Cellular mechanisms for integral feedback in visually guided behavior

Sensory feedback is a ubiquitous feature of guidance systems in both animals and engineered vehicles. For example, a common strategy for moving along a straight path is to turn such that the measured rate of rotation is zero. This task can be accomplished by using a feedback signal that is proportional to the instantaneous value of the measured sensory signal. In such a system, the addition of an integral term depending on past values of the sensory input is needed to eliminate steady-state error [proportional-integral (PI) control]. However, the means by which nervous systems implement such a computation are poorly understood. Here, we show that the optomotor responses of flying Drosophila follow a time course consistent with temporal integration of horizontal motion input. To investigate the cellular basis of this effect, we performed whole-cell patch-clamp recordings from the set of identified visual interneurons [horizontal system (HS) cells] thought to control this reflex during tethered flight. At high stimulus speeds, HS cells exhibit steady-state responses during flight that are absent during quiescence, a state-dependent difference in physiology that is explained by changes in their presynaptic inputs. However, even during flight, the membrane potential of the large-field interneurons exhibits no evidence for integration that could explain the behavioral responses. However, using a genetically encoded indicator, we found that calcium accumulates in the terminals of the interneurons along a time course consistent with the behavior and propose that this accumulation provides a mechanism for temporal integration of sensory feedback consistent with PI control

Renormalization group and anomalous scaling in a simple model of passive scalar advection in compressible flow

Field theoretical renormalization group methods are applied to a simple model of a passive scalar quantity advected by the Gaussian non-solenoidal (``compressible'') velocity field with the covariance $\propto\delta(t-t')| x-x'|^{\epsilon}$. Convective range anomalous scaling for the structure functions and various pair correlators is established, and the corresponding anomalous exponents are calculated to the order $\epsilon^2$ of the $\epsilon$ expansion. These exponents are non-universal, as a result of the degeneracy of the RG fixed point. In contrast to the case of a purely solenoidal velocity field (Obukhov--Kraichnan model), the correlation functions in the case at hand exhibit nontrivial dependence on both the IR and UV characteristic scales, and the anomalous scaling appears already at the level of the pair correlator. The powers of the scalar field without derivatives, whose critical dimensions determine the anomalous exponents, exhibit multifractal behaviour. The exact solution for the pair correlator is obtained; it is in agreement with the result obtained within the $\epsilon$ expansion. The anomalous exponents for passively advected magnetic fields are also presented in the first order of the $\epsilon$ expansion.Comment: 31 pages, REVTEX file. More detailed discussion of the one-dimensional case and comparison to the previous paper [20] are given; references updated. Results and formulas unchange

Exact Resummations in the Theory of Hydrodynamic Turbulence: III. Scenarios for Anomalous Scaling and Intermittency

Elements of the analytic structure of anomalous scaling and intermittency in fully developed hydrodynamic turbulence are described. We focus here on the structure functions of velocity differences that satisfy inertial range scaling laws $S_n(R)\sim R^{\zeta_n}$, and the correlation of energy dissipation $K_{\epsilon\epsilon}(R) \sim R^{-\mu}$. The goal is to understand the exponents $\zeta_n$ and $\mu$ from first principles. In paper II of this series it was shown that the existence of an ultraviolet scale (the dissipation scale $\eta$) is associated with a spectrum of anomalous exponents that characterize the ultraviolet divergences of correlations of gradient fields. The leading scaling exponent in this family was denoted $\Delta$. The exact resummation of ladder diagrams resulted in the calculation of $\Delta$ which satisfies the scaling relation $\Delta=2-\zeta_2$. In this paper we continue our analysis and show that nonperturbative effects may introduce multiscaling (i.e. $\zeta_n$ not being linear in $n$) with the renormalization scale being the infrared outer scale of turbulence $L$. It is shown that deviations from K41 scaling of $S_n(R)$ ($\zeta_n\neq n/3$) must appear if the correlation of dissipation is mixing (i.e. $\mu>0$). We derive an exact scaling relation $\mu = 2\zeta_2-\zeta_4$. We present analytic expressions for $\zeta_n$ for all $n$ and discuss their relation to experimental data. One surprising prediction is that the time decay constant $\tau_n(R)\propto R^{z_n}$ of $S_n(R)$ scales independently of $n$: the dynamic scaling exponent $z_n$ is the same for all $n$-order quantities, $z_n=\zeta_2$.Comment: PRE submitted, 22 pages + 11 figures, REVTeX. The Eps files of figures will be FTPed by request to [email protected]

Adaptive Filtering Enhances Information Transmission in Visual Cortex

Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain's coding strategy depend on the stimulus ensemble, we apply a new information-theoretic method that allows unbiased calculation of neural filters (receptive fields) from responses to natural scenes or other complex signals with strong multipoint correlations. In the cat primary visual cortex we compare responses to natural inputs with those to noise inputs matched for luminance and contrast. We find that neural filters adaptively change with the input ensemble so as to increase the information carried by the neural response about the filtered stimulus. Adaptation affects the spatial frequency composition of the filter, enhancing sensitivity to under-represented frequencies in agreement with optimal encoding arguments. Adaptation occurs over 40 s to many minutes, longer than most previously reported forms of adaptation.Comment: 20 pages, 11 figures, includes supplementary informatio

Network adaptation improves temporal representation of naturalistic stimuli in drosophila eye: II Mechanisms

