The Fungal Fast Lane: Common Mycorrhizal Networks Extend Bioactive Zones of Allelochemicals in Soils

Allelopathy, a phenomenon where compounds produced by one plant limit the growth of surrounding plants, is a controversially discussed factor in plant-plant interactions with great significance for plant community structure. Common mycorrhizal networks (CMNs) form belowground networks that interconnect multiple plant species; yet these networks are typically ignored in studies of allelopathy. We tested the hypothesis that CMNs facilitate transport of allelochemicals from supplier to target plants, thereby affecting allelopathic interactions. We analyzed accumulation of a model allelopathic substance, the herbicide imazamox, and two allelopathic thiophenes released from Tagetes tenuifolia roots, by diffusion through soil and CMNs. We also conducted bioassays to determine how the accumulated substances affected plant growth. All compounds accumulated to greater levels in target soils with CMNs as opposed to soils without CMNs. This increased accumulation was associated with reduced growth of target plants in soils with CMNs. Our results show that CMNs support transfer of allelochemicals from supplier to target plants and thus lead to allelochemical accumulation at levels that could not be reached by diffusion through soil alone. We conclude that CMNs expand the bioactive zones of allelochemicals in natural environments, with significant implications for interspecies chemical interactions in plant communities

Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

Changes of synaptic connections between neurons are thought to be the physiological basis of learning. These changes can be gated by neuromodulators that encode the presence of reward. We study a family of reward-modulated synaptic learning rules for spiking neurons on a learning task in continuous space inspired by the Morris Water maze. The synaptic update rule modifies the release probability of synaptic transmission and depends on the timing of presynaptic spike arrival, postsynaptic action potentials, as well as the membrane potential of the postsynaptic neuron. The family of learning rules includes an optimal rule derived from policy gradient methods as well as reward modulated Hebbian learning. The synaptic update rule is implemented in a population of spiking neurons using a network architecture that combines feedforward input with lateral connections. Actions are represented by a population of hypothetical action cells with strong mexican-hat connectivity and are read out at theta frequency. We show that in this architecture, a standard policy gradient rule fails to solve the Morris watermaze task, whereas a variant with a Hebbian bias can learn the task within 20 trials, consistent with experiments. This result does not depend on implementation details such as the size of the neuronal populations. Our theoretical approach shows how learning new behaviors can be linked to reward-modulated plasticity at the level of single synapses and makes predictions about the voltage and spike-timing dependence of synaptic plasticity and the influence of neuromodulators such as dopamine. It is an important step towards connecting formal theories of reinforcement learning with neuronal and synaptic properties

Land‐use intensity and biodiversity effects on infiltration capacity and hydraulic conductivity of grassland soils in southern Germany

Evidence from experimental and established grasslands indicates that plant biodiversity can modify the water cycle. One suspected mechanism behind this is a higher infiltration capacity (ν$_{B}$) and hydraulic conductivity (K) of the soil on species-rich grasslands. However, in established and agriculturally managed grasslands, biodiversity effects cannot be studied independent of land-use effects. Therefore, we investigated in established grassland systems how land-use intensity and associated biodiversity of plants and soil animals affect νB and K at and close to saturation. On 50 grassland plots along a land-use intensity gradient in the Biodiversity Exploratory Schwäbische Alb, Germany, we measured νB with a hood infiltrometer at several matrix potentials and calculated the saturated and unsaturated K. We statistically analysed the relationship between ν$_{B}$ or K and land-use information (e.g., fertilising intensity), abiotic (e.g., soil texture) and biotic data (e.g., plant species richness, earthworm abundance). Land-use intensity decreased and plant species richness increased ν$_{B}$ and K, while the direction of the effects of soil animals was inconsistent. The effect of land-use intensity on ν$_{B}$ and K was mainly attributable to its negative effect on plant species richness. Our results demonstrate that plant species richness was a better predictor of ν$_{B}$ and K at and close to saturation than land-use intensity or soil physical properties in the established grassland systems of the Schwäbische Alb

