An executive-functions-based reading training enhances sensory-motor systems integration during reading fluency in children with dyslexia

The Simple View of Reading model suggests that intact language processing and word decoding lead to proficient reading comprehension, with recent studies pointing at executive functions as an important component contributing to reading proficiency. Here, we aimed to determine the underlying mechanism(s) for these changes. Participants include 120 8- to 12-year-old children (n = 55 with dyslexia, n = 65 typical readers) trained on an executive functions-based reading program, including pre/postfunctional MRI and behavioral data collection. Across groups, improved word reading was related to stronger functional connections within executive functions and sensory networks. In children with dyslexia, faster and more accurate word reading was related to stronger functional connections within and between sensory networks. These results suggest greater synchronization of brain systems after the intervention, consistent with the "neural noise" hypothesis in children with dyslexia and support the consideration of including executive functions as part of the Simple View of Reading model.

Interindividual aperiodic resting-state EEG activity predicts cognitive-control styles

The ability to find the right balance between more persistent and more flexible cognitive-control styles is known as "metacontrol." Recent findings suggest a relevance of aperiodic EEG activity and task conditions that are likely to elicit a specific metacontrol style. Here we investigated whether individual differences in aperiodic EEG activity obtained off-task (during resting state) predict individual cognitive-control styles under task conditions that pose different demands on metacontrol. We analyzed EEG resting-state data, task-EEG, and behavioral outcomes from a sample of N = 65 healthy participants performing a Go/Nogo task. We examined aperiodic activity as indicator of "neural noise" in the EEG power spectrum, and participants were assigned to a high-noise or low-noise group according to a median split of the exponents obtained for resting state. We found that off-task aperiodic exponents predicted different cognitive-control styles in Go and Nogo conditions: Overall, aperiodic exponents were higher (i.e., noise was lower) in the low-noise group, who however showed no difference between Go and Nogo trials, whereas the high-noise group exhibited significant noise reduction in the more persistence-heavy Nogo condition. This suggests that trait-like biases determine the default cognitive-control style, which however can be overwritten or compensated for under challenging task demands. We suggest that aperiodic activity in EEG signals represents valid indicators of highly dynamic arbitration between metacontrol styles, representing the brain's capability to reorganize itself and adapt its neural activity patterns to changing environmental conditions.

Decoding the Spike-Band Subthreshold Motor Cortical Activity

Intracortical Brain-Computer Interfaces (iBCI) use single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP) to control neuroprosthetic devices. SUA and MUA are usually extracted from the bandpassed recording through amplitude thresholding, while subthreshold data are ignored. Here, we show that subthreshold data can actually be decoded to determine behavioral variables with test set accuracy of up to 100%. Although the utility of SUA, MUA and LFP for decoding behavioral variables has been explored previously, this study investigates the utility of spike-band subthreshold activity exclusively. We provide evidence suggesting that this activity can be used to keep decoding performance at acceptable levels even when SUA quality is reduced over time. To the best of our knowledge, the signals that we derive from the subthreshold activity may be the weakest neural signals that have ever been extracted from extracellular neural recordings, while still being decodable with test set accuracy of up to 100%. These results are relevant for the development of fully data-driven and automated methods for amplitude thresholding spike-band extracellular neural recordings in iBCIs containing thousands of electrodes.

Delta-9-Tetrahydrocannabinol, Cannabidiol, and Acute Psychotomimetic States: A Balancing Act of the Principal Phyto-Cannabinoids on Human Brain and Behavior

