Purely translational realignment in grid cell firing patterns following nonmetric context change

Grid cells in entorhinal and parahippocampal cortices contribute to a network, centered on the hippocampal place cell system, that constructs a representation of spatial context for use in navigation and memory. In doing so, they use metric cues such as the distance and direction of nearby boundaries to position and orient their firing field arrays (grids). The present study investigated whether they also use purely nonmetric “context” information such as color and odor of the environment. We found that, indeed, purely nonmetric cues—sufficiently salient to cause changes in place cell firing patterns—can regulate grid positioning; they do so independently of orientation, and thus interact with linear but not directional spatial inputs. Grid cells responded homogeneously to context changes. We suggest that the grid and place cell networks receive context information directly and also from each other; the information is used by place cells to compute the final decision of the spatial system about which context the animal is in, and by grid cells to help inform the system about where the animal is within it

Influence of Cholecystokinin-8 on compound nerve action potentials from ventral gastric vagus in rats

Objective: Vagus Nerve Stimulation (VNS) has shown great promise as a potential therapy for a number of conditions, such as epilepsy, depression and for Neurometabolic Therapies, especially for treating obesity. The objective of this study was to characterize the left ventral subdiaphragmatic gastric trunk of vagus nerve (SubDiaGVN) and to analyze the influence of intravenous injection of gut hormone cholecystokinin octapeptide (CCK-8) on compound nerve action potential (CNAP) observed on the same branch, with the aim of understanding the impact of hormones on VNS and incorporating the methods and results into closed loop implant design. Methods: The cervical region of the left vagus nerve (CerVN) of male Wistar rats was stimulated with electric current and the elicited CNAPs were recorded on the SubDiaGVN under four different conditions: Control (no injection), Saline, CCK1 (100pmol/kg) and CCK2 (1000pmol/kg) injections. Results: We identified the presence of

Procesamiento de la información sensorial en la corteza somatosensorial de la rata: contribución del prosencéfalo basal

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Anatomía, Histología y Neurociencia. Fecha de lectura: 3 de Marzo de 200

Sensory input drives multiple intracellular information streams in somatosensory cortex

13 p., 6 figures and references.Stable perception arises from the interaction between sensory inputs and internal activity fluctuations in cortex. Here we analyzed how different types of activity contribute to cortical sensory processing at the cellular scale. We performed whole-cell recordings in the barrel cortex of anesthetized rats while applying ongoing whisker stimulation and measured the information conveyed about the time-varying stimulus by different types of input (membrane potential) and output (spiking) signals.Wefound that substantial, comparable amounts of incoming information are carried by two types of membrane potential signal: slow, large (up-down state) fluctuations, and faster (>20 Hz), smaller-amplitude synaptic activity. Both types of activity fluctuation are therefore significantly driven by the stimulus on an ongoing basis. Each stream conveys essentially independent information. Output (spiking) information is contained in spike timing not just relative to the stimulus but also relative to membrane potential fluctuations. Information transfer is favored in up states relative to down states. Thus, slow, ongoing activity fluctuations and finer-scale synaptic activity generate multiple channels for incoming and outgoing information within barrel cortex neurons during ongoing stimulation.Financial support was provided by Spanish Ministry of Science and Innovation (Grant BFU2008-03017/BFI, cofunded by the European Regional Development Fund); CONSOLIDER Grant CSD2007-00023), Human Frontier Science Program Grant RG0043/2004, the Italian Institute of Technology (Brain Machine Interface project of the Department of Robotics, Brain, and Cognitive Sciences), Compagnia di San Paulo, and European Commission Coordination Action ENINET Contract Number LSHM-CT-2005-19063.Peer reviewe

Transformation of adaptation and gain rescaling along the whisker sensory pathway.

