Membrane Resonance and Stochastic Resonance Modulate Firing Patterns of Thalamocortical Neurons

We examined the interactions of subthreshold membrane resonance and stochastic resonance using whole-cell patch clamp recordings in thalamocortical neurons of rat brain slices, as well as with a Hodgkin-Huxley-type mathematical model of thalamocortical neurons. The neurons exhibited the subthreshold resonance when stimulated with small amplitude sine wave currents of varying frequency, and stochastic resonance when noise was added to sine wave inputs. Stochastic resonance was manifest as a maximum in signal-to-noise ratio of output response to subthreshold periodic input combined with noise. Stochastic resonance in conjunction with subthreshold resonance resulted in action potential patterns that showed frequency selectivity for periodic inputs. Stochastic resonance was maximal near subthreshold resonance frequency and a high noise level was required for detection of high frequency signals. We speculate that combined membrane and stochastic resonances have physiological utility in coupling synaptic activity to preferred firing frequency and in network synchronization under noise.     

Slow Noise in the Period of a Biological Oscillator Underlies Gradual Trends and Abrupt Transitions in Phasic Relationships in Hybrid Neural Networks

In order to study the ability of coupled neural oscillators to synchronize in the presence of intrinsic as opposed to synaptic noise, we constructed hybrid circuits consisting of one biological and one computational model neuron with reciprocal synaptic inhibition using the dynamic clamp. Uncoupled, both neurons fired periodic trains of action potentials. Most coupled circuits exhibited qualitative changes between one-to-one phase-locking with fairly constant phasic relationships and phase slipping with a constant progression in the phasic relationships across cycles. The phase resetting curve (PRC) and intrinsic periods were measured for both neurons, and used to construct a map of the firing intervals for both the coupled and externally forced (PRC measurement) conditions. For the coupled network, a stable fixed point of the map predicted phase locking, and its absence produced phase slipping. Repetitive application of the map was used to calibrate different noise models to simultaneously fit the noise level in the measurement of the PRC and the dynamics of the hybrid circuit experiments. Only a noise model that added history-dependent variability to the intrinsic period could fit both data sets with the same parameter values, as well as capture bifurcations in the fixed points of the map that cause switching between slipping and locking. We conclude that the biological neurons in our study have slowly-fluctuating stochastic dynamics that confer history dependence on the period. Theoretical results to date on the behavior of ensembles of noisy biological oscillators may require re-evaluation to account for transitions induced by slow noise dynamics.     

Hierarchic Stochastic Modelling Applied to Intracellular Ca2+ Signals

Important biological processes like cell signalling and gene expression have noisy components and are very complex at the same time. Mathematical analysis of such systems has often been limited to the study of isolated subsystems, or approximations are used that are difficult to justify. Here we extend a recently published method (Thurley and Falcke, PNAS 2011) which is formulated in observable system configurations instead of molecular transitions. This reduces the number of system states by several orders of magnitude and avoids fitting of kinetic parameters. The method is applied to signalling. is a ubiquitous second messenger transmitting information by stochastic sequences of concentration spikes, which arise by coupling of subcellular release events (puffs). We derive analytical expressions for a mechanistic model, based on recent data from live cell imaging, and calculate spike statistics in dependence on cellular parameters like stimulus strength or number of channels. The new approach substantiates a generic model, which is a very convenient way to simulate spike sequences with correct spiking statistics.     

Facial Masculinity: How the Choice of Measurement Method Enables to Detect Its Influence on Behaviour

Recent research has explored the relationship between facial masculinity, human male behaviour and males'' perceived features (i.e. attractiveness). The methods of measurement of facial masculinity employed in the literature are quite diverse. In the present paper, we use several methods of measuring facial masculinity to study the effect of this feature on risk attitudes and trustworthiness. We employ two strategic interactions to measure these two traits, a first-price auction and a trust game. We find that facial width-to-height ratio is the best predictor of trustworthiness, and that measures of masculinity which use Geometric Morphometrics are the best suited to link masculinity and bidding behaviour. However, we observe that the link between masculinity and bidding in the first-price auction might be driven by competitiveness and not by risk aversion only. Finally, we test the relationship between facial measures of masculinity and perceived masculinity. As a conclusion, we suggest that researchers in the field should measure masculinity using one of these methods in order to obtain comparable results. We also encourage researchers to revise the existing literature on this topic following these measurement methods.     