Background: THC and CBD are the principal phyto-cannabinoids in the cannabis plant. The differential and possibly antagonistic effects of these compounds on specific brain and behavioral responses, and the mechanisms underlying their effects have generated extensive interest in pre-clinical and clinical neuroscience investigations. Methods: In this double-blind randomized placebo-controlled counterbalanced Human Laboratory Study, we examined the effects of three different dose ratios of CBD:THC (1:1, 2:1, and 3:1) on "neural noise," an electrophysiological biomarker of psychosis known to be sensitive to cannabinoids as well as subjective and psychotomimetic effects. Healthy volunteers (n=28, 12 women) with at least one prior exposure to cannabis participated in the study. Outcomes: The lowest CBD (2.5 mg):THC (0.035 mg/kg) ratio (1:1) resulted in maximal attenuation of both THC-induced psychotomimetic effects (Positive and Negative Syndrome Scale [PANSS] positive: Anova Type Statistic [ATS]=7.83, pcorrected=0.015) and neural noise (ATS=8.83, pcorrected=0.009). Further addition of CBD did not reduce the subjective experience of THC-induced "high" (p>0.05 for all CBD doses). Interpretation: These novel results demonstrate that CBD attenuates specific THC-induced subjective and objective effects relevant to psychosis in a dose/ratio-dependent manner. Given the increasing global trend of cannabis liberalization and application for medical indications, these results assume considerable significance given the potential dose-related interactions of these key phyto-cannabinoids. Trial registration: The trial was registered in clinicaltrials.gov ID: NCT01180374.

Subtractive adaptation is a more effective and general mechanism in binocular rivalry than divisive adaptation

The activity of neurons is influenced by random fluctuations and can be strongly modulated by firing rate adaptation, particularly in sensory systems. Still, there is ongoing debate about the characteristics of neuronal noise and the mechanisms of adaptation, and even less is known about how exactly they affect perception. Noise and adaptation are critical in binocular rivalry, a visual phenomenon where two images compete for perceptual dominance. Here, we investigated the effects of different noise processes and adaptation mechanisms on visual perception by simulating a model of binocular rivalry with Gaussian white noise, Ornstein-Uhlenbeck noise, and pink noise, in variants with divisive adaptation, subtractive adaptation, and without adaptation. By simulating the nine models in parameter space, we find that white noise only produces rivalry when paired with subtractive adaptation and that subtractive adaptation reduces the influence of noise intensity on rivalry strength and introduces convergence of the mean percept duration, an important metric of binocular rivalry, across all noise processes. In sum, our results show that white noise is an insufficient description of background activity in the brain and that subtractive adaptation is a stronger and more general switching mechanism in binocular rivalry than divisive adaptation, with important noise-filtering properties.

Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy

Introduction

Investigating the pathological mechanisms of developmental disorders is a challenge because the symptoms are a result of complex and dynamic factors such as neural networks, cognitive behavior, environment, and developmental learning. Recently, computational methods have started to provide a unified framework for understanding developmental disorders, enabling us to describe the interactions among those multiple factors underlying symptoms. However, this approach is still limited because most studies to date have focused on cross-sectional task performance and lacked the perspectives of developmental learning. Here, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as in silico neurodevelopment framework for atypical representation learning.

Methods

Simple simulation experiments were conducted using the proposed framework to examine whether manipulating the neural stochasticity and noise levels in external environments during the learning process can lead to the altered acquisition of hierarchical Bayesian representation and reduced flexibility.

Results

Networks with normal neural stochasticity acquired hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good behavioral and cognitive flexibility. When the neural stochasticity was high during learning, top-down generation using higher-order representation became atypical, although the flexibility did not differ from that of the normal stochasticity settings. However, when the neural stochasticity was low in the learning process, the networks demonstrated reduced flexibility and altered hierarchical representation. Notably, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli.

Discussion

These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment.

Does stochastic resonance improve performance for individuals with higher autism-spectrum quotient?

While noise is generally believed to impair performance, the detection of weak stimuli can sometimes be enhanced by introducing optimum noise levels. This phenomenon is termed 'Stochastic Resonance' (SR). Past evidence suggests that autistic individuals exhibit higher neural noise than neurotypical individuals. It has been proposed that the enhanced performance in Autism Spectrum Disorder (ASD) on some tasks could be due to SR. Here we present a computational model, lab-based, and online visual identification experiments to find corroborating evidence for this hypothesis in individuals without a formal ASD diagnosis. Our modeling predicts that artificially increasing noise results in SR for individuals with low internal noise (e.g., neurotypical), however not for those with higher internal noise (e.g., autistic, or neurotypical individuals with higher autistic traits). It also predicts that at low stimulus noise, individuals with higher internal noise outperform those with lower internal noise. We tested these predictions using visual identification tasks among participants from the general population with autistic traits measured by the Autism-Spectrum Quotient (AQ). While all participants showed SR in the lab-based experiment, this did not support our model strongly. In the online experiment, significant SR was not found, however participants with higher AQ scores outperformed those with lower AQ scores at low stimulus noise levels, which is consistent with our modeling. In conclusion, our study is the first to investigate the link between SR and superior performance by those with ASD-related traits, and reports limited evidence to support the high neural noise/SR hypothesis.

