Population coding with correlation and an unfaithful model.

Neuronal representations of external events are often distributed across large populations of cells. We study the effect of correlated noise on the accuracy of these neuronal population codes. Our main question is whether the inherent error in the population code can be suppressed by increasing the size of the population N in the presence of correlated noise. We address this issue using a model of a population of neurons that are broadly tuned to an angular variable in two dimensions. The fluctuations in the neuronal activities are modeled as Gaussian noises with pairwise correlations that decay exponentially with the difference between the preferred angles of the correlated cells. We assume that the system is broadly tuned, which means that both the correlation length and the width of the tuning curves of the mean responses span a substantial fraction of the entire system length. The performance of the system is measured by the Fisher information (FI), which bounds its estimation error. By calculating the FI in the limit of a large N, we show that positive correlations decrease the estimation capability of the network, relative to the uncorrelated population. The information capacity saturates to a finite value as the number of cells in the population grows. In contrast, negative correlations substantially increase the information capacity of the neuronal population. These results are supplemented by the effect of correlations on the mutual information of the system. Our analysis provides an estimate of the effective number of statistically independent degrees of freedom, denoted N(eff), that a large correlated system can have. According to our theory N(eff) remains finite in the limit of a large N. Estimating the parameters of the correlations and tuning curves from experimental data in some cortical areas that code for angles, we predict that the number of effective degrees of freedom embedded in localized populations in these areas is less than or of the order of approximately 10(2).

Inducing Gamma Oscillations and Precise Spike Synchrony by Operant Conditioning via Brain-Machine Interface

Population coding is a simplified model of distributed information processing in the brain. This study investigates the performance and implementation of a sequential Bayesian decoding (SBD) paradigm in the framework of population coding. In the first step of decoding, when no prior knowledge is available, maximum likelihood inference is used; the result forms the prior knowledge of stimulus for the second step of decoding. Estimates are propagated sequentially to apply maximum a posteriori (MAP) decoding in which prior knowledge for any step is taken from estimates from the previous step. Not only do we analyze the performance of SBD, obtaining the optimal form of prior knowledge that achieves the best estimation result, but we also investigate its possible biological realization, in the sense that all operations are performed by the dynamics of a recurrent network. In order to achieve MAP, a crucial point is to identify a mechanism that propagates prior knowledge. We find that this could be achieved by short-term adaptation of network weights according to the Hebbian learning rule. Simulation results on both constant and time-varying stimulus support the analysis.

BackgroundBayesian phylogenetic inference holds promise as an alternative to maximum likelihood, particularly for large molecular-sequence data sets. We have investigated the performance of Bayesian inference with empirical and simulated protein-sequence data under conditions of relative branch-length differences and model violation.ResultsWith empirical protein-sequence data, Bayesian posterior probabilities provide more-generous estimates of subtree reliability than does the nonparametric bootstrap combined with maximum likelihood inference, reaching 100% posterior probability at bootstrap proportions around 80%. With simulated 7-taxon protein-sequence datasets, Bayesian posterior probabilities are somewhat more generous than bootstrap proportions, but do not saturate. Compared with likelihood, Bayesian phylogenetic inference can be as or more robust to relative branch-length differences for datasets of this size, particularly when among-sites rate variation is modeled using a gamma distribution. When the (known) correct model was used to infer trees, Bayesian inference recovered the (known) correct tree in 100% of instances in which one or two branches were up to 20-fold longer than the others. At ratios more extreme than 20-fold, topological accuracy of reconstruction degraded only slowly when only one branch was of relatively greater length, but more rapidly when there were two such branches. Under an incorrect model of sequence change, inaccurate trees were sometimes observed at less extreme branch-length ratios, and (particularly for trees with single long branches) such trees tended to be more inaccurate. The effect of model violation on accuracy of reconstruction for trees with two long branches was more variable, but gamma-corrected Bayesian inference nonetheless yielded more-accurate trees than did either maximum likelihood or uncorrected Bayesian inference across the range of conditions we examined. Assuming an exponential Bayesian prior on branch lengths did not improve, and under certain extreme conditions significantly diminished, performance. The two topology-comparison metrics we employed, edit distance and Robinson-Foulds symmetric distance, yielded different but highly complementary measures of performance.ConclusionsOur results demonstrate that Bayesian inference can be relatively robust against biologically reasonable levels of relative branch-length differences and model violation, and thus may provide a promising alternative to maximum likelihood for inference of phylogenetic trees from protein-sequence data.

