1,442 research outputs found

    On the effects of firing memory in the dynamics of conjunctive networks

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    Boolean networks are one of the most studied discrete models in the context of the study of gene expression. In order to define the dynamics associated to a Boolean network, there are several \emph{update schemes} that range from parallel or \emph{synchronous} to \emph{asynchronous.} However, studying each possible dynamics defined by different update schemes might not be efficient. In this context, considering some type of temporal delay in the dynamics of Boolean networks emerges as an alternative approach. In this paper, we focus in studying the effect of a particular type of delay called \emph{firing memory} in the dynamics of Boolean networks. Particularly, we focus in symmetric (non-directed) conjunctive networks and we show that there exist examples that exhibit attractors of non-polynomial period. In addition, we study the prediction problem consisting in determinate if some vertex will eventually change its state, given an initial condition. We prove that this problem is {\bf PSPACE}-complete

    Segregation of cortical head direction cell assemblies on alternating theta cycles

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    High-level cortical systems for spatial navigation, including entorhinal grid cells, critically depend on input from the head direction system. We examined spiking rhythms and modes of synchrony between neurons participating in head direction networks for evidence of internal processing, independent of direct sensory drive, which may be important for grid cell function. We found that head direction networks of rats were segregated into at least two populations of neurons firing on alternate theta cycles (theta cycle skipping) with fixed synchronous or anti-synchronous relationships. Pairs of anti-synchronous theta cycle skipping neurons exhibited larger differences in head direction tuning, with a minimum difference of 40 degrees of head direction. Septal inactivation preserved the head direction signal, but eliminated theta cycle skipping of head direction cells and grid cell spatial periodicity. We propose that internal mechanisms underlying cycle skipping in head direction networks may be critical for downstream spatial computation by grid cells.We kindly thank S. Gillet, J. Hinman, E. Newman and L. Ewell for their invaluable consultations and comments on previous versions of this manuscript, as well as M. Connerney, S. Eriksson, C. Libby and T. Ware for technical assistance and behavioral training. This work was supported by grants from the National Institute of Mental Health (R01 MH60013 and MH61492) and the Office of Naval Research Multidisciplinary University Research Initiative (N00014-10-1-0936). (R01 MH60013 - National Institute of Mental Health; MH61492 - National Institute of Mental Health; N00014-10-1-0936 - Office of Naval Research Multidisciplinary University Research Initiative)Accepted manuscrip

    Parallel and convergent processing in grid cell, head-direction cell, boundary cell, and place cell networks.

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    The brain is able to construct internal representations that correspond to external spatial coordinates. Such brain maps of the external spatial topography may support a number of cognitive functions, including navigation and memory. The neuronal building block of brain maps are place cells, which are found throughout the hippocampus of rodents and, in a lower proportion, primates. Place cells typically fire in one or few restricted areas of space, and each area where a cell fires can range, along the dorsoventral axis of the hippocampus, from 30 cm to at least several meters. The sensory processing streams that give rise to hippocampal place cells are not fully understood, but substantial progress has been made in characterizing the entorhinal cortex, which is the gateway between neocortical areas and the hippocampus. Entorhinal neurons have diverse spatial firing characteristics, and the different entorhinal cell types converge in the hippocampus to give rise to a single, spatially modulated cell type-the place cell. We therefore suggest that parallel information processing in different classes of cells-as is typically observed at lower levels of sensory processing-continues up into higher level association cortices, including those that provide the inputs to hippocampus. WIREs Cogn Sci 2014, 5:207-219. doi: 10.1002/wcs.1272 Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website

    Neural systems supporting navigation

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    Highlights: • Recent neuroimaging and electrophysiology studies have begun to shed light on the neural dynamics of navigation systems. • Computational models have advanced theories of how entorhinal grid cells and hippocampal place cells might serve navigation. • Hippocampus and entorhinal cortex provide complementary representations of routes and vectors for navigation. Much is known about how neural systems determine current spatial position and orientation in the environment. By contrast little is understood about how the brain represents future goal locations or computes the distance and direction to such goals. Recent electrophysiology, computational modelling and neuroimaging research have shed new light on how the spatial relationship to a goal may be determined and represented during navigation. This research suggests that the hippocampus may code the path to the goal while the entorhinal cortex represents the vector to the goal. It also reveals that the engagement of the hippocampus and entorhinal cortex varies across the different operational stages of navigation, such as during travel, route planning, and decision-making at waypoints

