Optimal decision making for sperm chemotaxis in the presence of noise

For navigation, microscopic agents such as biological cells rely on noisy sensory input. In cells performing chemotaxis, such noise arises from the stochastic binding of signaling molecules at low concentrations. Using chemotaxis of sperm cells as application example, we address the classic problem of chemotaxis towards a single target. We reveal a fundamental relationship between the speed of chemotactic steering and the strength of directional fluctuations that result from the amplification of noise in the chemical input signal. This relation implies a trade-off between slow, but reliable, and fast, but less reliable, steering. By formulating the problem of optimal navigation in the presence of noise as a Markov decision process, we show that dynamic switching between reliable and fast steering substantially increases the probability to find a target, such as the egg. Intriguingly, this decision making would provide no benefit in the absence of noise. Instead, decision making is most beneficial, if chemical signals are above detection threshold, yet signal-to-noise ratios of gradient measurements are low. This situation generically arises at intermediate distances from a target, where signaling molecules emitted by the target are diluted, thus defining a `noise zone' that cells have to cross. Our work addresses the intermediate case between well-studied perfect chemotaxis at high signal-to-noise ratios close to a target, and random search strategies in the absence of navigation cues, e.g. far away from a target. Our specific results provide a rational for the surprising observation of decision making in recent experiments on sea urchin sperm chemotaxis. The general theory demonstrates how decision making enables chemotactic agents to cope with high levels of noise in gradient measurements by dynamically adjusting the persistence length of a biased persistent random walk.Comment: 9 pages, 5 figure

Timing Cellular Decision Making Under Noise via Cell–Cell Communication

Many cellular processes require decision making mechanisms, which must act reliably even in the unavoidable presence of substantial amounts of noise. However, the multistable genetic switches that underlie most decision-making processes are dominated by fluctuations that can induce random jumps between alternative cellular states. Here we show, via theoretical modeling of a population of noise-driven bistable genetic switches, that reliable timing of decision-making processes can be accomplished for large enough population sizes, as long as cells are globally coupled by chemical means. In the light of these results, we conjecture that cell proliferation, in the presence of cell–cell communication, could provide a mechanism for reliable decision making in the presence of noise, by triggering cellular transitions only when the whole cell population reaches a certain size. In other words, the summation performed by the cell population would average out the noise and reduce its detrimental impact

Timing cellular decision making under noise via cell-cell communication

Many cellular processes require decision making mechanisms, which must act reliably even in the unavoidable presence of substantial amounts of noise. However, the multistable genetic switches that underlie most decision-making processes are dominated by fluctuations that can induce random jumps between alternative cellular states. Here we show, via theoretical modeling of a population of noise-driven bistable genetic switches, that reliable timing of decision-making processes can be accomplished for large enough population sizes, as long as cells are globally coupled by chemical means. In the light of these results, we conjecture that cell proliferation, in the presence of cell-cell communication, could provide a mechanism for reliable decision making in the presence of noise, by triggering cellular transitions only when the whole cell population reaches a certain size. In other words, the summation performed by the cell population would average out the noise and reduce its detrimental impact

The effects of background music and sound in economic decision making: Evidence from a laboratory experiment

This paper experimentally studies the effects of background music and sound on the preference of the decision makers for rewards in pairwise intertemporal choice tasks and lottery choice tasks. The participants took part in the current experiment, involving four treatments: (1) the familiar music treatment; (2) the unfamiliar music treatment; (3) the noise treatment and (4) the no music treatment. The experimental results confirm that background noise affects human performance in decision making under risk and intertemporal decision making, though the results do not indicate the significant familiarity effect that is a change of the preference in the presence of familiar background music and sound.Allais-type preferences; choice under risk; intertemporal choice; the familiarity effect

Sensitivity of asymmetric rate-dependent critical systems to initial conditions: insights into cellular decision making

The work reported here aims to address the effects of time-dependent parameters and stochasticity on decision-making in biological systems. We achieve this by extending previous studies that resorted to simple normal forms. Yet, we focus primarily on the issue of the system's sensitivity to initial conditions in the presence of different noise distributions. In addition, we assess the impact of two-way sweeping through the critical region of a canonical Pitchfork bifurcation with a constant external asymmetry. The parallel with decision-making in bio-circuits is performed on this simple system since it is equivalent in its available states and dynamics to more complex genetic circuits. Overall, we verify that rate-dependent effects are specific to particular initial conditions. Information processing for each starting state is affected by the balance between sweeping speed through critical regions, and the type of fluctuations added. For a heavy-tail noise, forward-reverse dynamic bifurcations are more efficient in processing the information contained in external signals, when compared to the system relying on escape dynamics, if it starts at an attractor not favoured by the asymmetry and, in conjunction, if the sweeping amplitude is large

