Foveolar Drusen Decrease Fixation Stability in Pre-Symptomatic AMD.

Purpose: This study aims at linking subtle changes of fixational eye movements (FEM) in controls and in patients with foveal drusen using adaptive optics retinal imaging in order to find anatomo-functional markers for pre-symptomatic age-related macular degeneration (AMD). Methods: We recruited 7 young controls, 4 older controls, and 16 patients with presymptomatic AMD with foveal drusen from the Silversight Cohort. A high-speed research-grade adaptive optics flood illumination ophthalmoscope (AO-FIO) was used for monocular retinal tracking of fixational eye movements. The system allows for sub-arcminute resolution, and high-speed and distortion-free imaging of the foveal area. Foveal drusen position and size were documented using gaze-dependent imaging on a clinical-grade AO-FIO. Results: FEM were measured with high precision (RMS-S2S = 0.0015 degrees on human eyes) and small foveal drusen (median diameter = 60 µm) were detected with high contrast imaging. Microsaccade amplitude, drift diffusion coefficient, and ISOline area (ISOA) were significantly larger for patients with foveal drusen compared with controls. Among the drusen participants, microsaccade amplitude was correlated to drusen eccentricity from the center of the fovea. Conclusions: A novel high-speed high-precision retinal tracking technique allowed for the characterization of FEM at the microscopic level. Foveal drusen altered fixation stability, resulting in compensatory FEM changes. Particularly, drusen at the foveolar level seemed to have a stronger impact on microsaccade amplitudes and ISOA. The unexpected anatomo-functional link between small foveal drusen and fixation stability opens up a new perspective of detecting oculomotor signatures of eye diseases at the presymptomatic stage.

Landmark-based spatial navigation across the human lifespan.

Human spatial cognition has been mainly characterized in terms of egocentric (body-centered) and allocentric (world-centered) wayfinding bhavior. It was hypothesized that allocentric spatial coding, as a special high-level cognitive ability, develops later and deteriorates earlier than the egocentric one throughout lifetime. We challenged this hypothesis by testing the use of landmarks versus geometric cues in a cohort of 96 deeply phenotyped participants, who physically navigated an equiangular Y maze, surrounded by landmarks or an anisotropic one. The results show that an apparent allocentric deficit in children and aged navigators is caused specifically by difficulties in using landmarks for navigation while introducing a geometric polarization of space made these participants as efficient allocentric navigators as young adults. This finding suggests that allocentric behavior relies on two dissociable sensory processing systems that are differentially affected by human aging. Whereas landmark processing follows an inverted-U dependence on age, spatial geometry processing is conserved, highlighting its potential in improving navigation performance across the lifespan.

Agreement in Spiking Neural Networks.

We study the problem of binary agreement in a spiking neural network (SNN). We show that binary agreement on n inputs can be achieved with O(n) of auxiliary neurons. Our simulation results suggest that agreement can be achieved in our network in O(logn) time. We then describe a subclass of SNNs with a biologically plausible property, which we call size-independence. We prove that solving a class of problems, including agreement and Winner-Take-All, in this model requires Ω(n) auxiliary neurons, which makes our agreement network size-optimal.

Computational epidemiology study of homeostatic compensation during sensorimotor aging.

The vestibulo-ocular reflex (VOR) stabilizes vision during head motion. Age-related changes of vestibular neuroanatomical properties predict a linear decay of VOR function. Nonetheless, human epidemiological data show a stable VOR function across the life span. In this study, we model cerebellum-dependent VOR adaptation to relate structural and functional changes throughout aging. We consider three neurosynaptic factors that may codetermine VOR adaptation during aging: the electrical coupling of inferior olive neurons, the long-term spike timing-dependent plasticity at parallel fiber - Purkinje cell synapses and mossy fiber - medial vestibular nuclei synapses, and the intrinsic plasticity of Purkinje cell synapses Our cross-sectional aging analyses suggest that long-term plasticity acts as a global homeostatic mechanism that underpins the stable temporal profile of VOR function. The results also suggest that the intrinsic plasticity of Purkinje cell synapses operates as a local homeostatic mechanism that further sustains the VOR at older ages. Importantly, the computational epidemiology approach presented in this study allows discrepancies among human cross-sectional studies to be understood in terms of interindividual variability in older individuals. Finally, our longitudinal aging simulations show that the amount of residual fibers coding for the peak and trough of the VOR cycle constitutes a predictive hallmark of VOR trajectories over a lifetime.