    Theta oscillations, timing and cholinergic modulation in the rodent hippocampal circuit

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    The medial temporal lobe (MTL) is crucial for episodic and spatial memory, and shows rhythmicity in the local field potential and neuronal spiking. Gamma oscillations (>40Hz) are mediatepd by local circuitry and interact with slower theta oscillations (6-10 Hz). Both oscillation frequencies are modulated by cholinergic input from the medial septum. Entorhinal grid cells fire when an animal visits particular locations in the environment arranged on the corners of tightly packed, equilateral triangles. Grid cells show phase precession, in which neurons fire at progressively earlier phases relative to theta oscillation as animals move through firing fields. This work focuses on the temporal organization of spiking and network rhythms, and their modulation by septal inputs, which are thought to be involved in MTL function. First, I recorded grid cells as rats explored open spaces and examined precession, previously only characterized on linear tracks, and compared it to predictions from models. I identified precession, including in conjunctive head-direction-by-grid cells and on passes that clipped the edge of the firing field. Secondly, I studied problems of measuring single neuron theta rhythmicity and developed an improved approach. Using the novel approach, I identified diverse modulation of rat medial entorhinal neurons’ rhythmic frequencies by running speed, independent from the modulation of firing rate by speed. Under pharmacological inactivation of the septum, rhythmic tuning was disrupted while rate tuning was enhanced. The approach also showed that available data is insufficient to prove that bat grid cells are arrhythmic due to low firing rates. In the final project, I optogenetically silenced cholinergic septal cells while recording from hippocampal area CA1. I identified changes in theta rhythmic currents and in theta-gamma coupling. This silencing disrupted performance when applied during the encoding phase of a delayed match to position task. These data support hypothetical roles of these rhythms in encoding and retrieval and suggest possible mechanisms for their modulation. Together, evidence from these projects suggests a role for theta in the function of spatial and episodic memory. These oscillations have important implications for communication and computation, and they can provide a substrate for efficient brain function

    Learning place cells, grid cells and invariances with excitatory and inhibitory plasticity

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    Neurons in the hippocampus and adjacent brain areas show a large diversity in their tuning to location and head direction, and the underlying circuit mechanisms are not yet resolved. In particular, it is unclear why certain cell types are selective to one spatial variable, but invariant to another. For example, place cells are typically invariant to head direction. We propose that all observed spatial tuning patterns – in both their selectivity and their invariance – arise from the same mechanism: Excitatory and inhibitory synaptic plasticity driven by the spatial tuning statistics of synaptic inputs. Using simulations and a mathematical analysis, we show that combined excitatory and inhibitory plasticity can lead to localized, grid-like or invariant activity. Combinations of different input statistics along different spatial dimensions reproduce all major spatial tuning patterns observed in rodents. Our proposed model is robust to changes in parameters, develops patterns on behavioral timescales and makes distinctive experimental predictions.BMBF, 01GQ1201, Lernen und Gedächtnis in balancierten Systeme

    Space, time and memory in the medial temporal lobe

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    This thesis focuses on memory and the representation of space in the medial temporal lobe, their interaction and their temporal structure. Chapter 1 briefly introduces the topic, with emphasis on the open questions that the subsequent chapters aim to address. Chapter 2 is dedicated to the issue of spatial memory in the medial entorhinal cortex. It investigates the possibility to store multiple independent maps in a recurrent network of grid cells, from a theoretical perspective. This work was conducted in collaboration with Remi Monasson, Alexis Dubreuil and Sophie Rosay and is published in (Spalla et al. 2019). Chapter 3 focuses on the problem of the dynamical update of the representation of space during navigation. It presents the results of the analysis of electrophysiological data, previously collected by Charlotte Boccara (Boccara et al., 2010), investigating the encoding of self-movement signals (speed and angular velocity of the head) in the parahippocampal region of rats. Chapter 4 addresses the problem of the temporal dynamics of memory retrieval, again from a computational point of view. A continuous attractor network model is presented, endowed with a mechanism that makes it able to retrieve continuous temporal sequences. The dynamical behaviour of the system is investigated with analytical calculations and numerical simulations, and the storage capacity for dynamical memories is computed. Finally, chapter 4 discusses the meaning and the scope of the results presented, and highlights possible future directions

    Two computational neural models : rodent perirhinal cortex and crab cardiac ganglion