Retinal networks must adapt constantly to best present the ever changing visual world to the brain. Here we test the hypothesis that adaptation is a result of different mechanisms at several synaptic connections within the network. In a companion paper (Part I), we showed that adaptation in the photoreceptors (R1-R6) and large monopolar cells (LMC) of the Drosophila eye improves sensitivity to under-represented signals in seconds by enhancing both the amplitude and frequency distribution of LMCs' voltage responses to repeated naturalistic contrast series. In this paper, we show that such adaptation needs both the light-mediated conductance and feedback-mediated synaptic conductance. A faulty feedforward pathway in histamine receptor mutant flies speeds up the LMC output, mimicking extreme light adaptation. A faulty feedback pathway from L2 LMCs to photoreceptors slows down the LMC output, mimicking dark adaptation. These results underline the importance of network adaptation for efficient coding, and as a mechanism for selectively regulating the size and speed of signals in neurons. We suggest that concert action of many different mechanisms and neural connections are responsible for adaptation to visual stimuli. Further, our results demonstrate the need for detailed circuit reconstructions like that of the Drosophila lamina, to understand how networks process information

Particles and fields in fluid turbulence

The understanding of fluid turbulence has considerably progressed in recent years. The application of the methods of statistical mechanics to the description of the motion of fluid particles, i.e. to the Lagrangian dynamics, has led to a new quantitative theory of intermittency in turbulent transport. The first analytical description of anomalous scaling laws in turbulence has been obtained. The underlying physical mechanism reveals the role of statistical integrals of motion in non-equilibrium systems. For turbulent transport, the statistical conservation laws are hidden in the evolution of groups of fluid particles and arise from the competition between the expansion of a group and the change of its geometry. By breaking the scale-invariance symmetry, the statistically conserved quantities lead to the observed anomalous scaling of transported fields. Lagrangian methods also shed new light on some practical issues, such as mixing and turbulent magnetic dynamo.Comment: 165 pages, review article for Rev. Mod. Phy

Multifactorial falls prevention programmes for older adults presenting to the Emergency Department with a fall: systematic review and meta-analysis.

Background: Falls are a leading cause of emergency department (ED) presentations in older adults. Objective: To determine whether multifactorial falls prevention interventions are effective in preventing falls, fall injuries, ED re-presentations and hospital admissions in older adults presenting to the ED with a fall. Design: Systematic review and meta-analyses of randomised control trials (RCTs). Methods: Four health-related electronic databases were searched (inception to June 2018) with two independent reviewers determining inclusion, assessing study quality and undertaking data extraction. Study selection: RCTs of multifactorial falls prevention interventions targeting community dwelling older adults (≥ 60 years) presenting to the ED with a fall and providing quantitative data on at least one of the review outcomes. Results: Twelve studies involving 3,986 participants, from six countries, were eligible for inclusion. Studies were of variable methodological quality. The multifactorial interventions were heterogeneous, though the majority included components such as education, referral to relevant healthcare services, home modifications, exercise, and medication changes. Meta-analyses demonstrated a non-significant reduction in falls (rate ratio=0.78; 95% CI 0.58, 1.05) with multi-factorial falls prevention programs. Multi-factorial interventions did not significantly affect the number of fallers (risk ratio=1.02; 95% CI 0.88, 1.18), rate of fractured neck of femur (risk ratio=0.82; 95% CI 0.53, 1.25), fall-related ED presentations (rate ratio=0.99; 95% CI 0.84, 1.16), or hospitalisations (rate ratio=1.14; 95% CI 0.69, 1.89). Conclusions: There is insufficient evidence to support the use of multifactorial falls interventions to prevent falls or hospital utilisation in older people presenting to ED following a fall. Further research targeting this population group is required

Second Order Dimensionality Reduction Using Minimum and Maximum Mutual Information Models

Conventional methods used to characterize multidimensional neural feature selectivity, such as spike-triggered covariance (STC) or maximally informative dimensions (MID), are limited to Gaussian stimuli or are only able to identify a small number of features due to the curse of dimensionality. To overcome these issues, we propose two new dimensionality reduction methods that use minimum and maximum information models. These methods are information theoretic extensions of STC that can be used with non-Gaussian stimulus distributions to find relevant linear subspaces of arbitrary dimensionality. We compare these new methods to the conventional methods in two ways: with biologically-inspired simulated neurons responding to natural images and with recordings from macaque retinal and thalamic cells responding to naturalistic time-varying stimuli. With non-Gaussian stimuli, the minimum and maximum information methods significantly outperform STC in all cases, whereas MID performs best in the regime of low dimensional feature spaces

Adaptation and Selective Information Transmission in the Cricket Auditory Neuron AN2

Sensory systems adapt their neural code to changes in the sensory environment, often on multiple time scales. Here, we report a new form of adaptation in a first-order auditory interneuron (AN2) of crickets. We characterize the response of the AN2 neuron to amplitude-modulated sound stimuli and find that adaptation shifts the stimulus–response curves toward higher stimulus intensities, with a time constant of 1.5 s for adaptation and recovery. The spike responses were thus reduced for low-intensity sounds. We then address the question whether adaptation leads to an improvement of the signal's representation and compare the experimental results with the predictions of two competing hypotheses: infomax, which predicts that information conveyed about the entire signal range should be maximized, and selective coding, which predicts that “foreground” signals should be enhanced while “background” signals should be selectively suppressed. We test how adaptation changes the input–response curve when presenting signals with two or three peaks in their amplitude distributions, for which selective coding and infomax predict conflicting changes. By means of Bayesian data analysis, we quantify the shifts of the measured response curves and also find a slight reduction of their slopes. These decreases in slopes are smaller, and the absolute response thresholds are higher than those predicted by infomax. Most remarkably, and in contrast to the infomax principle, adaptation actually reduces the amount of encoded information when considering the whole range of input signals. The response curve changes are also not consistent with the selective coding hypothesis, because the amount of information conveyed about the loudest part of the signal does not increase as predicted but remains nearly constant. Less information is transmitted about signals with lower intensity