Long-term memory, synaptic plasticity and dopamine in rodent medial prefrontal cortex: Role in executive functions.

Mnemonic functions, supporting rodent behavior in complex tasks, include both long-term and (short-term) working memory components. While working memory is thought to rely on persistent activity states in an active neural network, long-term memory and synaptic plasticity contribute to the formation of the underlying synaptic structure, determining the range of possible states. Whereas, the implication of working memory in executive functions, mediated by the prefrontal cortex (PFC) in primates and rodents, has been extensively studied, the contribution of long-term memory component to these tasks received little attention. This review summarizes available experimental data and theoretical work concerning cellular mechanisms of synaptic plasticity in the medial region of rodent PFC and the link between plasticity, memory and behavior in PFC-dependent tasks. A special attention is devoted to unique properties of dopaminergic modulation of prefrontal synaptic plasticity and its contribution to executive functions.

Modeling place cells and grid cells in multi-compartment environments: Entorhinal-hippocampal loop as a multisensory integration circuit.

Hippocampal place cells and entorhinal grid cells are thought to form a representation of space by integrating internal and external sensory cues. Experimental data show that different subsets of place cells are controlled by vision, self-motion or a combination of both. Moreover, recent studies in environments with a high degree of visual aliasing suggest that a continuous interaction between place cells and grid cells can result in a deformation of hexagonal grids or in a progressive loss of visual cue control over grid fields. The computational nature of such a bidirectional interaction remains unclear. In this work we present a neural network model of the dynamic interaction between place cells and grid cells within the entorhinal-hippocampal processing loop. The model was tested in two recent experimental paradigms involving environments with visually similar compartments that provided conflicting evidence about visual cue control over self-motion-based spatial codes. Analysis of the model behavior suggests that the strength of entorhinal-hippocampal dynamical loop is the key parameter governing differential cue control in multi-compartment environments. Moreover, construction of separate spatial representations of visually identical compartments required a progressive weakening of visual cue control over place fields in favor of self-motion based mechanisms. More generally our results suggest a functional segregation between plastic and dynamic processes in hippocampal processing.

Age-related preference for geometric spatial cues during real-world navigation.

Ageing effects on spatial navigation are characterized mainly in terms of impaired allocentric strategies. However, an alternative hypothesis is that navigation difficulties in aged people are associated with deficits in processing and encoding spatial cues. We tested this hypothesis by studying how geometry and landmark cues control navigation in young and older adults in a real, ecological environment. Recordings of body and gaze dynamics revealed a preference for geometry-based navigation in older adults, and for landmark-based navigation in younger ones. While cue processing was associated with specific fixation patterns, advanced age manifested itself in a longer reorientation time, reflecting an unbalanced exploration-exploitation trade-off in scanning policies. Moreover, a battery of tests revealed a specific cognitive deficit in older adults with geometric preference. These results suggest that allocentric strategy deficits in ageing can result from difficulties related to landmark coding, and predict recovery of allocentric strategies in geometrically polarized environments.

Unsupervised detection of microsaccades in a high-noise regime.

Micromovements of the eye during visual fixations provide clues about how our visual system acquires information. The analysis of fixational eye movements can thus serve as a noninvasive means to detect age-related or pathological changes in visual processing, which can in turn reflect associated cognitive or neurological disorders. However, the utility of such diagnostic approaches relies on the quality and usability of detection methods applied for the eye movement analysis. Here, we propose a novel method for (micro)saccade detection that is resistant to high-frequency recording noise, a frequent problem in video-based eye tracking in either aged subjects or subjects suffering from a vision-related pathology. The method is fast, it does not require manual noise removal, and it can work with position, velocity, or acceleration features, or a combination thereof. The detection accuracy of the proposed method is assessed on a new dataset of manually labeled recordings acquired from 14 subjects of advanced age (69-81 years old), performing an ocular fixation task. It is demonstrated that the detection accuracy of the new method compares favorably to that of two frequently used reference methods and that it is comparable to the best of the two algorithms when tested on an existing low-noise eye-tracking dataset.

Dopaminergic control of long-term depression/long-term potentiation threshold in prefrontal cortex.