A control theory model for human decision making

The optimal control model for pilot-vehicle systems has been extended to handle certain types of human decision tasks. The model for decision making incorporates the observation noise, optimal estimation, and prediction concepts that form the basis of the model for control behavior. Experiments are described for the following task situations: (1) single decision tasks; (2) two decision tasks; and (3) simultaneous manual control and decision tasks. Using fixed values for model parameters, single-task and two-task decision performance scores to within an accuracy of 10 percent can be predicted. The experiment on simultaneous control and decision indicates the presence of task interference in this situation, but the results are not adequate to allow a conclusive test of the predictive capability of the model

A Study of Deep CNN Model with Labeling Noise Based on Granular-ball Computing

In supervised learning, the presence of noise can have a significant impact on decision making. Since many classifiers do not take label noise into account in the derivation of the loss function, including the loss functions of logistic regression, SVM, and AdaBoost, especially the AdaBoost iterative algorithm, whose core idea is to continuously increase the weight value of the misclassified samples, the weight of samples in many presence of label noise will be increased, leading to a decrease in model accuracy. In addition, the learning process of BP neural network and decision tree will also be affected by label noise. Therefore, solving the label noise problem is an important element of maintaining the robustness of the network model, which is of great practical significance. Granular ball computing is an important modeling method developed in the field of granular computing in recent years, which is an efficient, robust and scalable learning method. In this paper, we pioneered a granular ball neural network algorithm model, which adopts the idea of multi-granular to filter label noise samples during model training, solving the current problem of model instability caused by label noise in the field of deep learning, greatly reducing the proportion of label noise in training samples and improving the robustness of neural network models

Transient cognitive dynamics, metastability, and decision making

Transient Cognitive Dynamics, Metastability, and Decision Making. Rabinovich et al. PLoS Computational Biology. 2008. 4(5) doi:10.1371/journal.pcbi.1000072The idea that cognitive activity can be understood using nonlinear dynamics has been intensively discussed at length for the last 15 years. One of the popular points of view is that metastable states play a key role in the execution of cognitive functions. Experimental and modeling studies suggest that most of these functions are the result of transient activity of large-scale brain networks in the presence of noise. Such transients may consist of a sequential switching between different metastable cognitive states. The main problem faced when using dynamical theory to describe transient cognitive processes is the fundamental contradiction between reproducibility and flexibility of transient behavior. In this paper, we propose a theoretical description of transient cognitive dynamics based on the interaction of functionally dependent metastable cognitive states. The mathematical image of such transient activity is a stable heteroclinic channel, i.e., a set of trajectories in the vicinity of a heteroclinic skeleton that consists of saddles and unstable separatrices that connect their surroundings. We suggest a basic mathematical model, a strongly dissipative dynamical system, and formulate the conditions for the robustness and reproducibility of cognitive transients that satisfy the competing requirements for stability and flexibility. Based on this approach, we describe here an effective solution for the problem of sequential decision making, represented as a fixed time game: a player takes sequential actions in a changing noisy environment so as to maximize a cumulative reward. As we predict and verify in computer simulations, noise plays an important role in optimizing the gain.This work was supported by ONR N00014-07-1-0741. PV acknowledges support from Spanish BFU2006-07902/BFI and CAM S-SEM-0255-2006

Robust averaging protects decisions from noise in neural computations

An ideal observer will give equivalent weight to sources of information that are equally reliable. However, when averaging visual information, human observers tend to downweight or discount features that are relatively outlying or deviant (‘robust averaging’). Why humans adopt an integration policy that discards important decision information remains unknown. Here, observers were asked to judge the average tilt in a circular array of high-contrast gratings, relative to an orientation boundary defined by a central reference grating. Observers showed robust averaging of orientation, but the extent to which they did so was a positive predictor of their overall performance. Using computational simulations, we show that although robust averaging is suboptimal for a perfect integrator, it paradoxically enhances performance in the presence of “late” noise, i.e. which corrupts decisions during integration. In other words, robust decision strategies increase the brain’s resilience to noise arising in neural computations during decision-making