Role of the neurotrophic factors BDNF and NGF in the commitment of pluripotent neural crest cells.

Since trophic factors are increasingly recognized as playing a role in some decision-making steps during development, the influence of brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) on the commitment of pluripotent neural crest cells was investigated by in vitro clonal analysis. BDNF caused an increase of up to 21-fold in the number of sensory neuron precursors per colony without a corresponding increase in the total number of cells. By contrast, BDNF treatment caused an equivalent decrease in the number of undifferentiated cells per colony. The data suggest that BDNF, but not NGF, directs pluripotent neural crest cells to differentiate along the primary sensory neuron lineage.     

Sensory adaptation

Adaptation occurs in a variety of forms in all sensory systems, motivating the question: what is its purpose? A productive approach has been to hypothesize that adaptation helps neural systems to efficiently encode stimuli whose statistics vary in time. To encode efficiently, a neural system must change its coding strategy, or computation, as the distribution of stimuli changes. Information theoretic methods allow this efficient coding hypothesis to be tested quantitatively. Empirically, adaptive processes occur over a wide range of timescales. On short timescales, underlying mechanisms include the contribution of intrinsic nonlinearities. Over longer timescales, adaptation is often power-law-like, implying the coexistence of multiple timescales in a single adaptive process. Models demonstrate that this can result from mechanisms within a single neuron.     

Neurobiological models of two-choice decision making can be reduced to a one-dimensional nonlinear diffusion equation

The response behaviors in many two-alternative choice tasks are well described by so-called sequential sampling models. In these models, the evidence for each one of the two alternatives accumulates over time until it reaches a threshold, at which point a response is made. At the neurophysiological level, single neuron data recorded while monkeys are engaged in two-alternative choice tasks are well described by winner-take-all network models in which the two choices are represented in the firing rates of separate populations of neurons. Here, we show that such nonlinear network models can generally be reduced to a one-dimensional nonlinear diffusion equation, which bears functional resemblance to standard sequential sampling models of behavior. This reduction gives the functional dependence of performance and reaction-times on external inputs in the original system, irrespective of the system details. What is more, the nonlinear diffusion equation can provide excellent fits to behavioral data from two-choice decision making tasks by varying these external inputs. This suggests that changes in behavior under various experimental conditions, e.g. changes in stimulus coherence or response deadline, are driven by internal modulation of afferent inputs to putative decision making circuits in the brain. For certain model systems one can analytically derive the nonlinear diffusion equation, thereby mapping the original system parameters onto the diffusion equation coefficients. Here, we illustrate this with three model systems including coupled rate equations and a network of spiking neurons.     

Modeling convergent ON and OFF pathways in the early visual system

For understanding the computation and function of single neurons in sensory systems, one needs to investigate how sensory stimuli are related to a neuron’s response and which biological mechanisms underlie this relationship. Mathematical models of the stimulus–response relationship have proved very useful in approaching these issues in a systematic, quantitative way. A starting point for many such analyses has been provided by phenomenological “linear–nonlinear” (LN) models, which comprise a linear filter followed by a static nonlinear transformation. The linear filter is often associated with the neuron’s receptive field. However, the structure of the receptive field is generally a result of inputs from many presynaptic neurons, which may form parallel signal processing pathways. In the retina, for example, certain ganglion cells receive excitatory inputs from ON-type as well as OFF-type bipolar cells. Recent experiments have shown that the convergence of these pathways leads to intriguing response characteristics that cannot be captured by a single linear filter. One approach to adjust the LN model to the biological circuit structure is to use multiple parallel filters that capture ON and OFF bipolar inputs. Here, we review these new developments in modeling neuronal responses in the early visual system and provide details about one particular technique for obtaining the required sets of parallel filters from experimental data.     