Magnitude and Timing of Leaf Damage Affect Seed Production in a Natural Population of Arabidopsis thaliana (Brassicaceae)

Background: The effect of herbivory on plant fitness varies widely. Understanding the causes of this variation is of considerable interest because of its implications for plant population dynamics and trait evolution. We experimentally defoliated the annual herb Arabidopsis thaliana in a natural population in Sweden to test the hypotheses that (a) plant fitness decreases with increasing damage, (b) tolerance to defoliation is lower before flowering than during flowering, and (c) defoliation before flowering reduces number of seeds more strongly than defoliation during flowering, but the opposite is true for effects on seed size. Methodology/Principal Findings: In a first experiment, between 0 and 75% of the leaf area was removed in May from plants that flowered or were about to start flowering. In a second experiment, 0, 25%, or 50% of the leaf area was removed from plants on one of two occasions, in mid April when plants were either in the vegetative rosette or bolting stage, or in mid May when plants were flowering. In the first experiment, seed production was negatively related to leaf area removed, and at the highest damage level, also mean seed size was reduced. In the second experiment, removal of 50% of the leaf area reduced seed production by 60% among plants defoliated early in the season at the vegetative rosettes, and by 22% among plants defoliated early in the season at the bolting stage, but did not reduce seed output of plants defoliated one month later. No seasonal shift in the effect of defoliation on seed size was detected. Conclusions/Significance: The results show that leaf damage may reduce the fitness of A. thaliana, and suggest that in this population leaf herbivores feeding on plants before flowering should exert stronger selection on defence traits than those feeding on plants during flowering, given similar damage levels

Active Inference: A Process Theory

This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence—or minimizing variational free energy—we ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena. These include repetition suppression, mismatch negativity, violation responses, place-cell activity, phase precession, theta sequences, theta-gamma coupling, evidence accumulation, race-to-bound dynamics, and transfer of dopamine responses. Furthermore, the (approximately Bayes’ optimal) behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton’s principle of least action

Tibialis posterior in health and disease: a review of structure and function with specific reference to electromyographic studies

Tibialis posterior has a vital role during gait as the primary dynamic stabiliser of the medial longitudinal arch; however, the muscle and tendon are prone to dysfunction with several conditions. We present an overview of tibialis posterior muscle and tendon anatomy with images from cadaveric work on fresh frozen limbs and a review of current evidence that define normal and abnormal tibialis posterior muscle activation during gait. A video is available that demonstrates ultrasound guided intra-muscular insertion techniques for tibialis posterior electromyography

Democratic population decisions result in robust policy-gradient learning: A parametric study with GPU simulations

High performance computing on the Graphics Processing Unit (GPU) is an emerging field driven by the promise of high computational power at a low cost. However, GPU programming is a non-trivial task and moreover architectural limitations raise the question of whether investing effort in this direction may be worthwhile. In this work, we use GPU programming to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and investigate its ability to learn a simplified navigation task using a policy-gradient learning rule stemming from Reinforcement Learning. The purpose of this paper is twofold. First, we want to support the use of GPUs in the field of Computational Neuroscience. Second, using GPU computing power, we investigate the conditions under which the said architecture and learning rule demonstrate best performance. Our work indicates that networks featuring strong Mexican-Hat-shaped recurrent connections in the top layer, where decision making is governed by the formation of a stable activity bump in the neural population (a "non-democratic" mechanism), achieve mediocre learning results at best. In absence of recurrent connections, where all neurons "vote" independently ("democratic") for a decision via population vector readout, the task is generally learned better and more robustly. Our study would have been extremely difficult on a desktop computer without the use of GPU programming. We present the routines developed for this purpose and show that a speed improvement of 5x up to 42x is provided versus optimised Python code. The higher speed is achieved when we exploit the parallelism of the GPU in the search of learning parameters. This suggests that efficient GPU programming can significantly reduce the time needed for simulating networks of spiking neurons, particularly when multiple parameter configurations are investigated. © 2011 Richmond et al

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards