Motor-maps, navigation and implicit space representation in the hippocampus

Multiple sensory-motor maps located in the brainstem and the cortex are involved in spatial orientation. Guiding movements of eyes, head, neck and arms they provide an approximately linear relation between target distance and motor response. This involves especially the superior colliculus in the brainstem and the parietal cortex. There, the natural frame of reference follows from the retinal representation of the environment. A model of navigation is presented that is based on the modulation of activity in those sensory-motor maps. The actual mechanism chosen was gain-field modulation, a process of multimodal integration that has been demonstrated in the parietal cortex and superior colliculus, and was implemented as attraction to visual cues (colour). Dependent on the metric of the sensory-motor map, the relative attraction to these cues implemented as gain field modulation and their position define a fixed point attractor on the plane for locomotive behaviour. The actual implementation used Kohonen-networks in a variant of reinforcement learning that are well suited to generate such topographically organized sensory-motor maps with roughly linear visuo-motor response characteristics. In the following, it was investigated how such an implicit coding of target positions by gain-field parameters might be represented in the hippocampus formation and under what conditions a direction-invariant space representation can arise from such retinotopic representations of multiple cues. Information about the orientation in the plane—as could be provided by head direction cells—appeared to be necessary for unambiguous space representation in our model in agreement with physiological experiments. With this information, Gauss-shaped “place-cells” could be generated, however, the representation of the spatial environment was repetitive and clustered and single cells were always tuned to the gain-field parameters as well     

Learning cortical topography from spatiotemporal stimuli

Stimulus representation is a functional interpretation of early sensory cortices. Early sensory cortices are subject to stimulus-induced modifications. Common models for stimulus-induced learning within topographic representations are based on the stimuli's spatial structure and probability distribution. Furthermore, we argue that average temporal stimulus distances reflect the stimuli's relatedness. As topographic representations reflect the stimuli's relatedness, the temporal structure of incoming stimuli is important for the learning in cortical maps. Motivated by recent neurobiological findings, we present an approach of cortical self-organization that additionally takes temporal stimulus aspects into account. The proposed model transforms average interstimulus intervals into representational distances. Thereby, neural topography is related to stimulus dynamics. This offers a new time-based interpretation of cortical maps. Our approach is based on a wave-like spread of cortical activity. Interactions between dynamics and feedforward activations lead to shifts of neural activity. The psychophysical saltation phenomenon may represent an analogue to the shifts proposed here. With regard to cortical plasticity, we offer an explanation for neurobiological findings that other models cannot explain. Moreover, we predict cortical reorganizations under new experimental, spatiotemporal conditions. With regard to psychophysics, we relate the saltation phenomenon to dynamics and interaction in early sensory cortices and predict further effects in the perception of spatiotemporal stimuli. Received: 17 March 1999 / Accepted in revised form: 10 August 1999     

Whisker-related circuitry in the trigeminal nucleus principalis: Topographic precision

Abstract

Single whiskers are topographically represented in the trigeminal (V) nucleus principalis (PrV) by a set of cylindrical aggregates of primary afferent terminals and somata (barrelettes). This isomorphic pattern is transmitted to the thalamus and barrel cortex. However, it is not known if terminals in PrV from neighboring whiskers interdigitate so as to violate rules of spatial parcellation predicted by barrelette borders; nor is it known the extent to which higher order inputs are topographic. The existence of inter-whisker arbor overlap or diffuse higher order inputs would demand additional theoretical principles to account for single whisker dominance in PrV cell responses. In adult rats, first, primary afferent pairs responding to the same or neighboring whiskers and injected with Neurobiotin or horseradish peroxidase were rendered brown or black to color-code their terminal boutons. When collaterals from both fibers appeared in the same topographic plane through PrV, the percentage of the summed area of the two arbor envelopes that overlapped was computed. For same-whisker pairs, overlap was 5?±?6% (mean?±?SD). For within-row neighbors, overlap was 2?±?5%. For between-row neighbors, overlap was 1?±?4%. Second, the areas of whisker primary afferent arbors and their corresponding barrelettes in the PrV were compared. In the transverse plane, arbor envelopes significantly exceeded the areas of cytochrome oxidase-stained barrelettes; arbors often extended into neighboring barrelettes. Third, bulk tracing of the projections from the spinal V subnucleus interpolaris (SpVi) to the PrV revealed strict topography such that they connect same-whisker barrelettes in the SpVi and PrV. Thus, whisker primary afferents do not exclusively project to their corresponding PrV barrelette, whereas higher order SpVi inputs to the PrV are precisely topographic.     