White-noise analysis of nonlinear behavior in an insect sensory neuron: Kernel and cascade approaches

A functional expansion was used to model the relationship between a Gaussian white noise stimulus current and the resulting action potential output in the single sensory neuron of the cockroach femoral tactile spine. A new precise procedure was used to measure the kernels of the functional expansion. Very similar kernel estimates were obtained from separate sections of the data produced by the same neuron with the same input noise power level, although some small time-varying effects were detectable in moving through the data. Similar kernel estimates were measured using different input noise power levels for a given cell, or when comparing different cells under similar stimulus conditions. The kernels were used to identify a model for sensory encoding in the neuron, comprising a cascade of dynamic linear, static nonlinear, and dynamic linear elements. Only a single slice of the estimated experimental second-order kernel was used in identifying the cascade model. However, the complete second-order kernel of the cascade model closely resembled the estimated experimental kernel. Moreover, the model could closely predict the experimental action potential train obtained with novel white noise inputs.     

The evolution of fidelity in sensory systems

We investigate the effect that noise has on the evolution of measurement strategies and competition in populations of organisms with sensory systems of differing fidelities. We address two questions motivated by experimental and theoretical work on sensory systems in noisy environments: (1) How complex must a sensory system be in order to face the need to develop adaptive measurement strategies that change depending on the noise level? (2) Does the principle of competitive exclusion for sensory systems force one population to win out over all others? We find that the answer to the first question is that even very simple sensory systems will need to change measurement strategies depending on the amount of noise in the environment. Interestingly, the answer to the second question is that, in general, at most two populations with different fidelity sensory systems may co-exist within a single environment.     

Tuning curves, neuronal variability, and sensory coding

Tuning curves are widely used to characterize the responses of sensory neurons to external stimuli, but there is an ongoing debate as to their role in sensory processing. Commonly, it is assumed that a neuron's role is to encode the stimulus at the tuning curve peak, because high firing rates are the neuron's most distinct responses. In contrast, many theoretical and empirical studies have noted that nearby stimuli are most easily discriminated in high-slope regions of the tuning curve. Here, we demonstrate that both intuitions are correct, but that their relative importance depends on the experimental context and the level of variability in the neuronal response. Using three different information-based measures of encoding applied to experimentally measured sensory neurons, we show how the best-encoded stimulus can transition from high-slope to high-firing-rate regions of the tuning curve with increasing noise level. We further show that our results are consistent with recent experimental findings that correlate neuronal sensitivities with perception and behavior. This study illustrates the importance of the noise level in determining the encoding properties of sensory neurons and provides a unified framework for interpreting how the tuning curve and neuronal variability relate to the overall role of the neuron in sensory encoding.     

Data transmission between neurons

Summary By modulating sinusoidally the impulse frequency of the inhibitory input to the slowly adapting stretch receptor of Crustacea, the impulse frequency of the sensory neuron becomes sinusoidally modulated. The systems involved are linear for amplitudes of modulation up to 70% of the steady state frequency. The transfer function is given and some of its implications are discussed.     

Spike correlations in a songbird agree with a simple markov population model

The relationships between neural activity at the single-cell and the population levels are of central importance for understanding neural codes. In many sensory systems, collective behaviors in large cell groups can be described by pairwise spike correlations. Here, we test whether in a highly specialized premotor system of songbirds, pairwise spike correlations themselves can be seen as a simple corollary of an underlying random process. We test hypotheses on connectivity and network dynamics in the motor pathway of zebra finches using a high-level population model that is independent of detailed single-neuron properties. We assume that neural population activity evolves along a finite set of states during singing, and that during sleep population activity randomly switches back and forth between song states and a single resting state. Individual spike trains are generated by associating with each of the population states a particular firing mode, such as bursting or tonic firing. With an overall modification of one or two simple control parameters, the Markov model is able to reproduce observed firing statistics and spike correlations in different neuron types and behavioral states. Our results suggest that song- and sleep-related firing patterns are identical on short time scales and result from random sampling of a unique underlying theme. The efficiency of our population model may apply also to other neural systems in which population hypotheses can be tested on recordings from small neuron groups.     