Objective frequency analysis of transient visual evoked potentials in autistic children

Visual evoked potentials (VEPs) provide a means to examine neural mechanisms in autism with high temporal resolution. Conventional VEP analysis relies on subjective inspection of a few points (peaks and troughs) in the time-domain waveform. The current study applied power spectral analysis and magnitude-squared coherence (MSC) statistics (frequency-domain measures) to VEPs recorded during 1-minute runs and with a recently developed short-duration technique that allow for objective examination of the responses (Zemon & Gordon, European Journal of Neuroscience, 2018, 48, 1765-1788) from nonautistic and autistic children. Results indicate that, for both groups, early time-domain measures (P₆₀ , N₇₅ , P₁₀₀ ) are highly correlated with middle- and high-frequency (14-28 and 30-48 Hz, respectively) mechanisms, and late measures are highly correlated with a low-frequency (6-12 Hz) mechanism. One frequency-domain measure (power in the middle-frequency band) is capable of predicting the key amplitude measure (N₇₅ -P₁₀₀ ) with high accuracy. MSC and power measures were combined to yield separate measures of signal and noise strength to evaluate alternate hypotheses in autism. Linear mixed-effects modeling demonstrated selective differences in early time-domain and middle-to-high frequency-domain measures in autistic children as compared to nonautistic children given both recording techniques, implicating weaker excitatory input to the cortex. Receiver-operating-characteristic curve analysis showed predictive diagnostic accuracy for middle- and high-frequency bands based on MSC. These findings support the value of frequency analysis measures (power spectral analysis and MSC) in the objective examination of neural differences in autism. LAY SUMMARY: Visual evoked potentials (VEPs) are used to assess neural mechanisms. Typically, VEPs are analyzed by subjective examination of time-series waveforms; but here objective techniques were applied to quantify VEP frequency components to investigate neural differences between autistic and nonautistic children. The objective measures demonstrate group differences in brain function that point to weaker excitatory input to the cortex in autism.

Numerical Simulation: Fluctuation in Background Synaptic Activity Regulates Synaptic Plasticity

Synaptic plasticity is vital for learning and memory in the brain. It consists of long-term potentiation (LTP) and long-term depression (LTD). Spike frequency is one of the major components of synaptic plasticity in the brain, a noisy environment. Recently, we mathematically analyzed the frequency-dependent synaptic plasticity (FDP) in vivo and found that LTP is more likely to occur with an increase in the frequency of background synaptic activity. Meanwhile, previous studies suggest statistical fluctuation in the amplitude of background synaptic activity. Little is understood, however, about its contribution to synaptic plasticity. To address this issue, we performed numerical simulations of a calcium-based synapse model. Then, we found attenuation of the tendency to become LTD due to an increase in the fluctuation of background synaptic activity, leading to an enhancement of synaptic weight. Our result suggests that the fluctuation affects synaptic plasticity in the brain.

No Evidence of Reduced Contrast Sensitivity in Migraine-with-Aura for Large, Narrowband, Centrally Presented Noise-Masked Stimuli

Individuals with migraine aura show differences in visual perception compared to control groups. Measures of contrast sensitivity have suggested that people with migraine aura are less able to exclude external visual noise, and that this relates to higher variability in neural processing. The current study compared contrast sensitivity in migraine with aura and control groups for narrow-band grating stimuli at 2 and 8 cycles/degree, masked by Gaussian white noise. We predicted that contrast sensitivity would be lower in the migraine with aura group at high noise levels. Contrast sensitivity was higher for the low spatial frequency stimuli, and decreased with the strength of the masking noise. We did not, however, find any evidence of reduced contrast sensitivity associated with migraine with aura. We propose alternative methods as a more targeted assessment of the role of neural noise and excitability as contributing factors to migraine aura.