Hearing Sensitivity to Shifts of Rippled-Spectrum Sound Signals in Masking Noise

The goal of the study was to enlarge knowledge of discrimination of complex sound signals by the auditory system in masking noise. For that, influence of masking noise on detection of shift of rippled spectrum was studied in normal listeners. The signal was a shift of ripple phase within a 0.5-oct wide rippled spectrum centered at 2 kHz. The ripples were frequency-proportional (throughout the band, ripple spacing was a constant proportion of the ripple center frequency). Simultaneous masker was a 0.5-oct noise below-, on-, or above the signal band. Both the low-frequency (center frequency 1 kHz) and on-frequency (the same center frequency as for the signal) maskers increased the thresholds for detecting ripple phase shift. However, the threshold dependence on the masker level was different for these two maskers. For the on-frequency masker, the masking effect primarily depended on the masker/signal ratio: the threshold steeply increased at a ratio of 5 dB, and no shift was detectable at a ratio of 10 dB. For the low-frequency masker, the masking effect primarily depended on the masker level: the threshold increased at a masker level of 80 dB SPL, and no shift was detectable at a masker level of 90 dB (for a signal level of 50 dB) or 100 dB (for a signal level of 80 dB). The high-frequency masker had little effect. The data were successfully simulated using an excitation-pattern model. In this model, the effect of the on-frequency masker appeared to be primarily due to a decrease of ripple depth. The effect of the low-frequency masker appeared due to widening of the auditory filters at high sound levels.     

Suppression of Enhanced Physiological Tremor via Stochastic Noise: Initial Observations

Enhanced physiological tremor is a disabling condition that arises because of unstable interactions between central tremor generators and the biomechanics of the spinal stretch reflex. Previous work has shown that peripheral input may push the tremor-related spinal and cortical systems closer to anti-phase firing, potentially leading to a reduction in tremor through phase cancellation. The aim of the present study was to investigate whether peripherally applied mechanical stochastic noise can attenuate enhanced physiological tremor and improve motor performance. Eight subjects with enhanced physiological tremor performed a visuomotor task requiring the right index finger to compensate a static force generated by a manipulandum to which Gaussian noise (3–35 Hz) was applied. The finger position was displayed on-line on a monitor as a small white dot which the subjects had to maintain in the center of a larger green circle. Electromyogram (EMG) from the active hand muscles and finger position were recorded. Performance was measured by the mean absolute deviation of the white dot from the zero position. Tremor was identified by the acceleration in the frequency range 7–12 Hz. Two different conditions were compared: with and without superimposed noise at optimal amplitude (determined at the beginning of the experiment). The application of optimum noise reduced tremor (accelerometric amplitude and EMG activity) and improved the motor performance (reduced mean absolute deviation from zero). These data provide the first evidence of a significant reduction of enhanced physiological tremor in the human sensorimotor system due to application of external stochastic noise.     

How Spike Synchronization Among Olfactory Neurons Can Contribute to Sensory Discrimination

Recent studies in honeybees have demonstrated that, when odor-evoked action potentials in antennal lobe neurons are pharmacologically desynchronized, the bees are impaired in their ability to discriminate chemically similar odor stimuli. Using a reduced computational model of the honeybee antennal lobe, we show how changes in spike-synchronization properties alone, independent of changes in overall spike-discharge rate or differences in activity levels among responsive neurons, can produce changes in associative learning similar to those observed experimentally.     

Prefrontal Neurons Encode Actions and Outcomes in Conjunction with Spatial Location in Rats Performing a Dynamic Delayed Non-Match to Position Task

To respond adaptively to change organisms must utilize information about recent events and environmental context to select actions that are likely to produce favorable outcomes. We developed a dynamic delayed nonmatching to position task to study the influence of spatial context on event-related activity of medial prefrontal cortex neurons during reinforcement-guided decision-making. We found neurons with responses related to preparation, movement, lever press responses, reinforcement, and memory delays. Combined event-related and video tracking analyses revealed variability in spatial tuning of neurons with similar event-related activity. While all correlated neurons exhibited spatial tuning broadly consistent with relevant task events, for instance reinforcement-related activity concentrated in locations where reinforcement was delivered, some had elevated activity in more specific locations, for instance reinforcement-related activity in one of several locations where reinforcement was delivered. Timing analyses revealed a limited set of distinct response types with activity time-locked to critical behavioral events that represent the temporal organization of dDNMTP trials. Our results suggest that reinforcement-guided decision-making emerges from discrete populations of medial prefrontal neurons that encode information related to planned or ongoing movements and actions and anticipated or actual action-outcomes in conjunction with information about spatial context.     

The Effect of Slow Electrical Stimuli to Achieve Learning in Cultured Networks of Rat Cortical Neurons

Learning, or more generally, plasticity may be studied using cultured networks of rat cortical neurons on multi electrode arrays. Several protocols have been proposed to affect connectivity in such networks. One of these protocols, proposed by Shahaf and Marom, aimed to train the input-output relationship of a selected connection in a network using slow electrical stimuli. Although the results were quite promising, the experiments appeared difficult to repeat and the training protocol did not serve as a basis for wider investigation yet. Here, we repeated their protocol, and compared our ‘learning curves’ to the original results. Although in some experiments the protocol did not seem to work, we found that on average, the protocol showed a significantly improved stimulus response indeed. Furthermore, the protocol always induced functional connectivity changes that were much larger than changes that occurred after a comparable period of random or no stimulation. Finally, our data shows that stimulation at a fixed electrode induces functional connectivity changes of similar magnitude as stimulation through randomly varied sites; both larger than spontaneous connectivity fluctuations. We concluded that slow electrical stimulation always induced functional connectivity changes, although uncontrolled. The magnitude of change increased when we applied the adaptive (closed-loop) training protocol. We hypothesize that networks develop an equilibrium between connectivity and activity. Induced connectivity changes depend on the combination of applied stimulus and initial connectivity. Plain stimuli may drive networks to the nearest equilibrium that accommodates this input, whereas adaptive stimulation may direct the space for exploration and force networks to a new balance, at a larger distance from the initial state.     

How Cells Feel: Stochastic Model for a Molecular Mechanosensor

Understanding mechanosensitivity (i.e., how cells sense the stiffness of their environment) is very important, yet there is a fundamental difficulty in understanding its mechanism: to measure an elastic modulus one requires two points of application of force—a measuring and a reference point. The cell in contact with substrate has only one (adhesion) point to work with, and thus a new method of measurement needs to be invented. The aim of this theoretical work is to develop a self-consistent physical model for mechanosensitivity, a process by which a cell detects the mechanical stiffness of its environment (e.g., a substrate it is attached to via adhesion points) and generates an appropriate chemical signaling to remodel itself in response to this environment. The model uses the molecular mechanosensing complex of latent TGF-β attached to the adhesion point as the biomarker. We show that the underlying Brownian motion in the substrate is the reference element in the measuring process. The model produces a closed expression for the rate of release of active TGF-β, which depends on the substrate stiffness and the pulling force coming from the cell in a subtle and nontrivial way. It is consistent with basic experimental data showing an increase in signal for stiffer substrates and higher pulling forces. In addition, we find that for each cell there is a range of stiffness where a homeostatic configuration of the cell can be achieved, outside of which the cell either relaxes its cytoskeletal forces and detaches from the very weak substrate, or generates an increasingly strong pulling force through stress fibers with a positive feedback loop on very stiff substrates. In this way, the theory offers the underlying mechanism for the myofibroblast conversion in wound healing and smooth muscle cell dysfunction in cardiac disease.     

