Neuronal Population Responses Research Articles

Regulatory effect of Dimethyl Sulfoxide (DMSO) on astrocytic reactivity in a murine model of cerebral infarction by arterial embolization

The pathophysiology of cerebral ischemia is essential for early diagnosis, neurologic recovery, the early onset of drug treatment and the prognosis of ischemic events. Experimental models of cerebral ischemia can be used to evaluate the cellular response phenomena and possible neurological protection by drugs. To characterize the cellular changes in the neuronal population and astrocytic response by the effect of Dimethyl Sulfoxide (DMSO) on a model of ischemia caused by cerebral embolism. Twenty Wistar rats were divided into four groups (n= 5). The infarct was induced with α-bovine thrombin (40 NIH/Unit.). The treated group received 90 mg (100 μL) of DMSO in saline (1:1 v/v) intraperitoneally for 5 days; ischemic controls received only NaCl (placebo) and two non-ischemic groups (simulated) received NaCl and DMSO respectively. We evaluated the neuronal (anti-NeuN) and astrocytic immune-reactivity (anti-GFAP). The results were analyzed by densitometry (NIH Image J-Fiji 1.45 software) and analysis of variance (ANOVA) with the Graph pad software (Prism 5). Cerebral embolism induced reproducible and reliable lesions in the cortex and hippocampus (CA1)., similar to those of focal models. DMSO did not reverse the loss of post-ischemia neuronal immune-reactivity, but prevented the morphological damage of neurons, and significantly reduced astrocytic hyperactivity in the somato-sensory cortex and CA1 (p <0.001). The regulatory effect of DMSO on astrocyte hyperreactivity and neuronal-astroglial cytoarchitecture , gives it potential neuroprotective properties for the treatment of thromboembolic cerebral ischemia in the acute phase.

Effects of hybrid synapses on the vibrational resonance in small-world neuronal networks

We investigate the effect of vibrational resonance in small-world neuronal networks with hybrid chemical and electrical synapses. It is shown that, irrespective of the probability of chemical synapses, an optimal amplitude of high-frequency component of the signal can optimize the dynamical response of neuron populations to the low-frequency component, which encodes the information. This effect of vibrational resonance of neuronal systems depends extensively on the network structure and parameters, which determine the ability of neuronal networks to enhance the outreach of localized subthreshold low-frequency signal. In particular, chemical synaptic coupling is more efficient than the electrical coupling for the transmission of local input signal due to its selective coupling. Moreover, there exists an optimal small-world topology characterized by an optimal value of rewiring probability, warranting the largest peak value of the system response. Considering that two-frequency signals are ubiquity in brain dynamics, we expect the presented results could have important implications for signal processing in neuronal systems.

Optimal Population Codes for Space: Grid Cells Outperform Place Cells

Rodents use two distinct neuronal coordinate systems to estimate their position: place fields in the hippocampus and grid fields in the entorhinal cortex. Whereas place cells spike at only one particular spatial location, grid cells fire at multiple sites that correspond to the points of an imaginary hexagonal lattice. We study how to best construct place and grid codes, taking the probabilistic nature of neural spiking into account. Which spatial encoding properties of individual neurons confer the highest resolution when decoding the animal's position from the neuronal population response? A priori, estimating a spatial position from a grid code could be ambiguous, as regular periodic lattices possess translational symmetry. The solution to this problem requires lattices for grid cells with different spacings; the spatial resolution crucially depends on choosing the right ratios of these spacings across the population. We compute the expected error in estimating the position in both the asymptotic limit, using Fisher information, and for low spike counts, using maximum likelihood estimation. Achieving high spatial resolution and covering a large range of space in a grid code leads to a trade-off: the best grid code for spatial resolution is built of nested modules with different spatial periods, one inside the other, whereas maximizing the spatial range requires distinct spatial periods that are pairwisely incommensurate. Optimizing the spatial resolution predicts two grid cell properties that have been experimentally observed. First, short lattice spacings should outnumber long lattice spacings. Second, the grid code should be self-similar across different lattice spacings, so that the grid field always covers a fixed fraction of the lattice period. If these conditions are satisfied and the spatial "tuning curves" for each neuron span the same range of firing rates, then the resolution of the grid code easily exceeds that of the best possible place code with the same number of neurons.