Long-term memory in the prefrontal cortex is a necessary component of adaptive executive control and is strongly modulated by dopamine. However, the functional significance of this dopaminergic modulation remains elusive. In vitro experimental results on dopamine-dependent shaping of prefrontal long-term plasticity often appear inconsistent and, altogether, draw a complicated picture. It is also generally difficult to relate these findings to in vivo observations given strong differences between the two experimental conditions. This study presents a unified view of the functional role of dopamine in the prefrontal cortex by framing it within the Bienenstock-Cooper-Munro theory of cortical plasticity. We investigate dopaminergic modulation of long-term plasticity through a multicompartment Hodgkin-Huxley model of a prefrontal pyramidal neuron. Long-term synaptic plasticity in the model is governed by a calcium- and dopamine-dependent learning rule, in which dopamine exerts its action via D1 and D2 dopamine receptors in a concentration-dependent manner. Our results support a novel function of dopamine in the prefrontal cortex, namely that it controls the synaptic modification threshold between long-term depression and potentiation in pyramidal neurons. The proposed theoretical framework explains a wide range of experimental results and provides a link between in vitro and in vivo studies of dopaminergic plasticity modulation. It also suggests that dopamine may constitute a new player in metaplastic and homeostatic processes in the prefrontal cortex.

Contribution of cerebellar sensorimotor adaptation to hippocampal spatial memory.

Complementing its primary role in motor control, cerebellar learning has also a bottom-up influence on cognitive functions, where high-level representations build up from elementary sensorimotor memories. In this paper we examine the cerebellar contribution to both procedural and declarative components of spatial cognition. To do so, we model a functional interplay between the cerebellum and the hippocampal formation during goal-oriented navigation. We reinterpret and complete existing genetic behavioural observations by means of quantitative accounts that cross-link synaptic plasticity mechanisms, single cell and population coding properties, and behavioural responses. In contrast to earlier hypotheses positing only a purely procedural impact of cerebellar adaptation deficits, our results suggest a cerebellar involvement in high-level aspects of behaviour. In particular, we propose that cerebellar learning mechanisms may influence hippocampal place fields, by contributing to the path integration process. Our simulations predict differences in place-cell discharge properties between normal mice and L7-PKCI mutant mice lacking long-term depression at cerebellar parallel fibre-Purkinje cell synapses. On the behavioural level, these results suggest that, by influencing the accuracy of hippocampal spatial codes, cerebellar deficits may impact the exploration-exploitation balance during spatial navigation.

The role of tonic and phasic dopamine for long-term synaptic plasticity in the prefrontal cortex: a computational model.

This work presents a computational model of dopamine (DA) influence on long-term potentiation (LTP) and long-term depression (LTD) in the prefrontal cortex. Distinct properties of the model are a DA-concentration-dependent switch from depression to potentiation during induction of plasticity, and an inverted-U-shaped dependence of protein synthesis on the level of background DA. Protein synthesis is responsible for the maintenance of LTP/LTD in the model. Our simulations suggest that in vitro experimental data on prefrontal plasticity, induced by high-frequency stimulation, may be accounted for by a single synaptic mechanism that is slowly (on the timescale of minutes) activated in the presence of DA in a concentration-dependent manner. The activation value determines the direction of plasticity during induction, while it also modulates the magnitude of plasticity during maintenance. More generally, our results support the hypothesis that phasic release of endogenous DA is necessary for the maintenance of long-term changes in synaptic efficacy, while the concentration of tonic DA determines the direction and magnitude of these changes in the PFC.

Spatial learning and action planning in a prefrontal cortical network model.

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive "insight" capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates.

A reinforcement learning approach to model interactions between landmarks and geometric cues during spatial learning.

In contrast to predictions derived from the associative learning theory, a number of behavioral studies suggested the absence of competition between geometric cues and landmarks in some experimental paradigms. In parallel to these studies, neurobiological experiments suggested the existence of separate independent memory systems which may not always interact according to classic associative principles. In this paper we attempt to combine these two lines of research by proposing a model of spatial learning that is based on the theory of multiple memory systems. In our model, a place-based locale strategy uses activities of modeled hippocampal place cells to drive navigation to a hidden goal, while a stimulus-response taxon strategy, presumably mediated by the dorso-lateral striatum, learns landmark-approaching behavior. A strategy selection network, proposed to reside in the prefrontal cortex, implements a simple reinforcement learning rule to switch behavioral strategies. The model is used to reproduce the results of a behavioral experiment in which an interaction between a landmark and geometric cues was studied. We show that this model, built on the basis of neurobiological data, can explain the lack of competition between the landmark and geometry, potentiation of geometry learning by the landmark, and blocking. Namely, we propose that the geometry potentiation is a consequence of cooperation between memory systems during learning, while blocking is due to competition between the memory systems during action selection.