Neurons in all sensory systems have a remarkable ability to adapt their sensitivity to the statistical structure of the sensory signals to which they are tuned. In the barrel cortex, firing rate adapts to the variance of a whisker stimulus and neuronal sensitivity (gain) adjusts in inverse proportion to the stimulus standard deviation. To determine how adaptation might be transformed across the ascending lemniscal pathway, we measured the responses of single units in the first and last subcortical stages, the trigeminal ganglion (TRG) and ventral posterior medial thalamic nucleus (VPM), to controlled whisker stimulation in urethane-anesthetized rats. We probed adaptation using a filtered white noise stimulus that switched between low- and high-variance epochs. We found that the firing rate of both TRG and VPM neurons adapted to stimulus variance. By fitting the responses of each unit to a Linear-Nonlinear-Poisson model, we tested whether adaptation changed feature selectivity and/or sensitivity. We found that, whereas feature selectivity was unaffected by stimulus variance, units often exhibited a marked change in sensitivity. The extent of these sensitivity changes increased systematically along the pathway from TRG to barrel cortex. However, there was marked variability across units, especially in VPM. In sum, in the whisker system, the adaptation properties of subcortical neurons are surprisingly diverse. The significance of this diversity may be that it contributes to a rich population representation of whisker dynamics

Analysis of adaptation using LNP models: testing for adaptive changes in receptive field.

<p>A. Schematic of LNP model. In the L (linear) step, a stimulus time series is convolved with one or more filters. In the N (nonlinear) step, the filtered stimulus is passed through a non-linear tuning function to produce a time-dependent firing rate. Finally, in the P (Poisson) step, this firing rate drives an inhomogeneous Poissonian spike generation process. B. Number of units, in both TRG and VPM that could be well-described (see Materials and Methods) by either an LNP model consisting of a single filter (‘single’), an LNP model consisting of 2 or more filters (‘multi’), or that could not be well-described by any LNP model. C. Filters (STAs) of two example units computed separately using spikes evoked during the high-variance (red) or low-variance (blue) epochs. D. STAs expected from ideal position, velocity and acceleration detectors respectively, given the smoothed white noise stimulus used here <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082418#pone.0082418-Petersen1" target="_blank">[23]</a>. E. Histogram of normalized dot products between the high- and low-variance filters (STAs) of each unit in the TRG and VPM (red), compared to the histogram of normalized dot products between pairs of filters combined at random (white). Dot products between high- and low-variance filters were significantly different than those between pairs combined at random.</p

Response of subcortical neurons to variance switching.

<p>A1–A2. Schematic of the whisker stimulus showing low and high variance epochs. In order to show both low-to-high and high-to-low transitions, two high-low cycles are shown. B1. Average firing rate evoked by the stimulus for three example TRG units. B2. Corresponding data for three example VPM units. C1. The firing rate of each unit was normalized by converting it to a z-score. Average z-score for TRG. Error bars show SD. C2. Corresponding data for VPM. Note greater variability of rates across VPM units than across TRG.</p

Whisker motion stimulus for probing adaptation.

<p>Whiskers were simultaneously deflected with a filtered noise stimulus dorso-ventrally. The variance of the distribution of fluctuations in whisker position switched between ‘low’ and ‘high’ every 5 s. Variance during the high epochs was twice that during the low epochs. Lower left, magnified example of a high- to low-variance transition. Lower right, stimulus autocorrelation function.</p

Quantification of firing rate adaptation and comparison across the whisker pathway.

<p>A. The firing rate trajectory of an example unit over one high-low variance cycle, normalized as a z-score. Schematic represents computation of adaptation indices (AIs) from z-score rates at different times in cycle (e.g., <i>z_init,hi</i>). <i>init</i> signifies rates collected in the first two 100-ms bins within an epoch; <i>ss</i> the final five bins within the epoch, corresponding to the steady-state response. B. AI for each TRG unit and each VPM unit in our database. These data are compared to AIs of S1 units previously reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082418#pone.0082418-Maravall2" target="_blank">[20]</a>. Adaptation was more diverse in the VPM than in TRG, but was not significantly higher on average; adaptation did increase on average in S1.</p