Categorical representation of objects in the central nucleus of the monkey amygdala

The primate amygdala consists of several subnuclei. Neurons in this brain area have been known to respond to stimuli belonging to specific categories of objects, such as faces, animals, and artifacts. However, little is known about the functional differences among the nuclei of the primate amygdala. To clarify functional differences among these subnuclei in object categorization, we compared the responsiveness of neuronal populations among the lateral, basal and central nuclei of the monkey amygdala. The activity of 203 neurons was recorded while video clips of 13 stimuli belonging to three categories (monkey, human, and artifact) were presented. Of these neurons, 37, 39 and 37 neurons in the lateral, basal and central nuclei, respectively, responded to at least one of the stimuli. We applied a cluster analysis to the neuronal population responses from these nuclei, and also calculated information about the three categories and monkey identity from each neuronal population. We found that the three categories and monkey identity could be more properly classified by neuronal responsiveness in the central nucleus, which is an output gate of the amygdala, than by that in the lateral and basal nuclei. These results suggest that the information about objects suitable for the generation of appropriate emotional response is built up within the primate amygdala via an intra-amygdala network from the lateral nucleus to the central nucleus.

Stimulus repetition reduces stimulus encoding by small neuronal populations in macaque inferior temporal cortex

Event Abstract Back to Event Stimulus repetition reduces stimulus encoding by small neuronal populations in macaque inferior temporal cortex Dzmitry A. Kaliukhovich1 and Rufin Vogels1* 1 KU Leuven, Neuroscience, Belgium Stimulus repetition reduces spiking responses in macaque inferior temporal (IT) cortex, i.e. repetition suppression or adaptation. Single cell recording studies in V1 and V4 reported that adaptation improves stimulus encoding. Thus far it is not known whether the same holds true in IT, in particular given the apparently greater adaptation strength in IT compared to earlier visual areas. Here, we examined the effect of stimulus repetition on the accuracy of small populations of IT neurons to classify familiar images of objects. We recorded multi-unit activity with a laminar electrode (16 sites spaced 100 µm) in the rostral ventral bank of the superior temporal sulcus in 2 rhesus monkeys (N = 32 penetrations) while they were performing a passive fixation task. We presented 2 stimuli (A and B) successively, each for 500 ms, with an ISI of 500 ms. Trials consisted either of a repetition (AA or BB) of the same stimulus or of their alternation (AB or BA). Stimulus-selective adaptation was present in 90% of stimulus x responsive site combinations (N = 468; see Kaliukhovich and Vogels, 2012 for details). Machine learning-based classifiers (Support Vector Machines (SVM) and correlation-based classifier) were employed to decode stimulus identity (A vs. B) from the neuronal population responses (spike counts in an interval of 60-310 ms after stimulus onset per trial and site) of each penetration. Classifiers were trained using half of the data and tested using the remaining half. The SVM classifier showed significantly lower classification for the test compared to the adapter stimulus (mean across penetrations: 80% vs. 74%, P < 0.000005) in repetition trials. However, classification for test stimuli in alternation trials and when only one of the stimuli was adapted (e.g. A following A vs. B following A) did not differ significantly from that of the adapter. The correlation-based classifier produced highly similar results. Shuffling trial order of a stimulus across sites within a penetration, i.e. eliminating noise correlations, and using the SVM classifier produced similar effects, except that classification was significantly larger by ~2% (P < 0.0005). These data suggest that stimulus-specific adaptation in IT does not improve coding of objects repeated in short stimulus sequences but instead may impair it. Acknowledgements This work was supported by Fonds voor Wetenschappelijk Onderzoek Vlaanderen (G.0644-08), Geconcerteerde Onderzoeksactie (GOA/10/019), Interuniversitaire Attractiepool (IUAP) and Programma Financiering (PF 10/008). For the computation of classifiers we used the infrastructure of the VSC – Flemish Supercomputer Center, funded by the Hercules Foundation and the Flemish Government – department EWI. References Kaliukhovich, D. A., & Vogels, R. (2012). Stimulus repetition affects both strength and synchrony of macaque inferior temporal cortical activity. Journal of Neurophysiology, 107, 3509-3527 Keywords: adaptation, inferior temporal cortex, stimulus discriminability, repetition suppression, macaque monkey Conference: Belgian Brain Council, Liège, Belgium, 27 Oct - 27 Oct, 2012. Presentation Type: Poster Presentation Topic: Higher Brain Functions in health and disease: cognition and memory Citation: Kaliukhovich DA and Vogels R (2012). Stimulus repetition reduces stimulus encoding by small neuronal populations in macaque inferior temporal cortex. Conference Abstract: Belgian Brain Council. doi: 10.3389/conf.fnhum.2012.210.00106 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Aug 2012; Published Online: 12 Sep 2012. * Correspondence: Dr. Rufin Vogels, KU Leuven, Neuroscience, Leuven, 3000, Belgium, rufin.vogels@kuleuven.be Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Dzmitry A Kaliukhovich Rufin Vogels Google Dzmitry A Kaliukhovich Rufin Vogels Google Scholar Dzmitry A Kaliukhovich Rufin Vogels PubMed Dzmitry A Kaliukhovich Rufin Vogels Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Dopaminergic modulation of synaptic transmission and neuronal activity patterns in the zebrafish homolog of olfactory cortex