Response variability is often correlated across populations of neurons, and these noise correlations may play a role in information coding. In previous studies, this possibility has been examined from the encoding and decoding perspectives. Here we used d prime and related information measures to examine how studies of noise correlations from these two perspectives are related. We found that for a pair of neurons, the effect of noise correlations on information decoding can be zero when the effect of noise correlations on the information encoded obtains its largest positive or negative values. Furthermore, there can be no effect of noise correlations on the information encoded when it has an effect on information decoding. We also measured the effect of noise correlations on information encoding and decoding in simultaneously recorded neurons in the supplementary motor area to see how well d prime accounted for the information actually present in the neural responses and to see how noise correlations affected encoding and decoding in real data. These analyses showed that d prime provides an accurate measure of information encoding and decoding in our population of neurons. We also found that the effect of noise correlations on information encoding was somewhat larger than the effect of noise correlations on information decoding, but both were relatively small. Finally, as predicted theoretically, the effects of correlations were slightly greater for larger ensembles (3-8 neurons) than for pairs of neurons.

Cholinesterases in cardiac ganglia and modulation of canine intrinsic cardiac neuronal activity

Histamine is a neurotransmitter that modulates neuronal activity and regulates various brain functions. Histamine H3 receptor (H3R) antagonists/inverse agonists enhance its release in most brain regions, including the cerebral cortex, which improves learning and memory and exerts an antiepileptic effect. However, the mechanism underlying the effect of H3R antagonists/inverse agonists on cortical neuronal activity in vivo remains unclear. Here, we show the mechanism by which pitolisant, an H3R antagonist/inverse agonist, influenced perirhinal cortex (PRh) activity in individual neuron and neuronal population levels. We monitored neuronal activity in the PRh of freely moving mice using in vivo Ca2+ imaging through a miniaturized one-photon microscope. Pitolisant increased the activity of some PRh neurons while decreasing the activity of others without affecting the mean neuronal activity across neurons. Moreover, it increases neuron pairs with synchronous activity in excitatory-responsive neuronal populations. Furthermore, machine learning analysis revealed that pitolisant altered the neuronal population activity. The changes in the population activity were dependent on the neurons that were excited and inhibited by pitolisant treatment. These findings indicate that pitolisant influences the activity of a subset of PRh neurons by increasing the synchronous activity and modifying the population activity.

The immunopathological states of the brain induced by bacterial lipoproteins have been well characterized by using biochemical and histological assays. However, these studies have limitations in determining functional states of damaged brains involving aberrant synaptic activity and network, which makes it difficult to diagnose brain disorders during bacterial infection. To address this, we investigated the effect of Pam3CSK4 (PAM), a synthetic bacterial lipopeptide, on synaptic dysfunction of female mice brains and cultured neurons in parallel. Our functional brain imaging using PET with [18F]fluorodeoxyglucose and [18F] flumazenil revealed that the brain dysfunction induced by PAM is closely aligned to disruption of neurotransmitter-related neuronal activity and functional correlation in the region of the limbic system rather than to decrease of metabolic activity of neurons in the injection area. This finding was verified by in vivo tissue experiments that analyzed synaptic and dendritic alterations in the regions where PET imaging showed abnormal neuronal activity and network. Recording of synaptic activity also revealed that PAM reorganized synaptic distribution and decreased synaptic plasticity in hippocampus. Further study using in vitro neuron cultures demonstrated that PAM decreased the number of presynapses and the frequency of miniature EPSCs, which suggests PAM disrupts neuronal function by damaging presynapses exclusively. We also showed that PAM caused aggregation of synapses around dendrites, which may have caused no significant change in expression level of synaptic proteins, whereas synaptic number and function were impaired by PAM. Our findings could provide a useful guide for diagnosis and treatment of brain disorders specific to bacterial infection.SIGNIFICANCE STATEMENT It is challenging to diagnose brain disorders caused by bacterial infection because neural damage induced by bacterial products involves nonspecific neurological symptoms, which is rarely detected by laboratory tests with low spatiotemporal resolution. To better understand brain pathology, it is essential to detect functional abnormalities of brain over time. To this end, we investigated characteristic patterns of altered neuronal integrity and functional correlation between various regions in mice brains injected with bacterial lipopeptides using PET with a goal to apply new findings to diagnosis of brain disorder specific to bacterial infection. In addition, we analyzed altered synaptic density and function using both in vivo and in vitro experimental models to understand how bacterial lipopeptides impair brain function and network.