Learning through maps: functional significance of topographic organization in primary sensory cortex.

The presence of "maps" in sensory cortex is a hallmark of the mammalian nervous system, but the functional significance of topographic organization has been called into question by physiological studies claiming that patterns of neural behavioral activity transcend topographic boundaries. This paper discusses recent behavioral and physiological studies suggesting that, when animals or human subjects learn perceptual tasks, the neural modifications associated with the learning are distributed according to the spatial arrangement of the primary sensory cortical map. Topographical cortical representations of sensory events, therefore, appear to constitute a true structural framework for information processing and plasticity.     

The effect of intracortical competition on the formation of topographic maps in models of Hebbian learning

Correlation-based learning (CBL) models and self-organizing maps (SOM) are two classes of Hebbian models that have both been proposed to explain the activity-driven formation of cortical maps. Both models differ significantly in the way lateral cortical interactions are treated, leading to different predictions for the formation of receptive fields. The linear CBL models predict that receptive field profiles are determined by the average values and the spatial correlations of the second order of the afferent activity patterns, wheras SOM models map stimulus features. Here, we investigate a class of models which are characterized by a variable degree of lateral competition and which have the CBL and SOM models as limit cases. We show that there exists a critical value for intracortical competition below which the model exhibits CBL properties and above which feature mapping sets in. The class of models is then analyzed with respect to the formation of topographic maps between two layers of neurons. For Gaussian input stimuli we find that localized receptive fields and topographic maps emerge above the critical value for intracortical competition, and we calculate this value as a function of the size of the input stimuli and the range of the lateral interaction function. Additionally, we show that the learning rule can be derived via the optimization of a global cost function in a framework of probabilistic output neurons which represent a set of input stimuli by a sparse code. Received: 23 June 1999 / Accepted in revised form: 05 November 1999     

Parsimony,Exhaustivity and Balanced Detection in Neocortex

The layout of sensory brain areas is thought to subtend perception. The principles shaping these architectures and their role in information processing are still poorly understood. We investigate mathematically and computationally the representation of orientation and spatial frequency in cat primary visual cortex. We prove that two natural principles, local exhaustivity and parsimony of representation, would constrain the orientation and spatial frequency maps to display a very specific pinwheel-dipole singularity. This is particularly interesting since recent experimental evidences show a dipolar structures of the spatial frequency map co-localized with pinwheels in cat. These structures have important properties on information processing capabilities. In particular, we show using a computational model of visual information processing that this architecture allows a trade-off in the local detection of orientation and spatial frequency, but this property occurs for spatial frequency selectivity sharper than reported in the literature. We validated this sharpening on high-resolution optical imaging experimental data. These results shed new light on the principles at play in the emergence of functional architecture of cortical maps, as well as their potential role in processing information.     

Spontaneous symmetry breaking in self–organizing neural fields

We extend the theory of self-organizing neural fields in order to analyze the joint emergence of topography and feature selectivity in primary visual cortex through spontaneous symmetry breaking. We first show how a binocular one-dimensional topographic map can undergo a pattern forming instability that breaks the underlying symmetry between left and right eyes. This leads to the spatial segregation of eye specific activity bumps consistent with the emergence of ocular dominance columns. We then show how a 2-dimensional isotropic topographic map can undergo a pattern forming instability that breaks the underlying rotation symmetry. This leads to the formation of elongated activity bumps consistent with the emergence of orientation preference columns. A particularly interesting property of the latter symmetry breaking mechanism is that the linear equations describing the growth of the orientation columns exhibits a rotational shift-twist symmetry, in which there is a coupling between orientation and topography. Such coupling has been found in experimentally generated orientation preference maps     