Dopamine (DA) is an important modulator of synaptic transmission and plasticity that is causally involved in fundamental brain functions and dysfunctions. We examined the dopaminergic modulation of synaptic transmission and sensory responses in telencephalic area Dp of zebrafish, the homolog of olfactory cortex. By combining anatomical tracing and immunohistochemistry, we detected no DA neurons in Dp itself but long-range dopaminergic input from multiple other brain areas. Whole-cell recordings revealed no obvious effects of DA on membrane potential or input resistance in the majority of Dp neurons. Electrical stimulation of the olfactory tracts produced a complex sequence of synaptic currents in Dp neurons. DA selectively decreased inhibitory currents with little or no effect on excitatory components. Multiphoton calcium imaging showed that population responses of Dp neurons to olfactory tract stimulation or odor application were enhanced by DA, consistent with its effect on inhibitory synaptic transmission. These effects of DA were blocked by an antagonist of D2-like receptors. DA therefore disinhibits and reorganizes sensory responses in Dp. This modulation may affect sensory perception and could be involved in the experience-dependent modification of odor representations.

Representation of Dynamical Stimuli in Populations of Threshold Neurons

Many sensory or cognitive events are associated with dynamic current modulations in cortical neurons. This raises an urgent demand for tractable model approaches addressing the merits and limits of potential encoding strategies. Yet, current theoretical approaches addressing the response to mean- and variance-encoded stimuli rarely provide complete response functions for both modes of encoding in the presence of correlated noise. Here, we investigate the neuronal population response to dynamical modifications of the mean or variance of the synaptic bombardment using an alternative threshold model framework. In the variance and mean channel, we provide explicit expressions for the linear and non-linear frequency response functions in the presence of correlated noise and use them to derive population rate response to step-like stimuli. For mean-encoded signals, we find that the complete response function depends only on the temporal width of the input correlation function, but not on other functional specifics. Furthermore, we show that both mean- and variance-encoded signals can relay high-frequency inputs, and in both schemes step-like changes can be detected instantaneously. Finally, we obtain the pairwise spike correlation function and the spike triggered average from the linear mean-evoked response function. These results provide a maximally tractable limiting case that complements and extends previous results obtained in the integrate and fire framework.