Obesity and type 2 diabetes mellitus (T2DM) are associated with altered food-related neuronal functions. Besides weight loss, substantial improvement of glucose metabolism in patients with T2DM can be achieved by bariatric surgery. We aimed to target the neuronal and behavioral correlates of improved glycemic control after bariatric surgery. Two patient groups with T2DM were recruited. The treatment group (n = 12) consisted of patients who had undergone Roux-en-Y gastric bypass (RYGB) surgery, and a control group consisted of patients who did not undergo surgery (n = 12). The groups were matched for age and current BMI. HbA1c was matched by using the presurgical HbA1c of the RYGB group and the current HbA1c of the nonsurgical group. Neuronal activation during a food reward task was measured using functional MRI (fMRI). Behavioral data were assessed through questionnaires. RYGB improved HbA1c from 7.07 ± 0.50 to 5.70 ± 0.16% (P < 0.05) and BMI from 52.21 ± 1.90 to 35.71 ± 0.84 kg/m(2) (P < 0.001). Behavioral results showed lower wanting and liking scores as well as lower eating behavior-related pathologies for the patients after RYGB than for similar obese subjects without surgery but with impaired glycemic control. The fMRI analysis showed higher activation for the nonsurgical group in areas associated with inhibition and reward as well as in the precuneus, a major connectivity hub in the brain. By contrast, patients after RYGB showed higher activation in the visual, motor, cognitive control, memory, and gustatory regions. In obese patients with diabetes, RYGB normalizes glycemic control and leads to food reward-related brain activation patterns that are different from those of obese patients with less-well-controlled T2DM and without bariatric surgery. The differences in food reward processing might be one factor in determining the outcome of bariatric surgery in patients with T2DM.

Differential Neural Activation for Updating Rule versus Stimulus Information in Working Memory

A method to establish the spatiotemporal evolution of task-related cortical activity from electrocorticographic signals in single trials

Neural population coding and auditory temporal pattern analysis

The purpose of this paper is to incorporate semiparametric alternatives to maximum likelihood estimation and inference in the context of unordered multinomial response data when in practice there is often insufficient information to specify the parametric form of the function linking the observables to the unknown probabilities. We specify the function linking the observables to the unknown probabilities using a very general flexible class of functions belonging to the Pearson system of cumulative distribution equations. In this setting we consider the observations as arising from a multinomial distribution characterized by one of the CDFs in the Pearson system. Given this situation, it is possible to utilize the concept of unbiased estimating functions (EFs), combined with the concept of empirical likelihood (EL) to define an (empirical) likelihood function for the parameter vector based on a nonparametric representation of the sample’s PDF. This leads to the concept of maximum empirical likelihood (MEL) estimation and inference, which is analogous to parametric maximum likelihood methods in many respects.