Motor neurons and the sense of place

Seventy years ago George Romanes began to document the anatomical organization of the spinal motor system, uncovering a multilayered topographic plan that links the clustering and settling position of motor neurons to the spatial arrangement and biomechanical features of limb muscles. To this day, these findings have provided a structural foundation for analysis of the neural control of movement and serve as?a guide for studies to explore mechanisms that direct the wiring of spinal motor circuits. In this brief essay we outline the?core of Romanes's findings and place them in the context of recent studies that begin to provide insight?into molecular programs that assign motor pool position and to resolve how motor neuron position shapes circuit assembly. Romanes's findings reveal how and why neuronal positioning contributes to sensory-motor connectivity and may have relevance to circuit organization in other regions of the central nervous system.     

Learning about Time: Plastic Changes and Interindividual Brain Differences

Learning the timing of rapidly changing sensory events is crucial to construct a reliable representation of the environment and to efficiently control behavior. The neurophysiological mechanisms underlying the learning of time are unknown. We used functional and structural magnetic resonance imaging to investigate neurophysiological changes and individual brain differences underlying the learning of time in the millisecond range. We found that the representation of a trained visual temporal interval was associated with functional and structural changes in a sensory-motor network including occipital, parietal, and insular cortices, plus the cerebellum. We show that both types of neurophysiological changes correlated with changes of performance accuracy and that activity and gray-matter volume of sensorimotor cortices predicted individual learning abilities. These findings represent neurophysiological evidence of functional and structural plasticity associated with the learning of time in humans and highlight the role of sensory-motor circuits in the perceptual representation of time in the millisecond range.     

The coordinated mapping of visual space and response features in visual cortex

Whether general principles can explain the layouts of cortical maps remains unresolved. In primary visual cortex of ferret, the relationships between the maps of visual space and response features are predicted by a "dimension-reduction" model. The representation of visual space is anisotropic, with the elevation and azimuth axes having different magnification. This anisotropy is reflected in the orientation, ocular dominance, and spatial frequency domains, which are elongated such that their directions of rapid change, or high-gradient axes, are orthogonal to the high-gradient axis of the visual map. The feature maps are also strongly interdependent-their high-gradient regions avoid one another and intersect orthogonally where essential, so that overlap is minimized. Our results demonstrate a clear influence of the visual map on each feature map. In turn, the local representation of visual space is smooth, as predicted when many features are mapped within a cortical area.     

A neural model for nonassociative learning in a prototypical sensory-motor scheme: the landing reaction in flies

Nonassociative learning is an important property of neural organization in both vertebrate and invertebrate species. In this paper we propose a neural model for nonassociative learning in a well studied prototypical sensory-motor scheme: the landing reaction of flies. The general structure of the model consists of sensory processing stages, a sensory-motor gate network, and motor control circuits. The paper concentrates on the sensory-motor gate network which has an agonist-antagonist structure. Sensory inputs to this circuit are transduced by chemical messenger systems whose dynamics include depletion and replenishment terms. The resulting circuit is a gated dipole anatomy and we show that it gives a good account of nonassociative learning in the landing reaction of the fly.Supported by a grant from the National Institute of Mental Health     

Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity

 A computational model of hippocampal activity during spatial cognition and navigation tasks is presented. The spatial representation in our model of the rat hippocampus is built on-line during exploration via two processing streams. An allothetic vision-based representation is built by unsupervised Hebbian learning extracting spatio-temporal properties of the environment from visual input. An idiothetic representation is learned based on internal movement-related information provided by path integration. On the level of the hippocampus, allothetic and idiothetic representations are integrated to yield a stable representation of the environment by a population of localized overlapping CA3-CA1 place fields. The hippocampal spatial representation is used as a basis for goal-oriented spatial behavior. We focus on the neural pathway connecting the hippocampus to the nucleus accumbens. Place cells drive a population of locomotor action neurons in the nucleus accumbens. Reward-based learning is applied to map place cell activity into action cell activity. The ensemble action cell activity provides navigational maps to support spatial behavior. We present experimental results obtained with a mobile Khepera robot. Received: 02 July 1999 / Accepted in revised form: 20 March 2000     