Vibrational resonance in excitable neuronal systems

In this paper, we investigate the effect of a high-frequency driving on the dynamical response of excitable neuronal systems to a subthreshold low-frequency signal by numerical simulation. We demonstrate the occurrence of vibrational resonance in spatially extended neuronal networks. Different network topologies from single small-world networks to modular networks of small-world subnetworks are considered. It is shown that an optimal amplitude of high-frequency driving enhances the response of neuron populations to a low-frequency signal. This effect of vibrational resonance of neuronal systems depends extensively on the network structure and parameters, such as the coupling strength between neurons, network size, and rewiring probability of single small-world networks, as well as the number of links between different subnetworks and the number of subnetworks in the modular networks. All these parameters play a key role in determining the ability of the network to enhance the outreach of the localized subthreshold low-frequency signal. Considering that two-frequency signals are ubiquity in brain dynamics, we expect the presented results could have important implications for the weak signal detection and information propagation across neuronal systems.

Decoding the motion aftereffect in human visual cortex

Abstract In the motion aftereffect (MAE), adapting to a moving stimulus causes a subsequently presented stationary stimulus to appear to move in the opposite direction. Recently, the neural basis of the motion aftereffect has received considerable interest, and a number of brain areas have been implicated in the generation of the illusory motion. Here, we use functional magnetic resonance imaging in combination with multivariate pattern classification to directly compare the neural activity evoked during the observation of both real and illusory motions. We show that the perceived illusory motion is not encoded in the same way as real motion in the same direction. Instead, suppression of the adapted direction of motion results in a shift of the population response of motion sensitive neurons in area MT +, resulting in activation patterns that are in fact more similar to real motion in orthogonal, rather than opposite directions. Although robust motion selectivity was observed in visual areas V1, V2, V3, and V4, this MAE-specific modulation of the population response was only observed in area MT +. Implications for our understanding of the motion aftereffect, and models of motion perception in general, are discussed.

FMRI Repetition Suppression: Neuronal Adaptation or Stimulus Expectation?

Measurements of repetition suppression with functional magnetic resonance imaging (fMRI adaptation) have been used widely to probe neuronal population response properties in human cerebral cortex. fMRI adaptation techniques assume that fMRI repetition suppression reflects neuronal adaptation, an assumption that has been challenged on the basis of evidence that repetition-related response changes may reflect unrelated factors, such as attention and stimulus expectation. Specifically, Summerfield et al. (Summerfield C, Trittschuh EH, Monti JM, Mesulam MM, Egner T. 2008. Neural repetition suppression reflects fulfilled perceptual expectations. Nat Neurosci. 11:1004-1006) reported that the relative frequency of stimulus repetitions and non-repetitions influenced the magnitude of repetition suppression in the fusiform face area, suggesting that stimulus expectation accounted for most of the effect of repetition. We confirm that stimulus expectation can significantly influence fMRI repetition suppression throughout visual cortex and show that it occurs with long as well as short adaptation durations. However, the effect was attention dependent: When attention was diverted away from the stimuli, the effects of stimulus expectation completely disappeared. Nonetheless, robust and significant repetition suppression was still evident. These results suggest that fMRI repetition suppression reflects a combination of neuronal adaptation and attention-dependent expectation effects that can be experimentally dissociated. This implies that with an appropriate experimental design, fMRI adaptation can provide valid measures of neuronal adaptation and hence response specificity.

Cholinergic and glutamatergic agonists induce gamma frequency activity in dorsal subcoeruleus nucleus neurons