An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data

Drift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches only yield reliable parameter estimates in specific situations (c.f. fixed drift rates vs drift rates varying over trials). In addition, they become computationally unfeasible when more general DDM variants are considered (e.g., with collapsing bounds). In this note, we propose a fast and efficient approach to parameter estimation that relies on fitting a "self-consistency" equation that RT fulfill under the DDM. This effectively bypasses the computational bottleneck of standard DDM parameter estimation approaches, at the cost of estimating the trial-specific neural noise variables that perturb the underlying evidence accumulation process. For the purpose of behavioral data analysis, these act as nuisance variables and render the model "overcomplete," which is finessed using a variational Bayesian system identification scheme. However, for the purpose of neural data analysis, estimates of neural noise perturbation terms are a desirable (and unique) feature of the approach. Using numerical simulations, we show that this "overcomplete" approach matches the performance of current parameter estimation approaches for simple DDM variants, and outperforms them for more complex DDM variants. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.

Voltage distributions in extracellular brain recordings

Extracellular recordings of brain voltage signals have many uses, including the identification of spikes and the characterization of brain states via analysis of local field potential (LFP) or EEG recordings. Though the factors underlying the generation of these signals are time varying and complex, their analysis may be facilitated by an understanding of their statistical properties. To this end, we analyzed the voltage distributions of high-pass extracellular recordings from a variety of structures, including cortex, thalamus, and hippocampus, in monkeys, cats, and rodents. We additionally investigated LFP signals in these recordings as well as human EEG signals obtained during different sleep stages. In all cases, the distributions were accurately described by a Gaussian within ±1.5 standard deviations from zero. Outside these limits, voltages tended to be distributed exponentially, that is, they fell off linearly on log-linear frequency plots, with variable heights and slopes. A possible explanation for this is that sporadically and independently occurring events with individual Gaussian size distributions can sum to produce approximately exponential distributions. For the high-pass recordings, a second explanation results from a model of the noisy behavior of ion channels that produce action potentials via Hodgkin-Huxley kinetics. The distributions produced by this model, relative to the averaged potential, were also Gaussian with approximately exponential flanks. The model also predicted time-varying noise distributions during action potentials, which were observed in the extracellular spike signals. These findings suggest a principled method for detecting spikes in high-pass recordings and transient events in LFP and EEG signals.NEW & NOTEWORTHY We show that the voltage distributions in brain recordings, including high-pass extracellular recordings, the LFP, and human EEG, are accurately described by a Gaussian within ±1.5 standard deviations from zero, with heavy, exponential tails outside these limits. This offers a principled way of setting event detection thresholds in high-pass recordings. It also offers a means for identifying event-like, transient signals in LFP and EEG recordings which may correlate with other neural phenomena.

Beyond Reading Modulation: Temporo-Parietal tDCS Alters Visuo-Spatial Attention and Motion Perception in Dyslexia

Dyslexia is a neurodevelopmental disorder with an atypical activation of posterior left-hemisphere brain reading networks (i.e., temporo-occipital and temporo-parietal regions) and multiple neuropsychological deficits. Transcranial direct current stimulation (tDCS) is a tool for manipulating neural activity and, in turn, neurocognitive processes. While studies have demonstrated the significant effects of tDCS on reading, neurocognitive changes beyond reading modulation have been poorly investigated. The present study aimed at examining whether tDCS on temporo-parietal regions affected not only reading, but also phonological skills, visuo-spatial working memory, visuo-spatial attention, and motion perception in a polarity-dependent way. In a within-subjects design, ten children and adolescents with dyslexia performed reading and neuropsychological tasks after 20 min of exposure to Left Anodal/Right Cathodal (LA/RC) and Right Anodal/Left Cathodal (RA/LC) tDCS. LA/RC tDCS compared to RA/LC tDCS improved text accuracy, word recognition speed, motion perception, and modified attentional focusing in our group of children and adolescents with dyslexia. Changes in text reading accuracy and word recognition speed—after LA/RC tDCS compared to RA/LC—were related to changes in motion perception and in visuo-spatial working memory, respectively. Our findings demonstrated that reading and domain-general neurocognitive functions in a group of children and adolescents with dyslexia change following tDCS and that they are polarity-dependent.