Modeling the effects of varying data quality on trend detection in environmental monitoring

Detection of changes in ecosystem characteristics is a principal tool for identifying and understanding the effects of anthropogenic activities on the condition and functioning of ecosystems. It is widely known that temporal trends can be blurred by the imprecision of the data. Research program managers are aware of the difficulties surrounding representative sampling and therefore enforce strict sampling protocols. Standardized sampling can be so effective that the initially much smaller uncertainty in the instrumental analysis becomes substantial. However, until now the effect of the quality of the instrumental analysis on the time required for trend detection has only rarely been quantified. In this paper, we present a novel technique and theoretical computations for the detection of trends in single and combined indices. The theory is clarified with examples from the International Co-operative Programme on Assessment and Monitoring of Air Pollution on Forests (ICP Forests). Moreover, the theoretical computations were made for normalized or scaled distributions and are therefore equally valid outside the field of environmental monitoring. The results show that, when sampling protocols largely reduce the variability of representative sampling, poor quality of the instrumental analysis blurs the data such that environmental monitoring or long-term ecological research programs can lose the ability to detect trends by causing up to three decades-long delay in detecting changes. We can thus conclude that high quality of the instrumental analysis is a prerequisite for a sensitive monitoring program.     

Anatomical Organization of Multiple Modulatory Inputs in a Rhythmic Motor System

In rhythmic motor systems, descending projection neuron inputs elicit distinct outputs from their target central pattern generator (CPG) circuits. Projection neuron activity is regulated by sensory inputs and inputs from other regions of the nervous system, relaying information about the current status of an organism. To gain insight into the organization of multiple inputs targeting a projection neuron, we used the identified neuron MCN1 in the stomatogastric nervous system of the crab, Cancer borealis. MCN1 originates in the commissural ganglion and projects to the stomatogastric ganglion (STG). MCN1 activity is differentially regulated by multiple inputs including neuroendocrine (POC) and proprioceptive (GPR) neurons, to elicit distinct outputs from CPG circuits in the STG. We asked whether these defined inputs are compact and spatially segregated or dispersed and overlapping relative to their target projection neuron. Immunocytochemical labeling, intracellular dye injection and three-dimensional (3D) confocal microscopy revealed overlap of MCN1 neurites and POC and GPR terminals. The POC neuron terminals form a defined neuroendocrine organ (anterior commissural organ: ACO) that utilizes peptidergic paracrine signaling to act on MCN1. The MCN1 arborization consistently coincided with the ACO structure, despite morphological variation between preparations. Contrary to a previous 2D study, our 3D analysis revealed that GPR axons did not terminate in a compact bundle, but arborized more extensively near MCN1, arguing against sparse connectivity of GPR onto MCN1. Consistent innervation patterns suggest that integration of the sensory GPR and peptidergic POC inputs occur through more distributed and more tightly constrained anatomical interactions with their common modulatory projection neuron target than anticipated.     

Chemosensory-induced motor behaviors in fish

Chemical sensory signals play a crucial role in eliciting motor behaviors. We now review the different motor behaviors induced by chemosensory stimuli in fish as well as their neural substrate. A great deal of research has focused on migratory, reproductive, foraging, and escape behaviors but it is only recently that the molecules mediating these chemotactic responses have become well-characterized. Chemotactic responses are mediated by three sensory systems: olfactory, gustatory, and diffuse chemosensory. The olfactory sensory neuron responses to chemicals are now better understood. In addition, the olfactory projections to the central nervous system were recently shown to display an odotopic organization in the forebrain. Moreover, a specific downward projection underlying motor responses to olfactory inputs was recently described.     

Periodic orbits: a new language for neuronal dynamics.