Challenges and opportunities in synthesizing historical geospatial data using statistical models

We classified land cover types from 1940s historical aerial imagery using Object Based Image Analysis (OBIA) and compared these maps with data on recent cover. Few studies have used these kinds of maps to model drivers of cover change, partly due to two statistical challenges: 1) appropriately accounting for spatial autocorrelation and 2) appropriately modeling percent cover which is bounded between 0 and 100 and not normally distributed. We studied the change in woody cover at four sites in California's North Coast using historical (1948) and recent (2009) high spatial resolution imagery. We classified the imagery using eCognition Developer and aggregated the resulting maps to the scale of a Digital Elevation Model (DEM) in order to understand topographic drivers of woody cover change. We used Generalized Additive Models (GAMs) with a quasi-binomial probability distribution to account for spatial autocorrelation and the boundedness of the percent woody cover variable. We explored the relative influences on current percent woody cover of topographic variables (grouped using principal component analysis) reflecting water retention capacity, exposure, and within-site context, as well as historical percent woody cover and geographical coordinates. We estimated these models for pixel sizes of 20, 30, 40, 50, 60, 70, 80, 90, and 100 m, reflecting both tree neighborhood scales and stand scales. We found that historical woody cover had a consistent positive effect on current woody cover, and that the spatial autoregressive term in the model was significant even after controlling for historical cover. Specific topographic variables emerged as important for different sites at different scales, but no overall pattern emerged across sites or scales for any of the topographic variables we tested. This GAM framework for modeling historical data is flexible and could be used with more variables, more flexible relationships with predictor variables, and larger scales. Modeling drivers of woody cover change from historical ecology data sources can be a valuable way to plan restoration and enhance ecological insight into landscape change.     

Methods in phenological mapping

Summary Phenological maps of flowering provide useful information about both spatial and temporal patterns of pollen emission, and their use could bring a substantial improvement of aerobiological forecasts. This paper presents a method for preparing flowering maps by computer on the basis of phenological data and topography. Data drawn from topographic maps are processed with the aid of an empirical model, based on the relationships between phenology and environment, for obtaining a phenological delay matrix. From this matrix it is possible to derive automatically various kinds of maps (chronological, synoptical and differential). An application is described, relating to the blooming patterns of a set of wild plants in a mountainous area of Northern Italy.     

Exclusion of agricultural lands in spatial conservation prioritization strategies: consequences for biodiversity and ecosystem service representation

Agroecosystems have traditionally been considered incompatible with biological conservation goals, and often been excluded from spatial conservation prioritization strategies. The consequences for the representativeness of identified priority areas have been little explored. Here, we evaluate these for biodiversity and carbon storage representation when agricultural land areas are excluded from a spatial prioritization strategy for South America. Comparing different prioritization approaches, we also assess how the spatial overlap of priority areas changes. The exclusion of agricultural lands was detrimental to biodiversity representation, indicating that priority areas for agricultural production overlap with areas of relatively high occurrence of species. By contrast, exclusion of agricultural lands benefits representation of carbon storage within priority areas, as lands of high value for agriculture and carbon storage overlap little. When agricultural lands were included and equally weighted with biodiversity and carbon storage, a balanced representation resulted. Our findings suggest that with appropriate management, South American agroecosystems can significantly contribute to biodiversity conservation.     

Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity

Functional magnetic resonance imaging (fMRI) can provide maps of brain activation with millimeter spatial resolution but is limited in its temporal resolution to the order of seconds. Here, we describe a technique that combines structural and functional MRI with magnetoencephalography (MEG) to obtain spatiotemporal maps of human brain activity with millisecond temporal resolution. This new technique was used to obtain dynamic statistical parametric maps of cortical activity during semantic processing of visually presented words. An initial wave of activity was found to spread rapidly from occipital visual cortex to temporal, parietal, and frontal areas within 185 ms, with a high degree of temporal overlap between different areas. Repetition effects were observed in many of the same areas following this initial wave of activation, providing evidence for the involvement of feedback mechanisms in repetition priming.     

