and the slope discriminators have bigger capacity, yet the energy-based discriminator has a higher capacity than one based on its time derivative. Finally, both discriminators are found to have a different noise dependence.

Instabilities in attractor networks with fast synaptic fluctuations and partial updating of the neurons activity

We present and study a probabilistic neural automaton in which the fraction of simultaneously-updated neurons is a parameter, rhoin(0,1). For small rho, there is relaxation towards one of the attractors and a great sensibility to external stimuli and, for rho > or = rho(c), itinerancy among attractors. Tuning rho in this regime, oscillations may abruptly change from regular to chaotic and vice versa, which allows one to control the efficiency of the searching process. We argue on the similarity of the model behavior with recent observations, and on the possible role of chaos in neurobiology.

Competition between synaptic depression and facilitation in attractor neural networks

We study the effect of competition between short-term synaptic depression and facilitation on the dynamic properties of attractor neural networks, using Monte Carlo simulation and a mean-field analysis. Depending on the balance of depression, facilitation, and the underlying noise, the network displays different behaviors, including associative memory and switching of activity between different attractors. We conclude that synaptic facilitation enhances the attractor instability in a way that (1) intensifies the system adaptability to external stimuli, which is in agreement with experiments, and (2) favors the retrieval of information with less error during short time intervals.

Hysteresis and bistability in a realistic cell model for calcium oscillations and action potential firing

Many cells reveal oscillatory behavior. Some cells reveal action-potential firing resulting from Hodgkin-Huxley (HH) type dynamics of ion channels in the cell membrane. Another type of oscillation relates to periodic inositol triphospate (IP3)-mediated calcium transients in the cytosol. In this study we present a bifurcation analysis of a cell with an excitable membrane and an IP3-mediated intracellular calcium oscillator. With IP3 concentration as a control parameter the model reveals a complex, rich spectrum of both stable and unstable solutions with hysteresis corresponding to experimental data. Our results reveal the emergence of complex behavior due to interactions between subcomponents with a relatively simple dynamical behavior.

Control of neural chaos by synaptic noise

We study neural automata - or neurobiologically inspired cellular automata - which exhibits chaotic itinerancy among the different stored patterns or memories. This is a consequence of activity-dependent synaptic fluctuations, which continuously destabilize the attractor and induce irregular hopping to other possible attractors. The nature of these irregularities depends on the dynamic details, namely, on the intensity of the synaptic noise and the number of sites of the network, which are synchronously updated at each time step. Varying these factors, different regimes occur, ranging from regular to chaotic dynamics. As a result, and in absence of external agents, the chaotic behavior may turn regular after tuning the noise intensity. It is argued that a similar mechanism might be on the basis of self-controlling chaos in natural systems.

Effects of fast presynaptic noise in attractor neural networks

We study both analytically and numerically the effect of presynaptic noise on the transmission of information in attractor neural networks. The noise occurs on a very short timescale compared to that for the neuron dynamics and it produces short-time synaptic depression. This is inspired in recent neurobiological findings that show that synaptic strength may either increase or decrease on a short timescale depending on presynaptic activity. We thus describe a mechanism by which fast presynaptic noise enhances the neural network sensitivity to an external stimulus. The reason is that, in general, presynaptic noise induces nonequilibrium behavior and, consequently, the space of fixed points is qualitatively modified in such a way that the system can easily escape from the attractor. As a result, the model shows, in addition to pattern recognition, class identification and categorization, which may be relevant to the understanding of some of the brain complex tasks.

Effects of Fast Presynaptic Noise in Attractor Neural Networks

We study both analytically and numerically the effect of presynaptic noise on the transmission of information in attractor neural networks. The noise occurs on a very short timescale compared to that for the neuron dynamics and it produces short-time synaptic depression. This is inspired in recent neurobiological findings that show that synaptic strength may either increase or decrease on a short timescale depending on presynaptic activity. We thus describe a mechanism by which fast presynaptic noise enhances the neural network sensitivity to an external stimulus. The reason is that, in general, presynaptic noise induces nonequilibrium behavior and, consequently, the space of fixed points is qualitatively modified in such a way that the system can easily escape from the attractor. As a result, the model shows, in addition to pattern recognition, class identification and categorization, which may be relevant to the understanding of some of the brain complex tasks.

Effects of static and dynamic disorder on the performance of neural automata

We report on both analytical and numerical results concerning stochastic Hopfield-like neural automata exhibiting the following (biologically inspired) features: (1) Neurons and synapses evolve in time as in contact with respective baths at different temperatures; (2) the connectivity between neurons may be tuned from full connection to high random dilution, or to the case of networks with the small-world property and/or scale-free architecture; and (3) there is synaptic kinetics simulating repeated scanning of the stored patterns. Although these features may apparently result in additional disorder, the model exhibits, for a wide range of parameter values, an extraordinary computational performance, and some of the qualitative behaviors observed in natural systems. In particular, we illustrate here very efficient and robust associative memory, and jumping between pattern attractors.

Optimal concentration of ammonium ion in a minimal synthetic medium for the growth of Candida albicans

Candida albicans strain B 311-10 with and without starvation was cultivated in the minimal synthetic medium of Shepherd et al., modified without biotin, amino acids, low glucose concentration and with decreasing amounts of (NH4)2SO4, to determine the optimal growth requirement for this strain. All the experiments were carried out under sterile conditions at 25 degrees C in a thermostat with initial O.D.s (675 nm) of 0.500 and 0.100. Cell growth was generally monitored everyday for six days with a spectrophotometer by determining the absorbance of the cultures at 675 nm. All the experiments were repeated three times and a statistical analysis of the data with a probability of 99% and 1% of error was performed to confirm the validity of the results. Best growth was obtained with starved cells at an initial O.D. of 0.100 and with a 0.1 g/L concentration of (NH4)2SO4. At this concentration, the growth of C. albicans B 311-10 was best between the first and the fourth day with the maximum at the third day. With (NH4)2SO4 concentrations of 0.05 and 0.5 g/L, cell growth was the same.

Alpha subunit in gestational trophoblastic disease

Alpha HCG, estimated after evacuation of hydatidiform mole, was found to follow the decline of beta-HCG. In patients with low-risk non-metastatic gestational disease the alpha-subunit values also followed the beta-HCG values. This indicates no additional value in following alpha-HCG in such patients. Whether alpha-HCG elevation may portend recurrence in treated high-risk cases of metastatic disease in remission with non-detectable beta-HCG remains unresolved by this study.

Acid phosphatase production as an aid in rapid characterization of Candida species

Yeast isolates were tested for phosphatase activity, using p-nitrophenyl phosphate substrate. Several Candida species displayed characteristic patterns. The enzyme activity was absent in all except one of the 78 isolates of Candida albicans tested. C. tropicalis, C. pseudotropicalis, and C. krusei were the most consistent and active enzyme producers. Enzyme production by these yeasts was not influenced by the amount of growth or a decrease in the pH of the culture media. The characteristic enzyme activity of these three yeasts was demonstrated in the presence of concentrations of inorganic phosphate which inhibited the enzyme activity of other Candida species. The activity of isolates of C. gullermondii, C. parapsilosis, and C. stellatoidea was variable, although none of these were strong enzyme producers. It seems feasible to use phosphatase activity as a simple and rapid additional method for the separation of C. albicans from closely related species of the genus.