A new nonlinear dynamical analysis is applied to complex behavior from neuronal systems. The conceptual foundation of this analysis is the abstraction of observed neuronal activities into a dynamical landscape characterized by a hierarchy of "unstable periodic orbits" (UPOs). UPOs are rigorously identified in data sets representative of three different levels of organization in mammalian brain. An analysis based on UPOs affords a novel alternative method of decoding, predicting, and controlling these neuronal systems.     

Altered sensory projections in the chick hind limb following the early removal of motoneurons

Chick sensory neurons grow to their correct targets in the hindlimb from the outset during normal development and following various experimental manipulations. This may result not because sensory neurons respond to specific limb-derived cues, but because they interact in some way with motoneurons which are responsive to such cues. To test this possibility, we removed the ventral part of the neural tube, which contains motoneurons and their precursors, at stages 16 1/2-20 1/2 and later examined the pathways sensory neurons had taken within the limb. Muscle nerves generally were missing or were reduced in diameter beyond the extent expected simply from the absence of motoneuron axons. In many cases, cutaneous nerves were enlarged, presumably due to the addition of other sensory axons. This result suggests that, in the absence of motoneurons, sensory neurons that normally project to muscles are unable to do so and may instead project along cutaneous pathways. Sensory axons from different segments also crossed less extensively in the plexus region than they did in control embryos, suggesting that alterations in their trajectories may normally be facilitated by similar changes in motoneuron pathways. Thus, motoneurons greatly enhance sensory neuron growth to muscles and contribute significantly toward the achievement of the normal sensory projection pattern. Sensory axons may fasciculate with motoneuron axons, or motoneuron axons may provide an aligned substrate for sensory neurons to grow along. Alternatively, motoneuron axons may alter the environment, thereby making certain pathways in the limb permissive for sensory neuron growth.     

Evolution of a polymodal sensory response network

Background  

Avoidance of noxious stimuli is essential for the survival of an animal in its natural habitat. Some avoidance responses require polymodal sensory neurons, which sense a range of diverse stimuli, whereas other stimuli require a unimodal sensory neuron, which senses a single stimulus. Polymodality might have evolved to help animals quickly detect and respond to diverse noxious stimuli. Nematodes inhabit diverse habitats and most nematode nervous systems are composed of a small number of neurons, despite a wide assortment in nematode sizes. Given this observation, we speculated that cellular contribution to stereotyped avoidance behaviors would also be conserved between nematode species. The ASH neuron mediates avoidance of three classes of noxious stimuli in Caenorhabditis elegans. Two species of parasitic nematodes also utilize the ASH neuron to avoid certain stimuli. We wanted to extend our knowledge of avoidance behaviors by comparing multiple stimuli in a set of free-living nematode species.     

Circuit Motifs for Contrast-Adaptive Differentiation in Early Sensory Systems: The Role of Presynaptic Inhibition and Short-Term Plasticity

In natural signals, such as the luminance value across of a visual scene, abrupt changes in intensity value are often more relevant to an organism than intensity values at other positions and times. Thus to reduce redundancy, sensory systems are specialized to detect the times and amplitudes of informative abrupt changes in the input stream rather than coding the intensity values at all times. In theory, a system that responds transiently to fast changes is called a differentiator. In principle, several different neural circuit mechanisms exist that are capable of responding transiently to abrupt input changes. However, it is unclear which circuit would be best suited for early sensory systems, where the dynamic range of the natural input signals can be very wide. We here compare the properties of different simple neural circuit motifs for implementing signal differentiation. We found that a circuit motif based on presynaptic inhibition (PI) is unique in a sense that the vesicle resources in the presynaptic site can be stably maintained over a wide range of stimulus intensities, making PI a biophysically plausible mechanism to implement a differentiator with a very wide dynamical range. Moreover, by additionally considering short-term plasticity (STP), differentiation becomes contrast adaptive in the PI-circuit but not in other potential neural circuit motifs. Numerical simulations show that the behavior of the adaptive PI-circuit is consistent with experimental observations suggesting that adaptive presynaptic inhibition might be a good candidate neural mechanism to achieve differentiation in early sensory systems.