The dorsal subcoeruleus nucleus (SubCD) is involved in generating two signs of rapid eye movement (REM) sleep: muscle atonia and ponto-geniculo-occipital (PGO) waves. We tested the hypothesis that single cell and/or population responses of SubCD neurons are capable of generating gamma frequency activity in response to intracellular stimulation or receptor agonist activation. Whole cell patch clamp recordings (immersion chamber) and population responses (interface chamber) were conducted on 9- to 20-day-old rat brain stem slices. All SubCD neurons (n = 103) fired at gamma frequency when subjected to depolarizing steps. Two statistically distinct populations of neurons were observed, which were distinguished by their high (>80 Hz, n = 24) versus low (35-80 Hz, n = 16) initial firing frequencies. Both cell types exhibited subthreshold oscillations in the gamma range (n = 43), which may underlie the gamma band firing properties of these neurons. The subthreshold oscillations were blocked by the sodium channel blockers tetrodotoxin (TTX, n = 21) extracellularly and N-(2,6-dimethylphenylcarbamoylmethyl)triethylammonium bromide (QX-314) intracellularly (n = 5), indicating they were sodium channel dependent. Gamma frequency subthreshold oscillations were observed in response to the nonspecific cholinergic receptor agonist carbachol (CAR, n = 11, d = 1.08) and the glutamate receptor agonists N-methyl-d-aspartic acid (NMDA, n = 12, d = 1.09) and kainic acid (KA, n = 13, d = 0.96), indicating that cholinergic and glutamatergic inputs may be involved in the activation of these subthreshold currents. Gamma band activity also was observed in population responses following application of CAR (n = 4, P < 0.05), NMDA (n = 4, P < 0.05) and KA (n = 4, P < 0.05). Voltage-sensitive, sodium channel-dependent gamma band activity appears to be a part of the intrinsic membrane properties of SubCD neurons.

Differential Changes in Responses of Hypothalamic and Brainstem Neuronal Populations to Prolactin During Lactation in the Mouse

During lactation, there are numerous functional adaptations in the maternal brain. There is evidence that the high levels of circulating prolactin present during lactation might contribute to these adaptive changes. The present study aimed to investigate levels of functional prolactin-mediated signal transduction in the brain of lactating mice, using prolactin-induced phosphorylation of signal transducer and activator of transcription 5 (pSTAT5) as a marker, and compare these to the effect of exogenous prolactin during diestrus. On Day 7 of lactation, widespread induction of pSTAT5 was observed in numerous regions of the mouse forebrain and brainstem. In the medial preoptic nucleus, bed nuclei stria terminalis, paraventricular nucleus, and medial amygdala of the forebrain, and in the rostral periaqueductal gray, parabrachial nucleus, dorsal raphe, and the raphe obscurus nucleus of the brainstem, pSTAT5 expression was markedly increased during lactation compared with the response to exogenous prolactin during diestrus. In the anteroventral periventricular nucleus, arcuate nucleus, ventromedial nucleus, and dorsomedial nucleus, responses in lactation were comparable to diestrus. Conversely, in the area postrema of the brainstem, there was a reduction in response to prolactin, with a loss of pSTAT5 expression, during lactation. These differential responses following either acute or chronic elevations in prolactin were not accompanied by any changes in levels of prolactin receptor mRNA, when measured by in situ hybridization. These data are consistent with the hypothesis that prolactin might mediate widespread adaptive responses in the maternal brain.

Obtaining scalable and accurate classification in large-scale spatio-temporal domains

We present an approach for learning models that obtain accurate classification of data objects, collected in large-scale spatio-temporal domains. The model generation is structured in three phases: spatial dimension reduction, spatio-temporal features extraction, and feature selection. Novel techniques for the first two phases are presented, with two alternatives for the middle phase. We explore model generation based on the combinations of techniques from each phase. We apply the introduced methodology to data-sets from the Voltage-Sensitive Dye Imaging (VSDI) domain, where the resulting classification models successfully decode neuronal population responses in the visual cortex of behaving animals. VSDI is currently the best technique enabling simultaneous high spatial (10,000 points) and temporal (10 ms or less) resolution imaging from neuronal population in the cortex. We demonstrate that not only our approach is scalable enough to handle computationally challenging data, but it also contributes to the neuroimaging field of study with its decoding abilities. The effectiveness of our methodology is further explored on a data-set from the hurricanes domain, and a promising direction, based on the preliminary results of hurricane severity classification, is revealed.