Path planning versus cue responding: a bio-inspired model of switching between navigation strategies.

In this article, we describe a new computational model of switching between path-planning and cue-guided navigation strategies. It is based on three main assumptions: (i) the strategies are mediated by separate memory systems that learn independently and in parallel; (ii) the learning algorithms are different in the two memory systems-the cue-guided strategy uses a temporal-difference (TD) learning rule to approach a visible goal, whereas the path-planning strategy relies on a place-cell-based graph-search algorithm to learn the location of a hidden goal; (iii) a strategy selection mechanism uses TD-learning rule to choose the most successful strategy based on past experience. We propose a novel criterion for strategy selection based on the directions of goal-oriented movements suggested by the different strategies. We show that the selection criterion based on this "common currency" is capable of choosing the best among TD-learning and planning strategies and can be used to solve navigational tasks in continuous state and action spaces. The model has been successfully applied to reproduce rat behavior in two water-maze tasks in which the two strategies were shown to interact. The model was used to analyze competitive and cooperative interactions between different strategies during these tasks as well as relative influence of different types of sensory cues.

Is there a geometric module for spatial orientation? Insights from a rodent navigation model.

Modern psychological theories of spatial cognition postulate the existence of a geometric module for reorientation. This concept is derived from experimental data showing that in rectangular arenas with distinct landmarks in the corners, disoriented rats often make diagonal errors, suggesting their preference for the geometric (arena shape) over the nongeometric (landmarks) cues. Moreover, sensitivity of hippocampal cell firing to changes in the environment layout was taken in support of the geometric module hypothesis. Using a computational model of rat navigation, the authors proposed and tested the alternative hypothesis that the influence of spatial geometry on both behavioral and neuronal levels can be explained by the properties of visual features that constitute local views of the environment. Their modeling results suggest that the pattern of diagonal errors observed in reorientation tasks can be understood by the analysis of sensory information processing that underlies the navigation strategy employed to solve the task. In particular, 2 navigation strategies were considered: (a) a place-based locale strategy that relies on a model of grid and place cells and (b) a stimulus-response taxon strategy that involves direct association of local views with action choices. The authors showed that the application of the 2 strategies in the reorientation tasks results in different patterns of diagonal errors, consistent with behavioral data. These results argue against the geometric module hypothesis by providing a simpler and biologically more plausible explanation for the related experimental data. Moreover, the same model also describes behavioral results in different types of water-maze tasks.

Robust self-localisation and navigation based on hippocampal place cells.

A computational model of the hippocampal function in spatial learning is presented. A spatial representation is incrementally acquired during exploration. Visual and self-motion information is fed into a network of rate-coded neurons. A consistent and stable place code emerges by unsupervised Hebbian learning between place- and head direction cells. Based on this representation, goal-oriented navigation is learnt by applying a reward-based learning mechanism between the hippocampus and nucleus accumbens. The model, validated on a real and simulated robot, successfully localises itself by recalibrating its path integrator using visual input. A navigation map is learnt after about 20 trials, comparable to rats in the water maze. In contrast to previous works, this system processes realistic visual input. No compass is needed for localisation and the reward-based learning mechanism extends discrete navigation models to continuous space. The model reproduces experimental findings and suggests several neurophysiological and behavioural predictions in the rat.

A computational model of parallel navigation systems in rodents.

Several studies in rats support the idea of multiple neural systems competing to select the best action for reaching a goal or food location. Locale navigation strategies, necessary for reaching invisible goals, seem to be mediated by the hippocampus and the ventral and dorsomedial striatum whereas taxon strategies, applied for approaching goals in the visual field, are believed to involve the dorsolateral striatum. A computational model of action selection is presented, in which different experts, implementing locale and taxon strategies, compete in order to select the appropriate behavior for the current task. The model was tested in a simulated robot using an experimental paradigm that dissociates the use of cue and spatial information.