Range maps and species richness patterns: errors of commission and estimates of uncertainty

Range maps are often combined into “range overlap maps” to estimate spatial variation in species richness. Range maps are, in most cases, designed to represent a species’ maximum geographical extent and not patterns of occupancy within the range. As a consequence, range maps overestimate occupancy by presenting false occupancy (errors of commission) within the interior of the range. To assess the implications of errors of commission when developing and applying range overlap maps, we used neutral landscapes to simulate range maps and patterns of occupancy within ranges. We explored several scenarios based on combinations of six parameters defining biogeographical and cartographic factors typically encountered by investigators. Our results suggest that, in general, uncertainty is lowest when map resolutions are moderately fine, the majority of species have geographically restricted ranges, species occur throughout their range, patterns of occupancy within the range are not correlated among species, and geographically local and widespread species tend to occupy different regions. Several of these outcomes are associated with broad geographical extents, the scale at which range overlap maps are typically applied. Thus, under most circumstances, reasonably accurate and precise representation of species richness patterns can be achieved. However, these representations can be improved by enhancing occupancy data for widespread species – a primary source of uncertainty – and selecting a map resolution that captures relevant biological and environmental heterogeneity. Hence, by determining where a study is situated within the scenarios examined in our simulations, uncertainty and its sources and implications can be ascertained. With this knowledge, research goals, methods, and data sources can be reassessed and refined and, in the end, conclusions and recommendations can be qualified. Alternatively, unique regional, taxonomic, or cartographic factors could be included in future simulations to provide direct assessments of uncertainty.     

Just do it: action-dependent learning allows sensory prediction

Sensory-motor learning is commonly considered as a mapping process, whereby sensory information is transformed into the motor commands that drive actions. However, this directional mapping, from inputs to outputs, is part of a loop; sensory stimuli cause actions and vice versa. Here, we explore whether actions affect the understanding of the sensory input that they cause. Using a visuo-motor task in humans, we demonstrate two types of learning-related behavioral effects. Stimulus-dependent effects reflect stimulus-response learning, while action-dependent effects reflect a distinct learning component, allowing the brain to predict the forthcoming sensory outcome of actions. Together, the stimulus-dependent and the action-dependent learning components allow the brain to construct a complete internal representation of the sensory-motor loop.     

The emergence of somatotopic maps of the body in S1 in rats: the correspondence between functional and anatomical organization

Most of what we know about cortical map development and plasticity comes from studies in mice and rats, and for the somatosensory cortex, almost exclusively from the whisker-dominated posteromedial barrel fields. Whiskers are the main effector organs of mice and rats, and their representation in cortex and subcortical pathways is a highly derived feature of murine rodents. This specialized anatomical organization may therefore not be representative of somatosensory cortex in general, especially for species that utilize other body parts as their main effector organs, like the hands of primates. For these reasons, we examined the emergence of whole body maps in developing rats using electrophysiological recording techniques. In P5, P10, P15, P20 and adult rats, multiple recordings were made in the medial portion of S1 in each animal. Subsequently, these functional maps were related to anatomical parcellations of S1 based on a variety of histological stains. We found that at early postnatal ages (P5) medial S1 was composed almost exclusively of the representation of the vibrissae. At P10, other body part representations including the hindlimb and forelimb were present, although these were not topographically organized. By P15, a clear topographic organization began to emerge coincident with a reduction in receptive field size. By P20, body maps were adult-like. This study is the first to describe how topography of the body develops in S1 in any mammal. It indicates that anatomical parcellations and functional maps are initially incongruent but become tightly coupled by P15. Finally, because anatomical and functional specificity of developing barrel cortex appears much earlier in postnatal life than the rest of the body, the entire primary somatosensory cortex should be considered when studying general topographic map formation in development.