Natural stimuli display spatiotemporal characteristics that typically vary over orders of magnitude, and their encoding by sensory neurons remains poorly understood. We investigated population coding of highly heterogeneous natural electrocommunication stimuli in Apteronotus leptorhynchus of either sex. Neuronal activities were positively correlated with one another in the absence of stimulation, and correlation magnitude decayed with increasing distance between recording sites. Under stimulation, we found that correlations between trial-averaged neuronal responses (i.e., signal correlations) were positive and higher in magnitude for neurons located close to another, but that correlations between the trial-to-trial variability (i.e., noise correlations) were independent of physical distance. Overall, signal and noise correlations were independent of stimulus waveform as well as of one another. To investigate how neuronal populations encoded natural electrocommunication stimuli, we considered a nonlinear decoder for which the activities were combined. Decoding performance was best for a timescale of 6 ms, indicating that midbrain neurons transmit information via precise spike timing. A simple summation of neuronal activities (equally weighted sum) revealed that noise correlations limited decoding performance by introducing redundancy. Using an evolution algorithm to optimize performance when considering instead unequally weighted sums of neuronal activities revealed much greater performance values, indicating that midbrain neuron populations transmit information that reliably enable discrimination between different stimulus waveforms. Interestingly, we found that different weight combinations gave rise to similar discriminability, suggesting robustness. Our results have important implications for understanding how natural stimuli are integrated by downstream brain areas to give rise to behavioral responses.SIGNIFICANCE STATEMENT We show that midbrain electrosensory neurons display correlations between their activities and that these can significantly impact performance of decoders. While noise correlations limited discrimination performance by introducing redundancy, considering unequally weighted sums of neuronal activities gave rise to much improved performance and mitigated the deleterious effects of noise correlations. Further analysis revealed that increased discriminability was achieved by making trial-averaged responses more separable, as well as by reducing trial-to-trial variability by eliminating noise correlations. We further found that multiple combinations of weights could give rise to similar discrimination performances, which suggests that such combinatorial codes could be achieved in the brain. We conclude that the activities of midbrain neuronal populations can be used to reliably discriminate between highly heterogeneous stimulus waveforms.

Event Abstract Back to Event Timing precision in population coding of natural scenes in the early visual system The precision of neuronal spike trains is at the center of a fundamental debate in neuroscience as to what aspects of neuronal signaling are important in representing information in the brain. Individual neurons can have extremely precise and repeatable responses to the visual stimuli that strongly drive them (down to 1-ms variability), but they exhibit seemingly degraded temporal precision in response to suboptimal stimuli. In the presence of natural scenes, the activity of individual neurons is precisely timed across repeated presentations of the visual stimulus, even though natural stimuli tend to vary on a time scale that is several times slower. However, in most natural circumstances, the brain does not have access to multiple repetitions of the same identical stimulus, and, therefore, it is the precision of spiking across neuronal sub-populations on single trials that is ethologically relevant. While synchrony across neurons in the retina and visual cortex has been reported at various time scales, which can depend on the visual stimulus, the temporal precision of the neural code directly entering primary visual cortex, and its dependence on the stimulus, are still unknown. In this study (Desbordes et al., PLoS Biology, in press), we used natural visual stimuli to investigate spike timing precision in populations of geniculate neurons that serve as the direct input to visual cortex. A short movie of a natural scene recorded from a “cat-cam” (Kayser et al., 2003) was presented repeatedly to anesthetized cats while recording extracellular activity of multiple single units in the lateral geniculate nucleus (LGN) in vivo. To test how spike timing precision was affected by the properties of the visual stimulus, the same movie was presented both at high contrast and at low contrast. The absolute timing of firing events (or groups of closely spaced spikes) changed from trial to trial, and more so at low contrast than at high contrast, indicating that the response of individual neurons is less precise across stimulus repetitions when contrast is reduced. However, the relative timing of spikes occurring in the same trial was insensitive to changes in stimulus contrast - not only within cells but also across neighboring cells. At the population level, spike timing precision across LGN cells remained on the order of ~10 ms, irrespective of contrast. While it is well known that the response properties of single cells are strongly modulated by contrast adaptation, which has effects including slower temporal dynamics and increased gain and selectivity at lower contrast, our results indicate that the temporal precision of the LGN population code is globally maintained in the face of a reduction in contrast. Since closely timed spikes are more likely to induce a spike in the downstream cortical neuron to which they are projecting (Alonso et al., Nature, 1996), and since fine temporal precision is necessary in representing the more slowly varying natural environment (Butts et al., Nature, 2007), preserving the relative timing of spikes at a resolution of ~10 ms may be a crucial aspect of the neural code entering primary visual cortex. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Timing precision in population coding of natural scenes in the early visual system. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.325 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: 04 Feb 2009; Published Online: 04 Feb 2009. 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 Google Google Scholar PubMed 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.