Hedonic and nucleus accumbens neural responses to a natural reward are regulated by aversive conditioning

The nucleus accumbens (NAc) plays a role in hedonic reactivity to taste stimuli. Learning can alter the hedonic valence of a given stimulus, and it remains unclear how the NAc encodes this shift. The present study examined whether the population response of NAc neurons to a taste stimulus is plastic using a conditioned taste aversion (CTA) paradigm. Electrophysiological and electromyographic (EMG) responses to intraoral infusions of a sucrose (0.3 M) solution were made in naïve rats (Day 1). Immediately following the session, half of the rats (n = 6; Paired) received an injection of lithium chloride (0.15 M; i.p.) to induce malaise and establish a CTA while the other half (n = 6; Unpaired) received a saline injection. Days later (Day 5), NAc recordings during infusions of sucrose were again made. Electrophysiological and EMG responses to sucrose did not differ between groups on Day 1. For both groups, the majority of sucrose responsive neurons exhibited a decrease in firing rate (77% and 71% for Paired and Unpaired, respectively). Following conditioning, in Paired rats, EMG responses were indicative of aversion. Moreover, the majority of responsive NAc neurons now exhibited an increase in firing rate (69%). Responses in Unpaired rats were unchanged by the experience. Thus, the NAc differentially encodes the hedonic value of the same stimulus based on learned associations.

Tuning Curves for Movement Direction in the Human Visuomotor System

Neurons in macaque primary motor cortex (M1) are broadly tuned to arm movement direction. Recent evidence suggests that human M1 contains directionally tuned neurons, but it is unclear which other areas are part of the network coding movement direction and what characterizes the responses of neuronal populations in those areas. Such information would be highly relevant for the implementation of brain-computer interfaces (BCIs) in paralyzed patients. We used functional magnetic resonance imaging adaptation to identify which areas of the human brain show directional selectivity and the degree to which these areas are affected by the type of motor act (to press vs to grasp). After adapting participants to one particular hand movement direction, we measured the release from adaptation during occasional test trials, parametrically varying the angular difference between adaptation and test direction. We identified multiple areas broadly tuned to movement direction, including M1, dorsal premotor cortex, intraparietal sulcus, and the parietal reach region. Within these areas, we observed a gradient of directional selectivity, with highest directional selectivity in the right parietal reach region, for both right- and left-hand movements. Moreover, directional selectivity was modulated by the type of motor act to varying degrees, with the largest effect in M1 and the smallest modulation in the parietal reach region. These data provide an important extension of our knowledge about directional tuning in the human brain. Furthermore, our results suggest that the parietal reach region might be an ideal candidate for the implementation of BCI in paralyzed patients.

Gamma Band Unit Activity and Population Responses in the Pedunculopontine Nucleus

The pedunculopontine nucleus (PPN) is involved in the activated states of waking and paradoxical sleep, forming part of the reticular activating system (RAS). The studies described tested the hypothesis that single unit and/or population responses of PPN neurons are capable of generating gamma band frequency activity. Whole cell patch clamp recordings (immersion chamber) and population responses (interface chamber) were conducted on 9- to 20-day-old rat brain stem slices. Regardless of cell type (I, II, or III) or type of response to the nonselective cholinergic receptor agonist carbachol (excitation, inhibition, biphasic), almost all PPN neurons fired at gamma band frequency, but no higher, when subjected to depolarizing steps (50 +/- 2 Hz, mean +/- SE). Nonaccommodating neurons fired at 18-100 Hz throughout depolarizing steps, while most accommodating neurons exhibited gamma band frequency of action potentials followed by gamma band membrane oscillations. These oscillations were blocked by the sodium channel blocker tetrodotoxin (TTX), suggesting that at least some are mediated by sodium currents. Population responses in the PPN showed that carbachol induced peaks of activation in the theta and gamma range, while glutamatergic receptor agonists induced overall increases in activity at theta and gamma frequencies, although in differing patterns. Gamma band activity appears to be a part of the intrinsic membrane properties of PPN neurons, and the population as a whole generates different patterns of gamma band activity under the influence of specific transmitters. Given sufficient excitation, the PPN may impart gamma band activation on its targets.