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    Neural engineering research has been rapidly growing in prominence in the past two decades, with 'reverse engineer the brain' listed as one of the 14 grand challenges outlined by the National Academy of Engineering. The computational aspect of reverse engineering includes a study of how both single neurons and networks of neurons integrate diverse signals from both the environment and from within the animal and make complex decisions. Since there are many limitations on the experiments that can be performed in alive or isolated biological systems, there is a need of standalone computational models which can help perform 'in silico' experiments. This dissertation focuses on such 'in silico' neuronal models to predict underlying mechanisms of governing interactions and robustness. The first model investigated is that of a rodent perirhinal cortex area 36 (PRC), and its role in associative memory formation. A large-scale 520 cell biophysical model of the PRC was developed using biological data from the literature. We then used the model to shed light on the mechanisms that support associative memory in the perirhinal network. These analyses revealed that perirhinal associative plasticity is critically dependent on a specific subset of neurons, termed conjunctive cells. When the model network was trained with spatially distributed but coincident neocortical inputs, these conjunctive cells acquired excitatory responses to the paired neocortical inputs and conveyed them to widely distributed perirhinal sites via longitudinal projections. Ablation of conjunctive cells during recall abolished expression of the associative memory. The second model focuses on a model for crab cardiac system consisting of five Large Cells (LC) developed using firsthand biological data. The model is then used to study the features of its underlying oscillation in its membrane potential during a rhythm and to reverse engineer the experimentally discovered phenomenon related to network synchrony. The model predicted multiple mechanisms of compensation to restore network synchrony based on compensatory intrinsic conductances. Finally, a third model, related to the second one, was of an improved three-compartmental biophysical model of an LC that is morphologically realistic and includes provision for inputs from the SCs. To determine viable LC models, maximal conductances in three compartments of an LC are determined by random sampling from a biologically characterized 9D-parameter space, followed by a three-stage rejection protocol that checks for conformity with features in experimental single cell traces. Random LC models that pass the single cell rejection protocol are then incorporated into a network model followed by a final rejection protocol stage. Using disparate experimental data, the study provides hitherto unknown structure-function insights related to the crustacean cardiac ganglion large cell, including the differential roles of active conductances in the three compartments. The novel morphological architecture for the large cell was validated using biological data and used to make predictions about function. A testable prediction related to function was that active conductances, specifically, the persistent sodium current, is required in the neurite to transmit the spike waveforms from the spike initiation zone to the soma. Another pertains to the co-variation of maximal conductances of the persistent sodium current with that of the leak current

    The reentry hypothesis: The putative interaction of the frontal eye field, ventrolateral prefrontal cortex, and areas V4, IT for attention and eye movement

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    Attention is known to play a key role in perception, including action selection, object recognition and memory. Despite findings revealing competitive interactions among cell populations, attention remains difficult to explain. The central purpose of this paper is to link up a large number of findings in a single computational approach. Our simulation results suggest that attention can be well explained on a network level involving many areas of the brain. We argue that attention is an emergent phenomenon that arises from reentry and competitive interactions. We hypothesize that guided visual search requires the usage of an object-specific template in prefrontal cortex to sensitize V4 and IT cells whose preferred stimuli match the target template. This induces a feature-specific bias and provides guidance for eye movements. Prior to an eye movement, a spatially organized reentry from occulomotor centers, specifically the movement cells of the frontal eye field, occurs and modulates the gain of V4 and IT cells. The processes involved are elucidated by quantitatively comparing the time course of simulated neural activity with experimental data. Using visual search tasks as an example, we provide clear and empirically testable predictions for the participation of IT, V4 and the frontal eye field in attention. Finally, we explain a possible physiological mechanism that can lead to non-flat search slopes as the result of a slow, parallel discrimination process

    Replay as wavefronts and theta sequences as bump oscillations in a grid cell attractor network.

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    Grid cells fire in sequences that represent rapid trajectories in space. During locomotion, theta sequences encode sweeps in position starting slightly behind the animal and ending ahead of it. During quiescence and slow wave sleep, bouts of synchronized activity represent long trajectories called replays, which are well-established in place cells and have been recently reported in grid cells. Theta sequences and replay are hypothesized to facilitate many cognitive functions, but their underlying mechanisms are unknown. One mechanism proposed for grid cell formation is the continuous attractor network. We demonstrate that this established architecture naturally produces theta sequences and replay as distinct consequences of modulating external input. Driving inhibitory interneurons at the theta frequency causes attractor bumps to oscillate in speed and size, which gives rise to theta sequences and phase precession, respectively. Decreasing input drive to all neurons produces traveling wavefronts of activity that are decoded as replays
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