Keeping Signals Straight: How Cells Process Information and Make Decisions

As we become increasingly dependent on electronic information-processing systems at home and work, it’s easy to lose sight of the fact that our very survival depends on highly complex biological information-processing systems. Each of the trillions of cells that form the human body has the ability to detect and respond to a wide range of stimuli and inputs, using an extraordinary set of signaling proteins to process this information and make decisions accordingly. Indeed, cells in all organisms rely on these signaling proteins to survive and proliferate in unpredictable and sometimes rapidly changing environments. But how exactly do these proteins relay information within cells, and how do they keep a multitude of incoming signals straight? Here, I describe recent efforts to understand the fidelity of information flow inside cells. This work is providing fundamental insight into how cells function. Additionally, it may lead to the design of novel antibiotics that disrupt the signaling of pathogenic bacteria or it could help to guide the treatment of cancer, which often involves information-processing gone awry inside human cells.There are nearly 10¹³ cells in every human, and at least as many bacterial cells living in or on us [1]. Each of these cells, human and bacterial, is a sophisticated, information-processing device. Cells have evolved the remarkable ability to appraise their internal and external environments and then to act on the information gathered. They can decide whether to stay where they are or crawl away, whether to grow or hunker down until conditions improve, whether to produce one enzyme or another, and so much more. The ability to make decisions at the cellular level is absolutely critical to the survival and long-term proliferation of organisms throughout the biosphere—but how do individual cells accomplish this feat without the luxury of a brain or nervous system? The answer lies with a diverse and important set of molecules found inside all cells called signal transduction proteins [2].These signaling proteins do not typically carry out a specific metabolic process on their own or directly participate in the growth or maintenance of cells. Instead, their job is to effectively keep tabs on the environment and respond to various cues or stimuli by activating (or inactivating) the appropriate cellular processes. Signaling proteins are, in effect, pulling the puppet strings that enable cells to survive, grow, and reproduce.The sequencing of genomes from many different species in the late 1990s and early 2000s offered the first comprehensive assessment of the arsenal of signaling proteins available to individual cells. The signaling proteins encoded by most organisms often number in the hundreds but typically belong to a small number of protein families. The individual members of a given family are sometimes highly related at the sequence and structural levels.In many ways, the observation that cells harbor only a small number of signaling protein families makes sense. Over the course of evolution, cells must expand and diversify their information-processing capabilities to respond to new environments and new signals. It is much easier for cells to duplicate and then modify an existing signaling modality than it is to create a brand new form of signaling protein from scratch. But the benefit of expanding an organism''s signaling repertoire through duplication comes at a significant cost: how do individual cells keep signals straight and avoid unwanted cross-talk? How is specificity ensured to maintain the fidelity of information flow inside cells?A reasonable analogy here is the telecommunication network we each rely on every day to interact with one another. For example, if I want to call my mother, I need some way to make sure my cell phone connects with her cell phone, without crossing lines or inadvertently calling someone else. This specificity is dictated by the unique phone number I enter. Is there an equivalent system, or code, used by signaling proteins to ensure their specificity?My lab set out to address this question many years ago in bacterial cells, which rely on so-called two-component signaling pathways to perform many of their most complex information-processing tasks [3,4]. These signaling pathways involve one protein, called a histidine kinase, that resides in the membrane surrounding a cell and "listens" to the environment (Fig 1). If a signal or stimulus registers on the extracellular portion of the protein, the intracellular portion of the histidine kinase protein responds by grabbing a phosphate from ATP and attaching it to a particular histidine amino acid, a process called autophosphorylation. The kinase then docks with a second protein, called a response regulator, and passes the phosphate group to it. This regulatory protein is subsequently released to effect cellular changes, often by turning on a battery of genes that help cells cope with the environmental change or stimulus originally detected by the kinase. But how does a kinase "know" which response regulator, or substrate, to dock with and signal to?Open in a separate windowFig 1Specificity of signaling pathways.Two pathways are shown. Each initiates with a sensor kinase (orange) that can sense an extracellular signal and respond by phosphorylating itself using ATP. The phosphate group from ATP (circled ''P'') can then be passed to a substrate (blue), typically a regulatory protein that can effect changes in cellular behavior. Critical to the fidelity of information flow through these pathways are a set of ''specificity residues'' on each protein that are matched such that a kinase signals only to the correct substrate.We showed that this choice, or partner specificity, is intrinsic to the kinase, meaning that the kinase has an innate ability to discriminate between the right partner and all other possible substrates, without relying on other factors inside cells [5]. This exquisite specificity is ultimately determined by a small number of amino-acid residues in the kinase located at positions in the protein near the phosphorylated histidine [6]. Each kinase has a unique set of residues at these key positions that enable it to interact exclusively with its partner, or cognate substrate, which contains a complementary set of residues (see Fig 1). Together, these paired residues (called specificity residues) in kinases and substrates form a code that ultimately ensures signals are transmitted properly inside cells.Why do we care how signals get passed inside bacteria? Although my own lab''s work on this topic is driven mainly by a curiosity about how bacteria process information, this work has several potential applications. First, it turns out that many bacterial pathogens rely on two-component signaling proteins to infect humans, so a deeper understanding of how these proteins work may enable the design of novel antibiotics that target them [7]. Like cyberattacks that seek to disrupt computer-based information networks, drugs that disrupt the information-processing of pathogenic bacteria could cripple them. Understanding the basis of signaling specificity may also enable efforts to rationally engineer bacteria as biosensors [8]. As already noted, bacteria use histidine kinases to sense and respond to a spectacular diversity of molecules and compounds in their environments. By understanding how they signal in response to these various stimuli, we can now reprogram these histidine kinases to respond in novel ways, e.g. by producing an indicator of signal detection, such as light or fluorescence.The intrinsic and exquisite specificity of signaling proteins is, by no means, exclusive to bacteria. Exciting recent work has revealed that human kinases are also highly selective, using a defined set of specificity residues to ensure that they only phosphorylate the right substrate(s) inside cells [9]. Disrupting or altering this specificity could, in some cases, have catastrophic consequences for humans. For example, some types of cancer involve mutations in the specificity residues of signal-transducing kinases [10]. These mutations may be wreaking havoc on the information-processing capabilities of cells, possibly contributing to the unregulated growth and proliferation that is a hallmark of cancer. Thus, a better understanding of how signaling proteins ensure the specificity of their interactions may provide routes to new diagnostics or therapeutic strategies for the treatment of cancer.Whether any of these applications in biosensing or the treatment of bacterial infections and cancer ever come to fruition remains to be seen. Regardless, future efforts in this area promise to reveal the fundamental basis of information-processing in individual cells, a phenomenon that ultimately underlies the success and diversity of almost all life on the planet.