In vivo single-cell excitability probing of neuronal ensembles in the intact and awake developing Xenopus brain

Sensory experience can elicit long-lasting plasticity of both single neurons and ensemble neural circuit response properties during embryonic development. To investigate their relationship, one must image functional responses of large neuronal populations simultaneously with single-cell resolution. In this protocol, we describe a noninvasive approach to assay functional plasticity of individual neurons and neuronal populations in vivo using targeted infusion of calcium-sensitive dyes, two-photon microscopy and synchronized visual stimuli presentations. This technique allows visualization of approximately 200 neurons while probing visual responses in the optic tectum of awake, immobilized Xenopus laevis tadpoles. The protocol includes visual training paradigms that elicit long-lasting potentiation or depression of functional responses, allowing investigations of population and single-neuron plasticity induced by natural sensory stimuli in the awake, intact, developing brain. Setup time for this protocol, including dye injection and chamber preparation, is approximately 2 h. Excitability probing experiments can then be performed for at least 3 h.

Novel Methods to Investigate Multi‐fiber Nerve Recordings

We developed novel methods to investigate responses of neuronal populations within a multi‐fiber sympathetic nerve recording. We applied these methods to analyze the relationship between barosensitive RVLM medullospinal neurons marked by their response to systemic administration of the gastrointestinal hormone cholecystokinin (CCK) and splanchnic and lumbar sympathetic nerve activity (SNA). In addition to the standard analysis of Spike‐Triggered Averaging (STA) of SNA, we used Spike2 software to identify spike‐like structures within the sympathetic nerve recording (SND). We then calculated cross‐correlograms between apparent “spikes” in SND and RVLM neuronal discharges. Also, we calculated a STA of RVLM mean frequency triggered by the identified “spikes”. These novel analyses yielded consistent results with the standard STA of SNA and detected sympathetic nerve discharges driven only by the fast pathway. We find statistically significant peaks (p&lt;0.001) in the cross‐correlogram between CCK inhibited neurons and “spikes” driven by the fast pathway (n=5). The time‐delay between RVLM neuronal discharges and the fast burst of SNA in splanchnic nerves is 52 +/− 2ms. The corresponding delay for lumbar nerves is 78+/− 4ms. These novel analyses may serve as a tool to study differential behavior of populations of neurons that drive sympathetic vasomotor nerve activity. Supported by R01 NS39774, HL091440

Vibrational resonance in neuron populations

In this paper different topologies of populations of FitzHugh-Nagumo neurons have been introduce to investigate the effect of high-frequency driving on the response of neuron populations to a subthreshold low-frequency signal. We show that optimal amplitude of high-frequency driving enhances the response of neuron populations to a subthreshold low-frequency input and the optimal amplitude dependences on the connection among the neurons. By analyzing several kinds of topology (i.e., random and small world) different behaviors have been observed. Several topologies behave in an optimal way with respect to the range of low-frequency amplitude leading to an improvement in the stimulus response coherence, while others with respect to the maximum values of the performance index. However, the best results in terms of both the suitable amplitude of high-frequency driving and high stimulus response coherence have been obtained when the neurons have been connected in a small-world topology.

Adaptation of Reward Sensitivity in Orbitofrontal Neurons

Animals depend on a large variety of rewards but their brains have a limited dynamic coding range. When rewards are uncertain, neuronal coding needs to cover a wide range of possible rewards. However, when reward is likely to occur within a specific range, focusing the sensitivity on the predicted range would optimize the discrimination of small reward differences. One way to overcome the trade-off between wide coverage and optimal discrimination is to adapt reward sensitivity dynamically to the available rewards. We investigated how changes in reward distribution influenced the coding of reward in the orbitofrontal cortex. Animals performed an oculomotor task in which a fixation cue predicted the SD of the probability distribution of juice volumes, while the expected mean volume was kept constant. A subsequent cue specified the exact juice volume obtained for a correct saccade response. Population responses of orbitofrontal neurons that reflected the predicted juice volume showed adaptation to the reward distribution. Statistical tests on individual responses revealed that a quarter of value-coding neurons shifted the reward sensitivity slope significantly between two reward distributions, whereas the remaining neurons showed insignificant change or lack of adaptation. Adaptations became more prominent when reward distributions changed less frequently, indicating time constraints for assessing reward distributions and adjusting neuronal sensitivity. The observed neuronal adaptation would optimize discrimination and contribute to the efficient coding of a large variety of potential rewards by neurons with limited dynamic range.