Visual Noise Effect on Contour Integration and Gaze Allocation in Autism Spectrum Disorder

Contradictory results have been obtained in the studies that compare contour integration abilities in Autism Spectrum Disorders (ASDs) and typically developing individuals. The present study aimed to explore the limiting factors of contour integration ability in ASD and verify the role of the external visual noise by a combination of psychophysical and eye-tracking approaches. To this aim, 24 children and adolescents with ASD and 32 age-matched participants with typical development had to detect the presence of contour embedded among similar Gabor elements in a Yes/No procedure. The results obtained showed that the responses in the group with ASD were not only less accurate but also were significantly slower compared to the control group at all noise levels. The detection performance depended on the group differences in addition to the effect of the intellectual functioning of the participants from both groups. The comparison of the agreement and accuracy of the responses in the double-pass experiment showed that the results of the participants with ASD are more affected by the increase of the external noise. It turned out that the internal noise depends on the level of the added external noise: the difference between the two groups was non-significant at the low external noise and significant at the high external noise. In accordance with the psychophysical results, the eye-tracking data indicated a larger gaze allocation area in the group with autism. These findings may imply higher positional uncertainty in ASD due to the inability to maintain the information of the contour location from previous presentations and interference from noise elements in the contour vicinity. Psychophysical and eye-tracking data suggest lower efficiency in using stimulus information in the ASD group that could be caused by fixation instability and noisy and unstable perceptual template that affects noise filtering.

Characterizing pink and white noise in the human electroencephalogram

OBJECTIVE

The power spectrum of the human electroencephalogram (EEG) as a function of frequency is a mix of brain oscillations (e.g. alpha activity around 10 Hz) and non-oscillations or noise of uncertain origin. "White noise" is uniformly distributed over frequency, while "pink noise" has an inverse power-frequency relation (power ∝ 1/f). Interest in EEG pink noise has been growing, but previous human estimates appear methodologically flawed. We propose a new approach to extract separate valid estimates of pink and white noise from an EEG power spectrum.

APPROACH

We use simulated data to demonstrate its effectiveness compared with established procedures, and provide an illustrative example from a new resting eyes-open (EO) and eyes-closed (EC) dataset. The topographic characteristics of the obtained pink and white noise estimates are examined, as is the alpha power in this sample.

MAIN RESULTS

Valid pink and white noise estimates were successfully obtained for each of our 5400 individual spectra (60 participants × 30 electrodes × 3 conditions/blocks [EO1, EC, EO2]). The 1/f noise had a distinct central scalp topography, and white noise was occipital in distribution, both differing from the parietal topography of the alpha oscillation. These differences point to their separate neural origins. EC pink and white noise powers were globally greater than in EO.

SIGNIFICANCE

This valid estimation of pink and white noise in the human EEG holds promise for more accurate assessment of oscillatory neural activity in both typical and clinical groups, such as those with attention deficits. Further, outside the human EEG, the new methodology can be generalized to remove noise from spectra in many fields of science and technology.

GABA, noise and gain in human visual cortex

High levels of GABA (gamma-aminobutyric acid, the brain's primary inhibitory neurotransmitter) are associated with enhanced cognitive and perceptual performance. It has been proposed that these effects result from GABA reducing neural noise or variability, but the precise mechanisms remain unknown. We have measured how individual differences in GABA concentration in the visual cortex are related to performance on a visual contrast discrimination task. Our results reveal that the facilitatory strength of the typical "dipper" function elicited by this task is strongly correlated with GABA concentration. A simple, biologically plausible, network model comprising excitatory and suppressive neural populations accounts for the data well and indicates that the strength of suppression increases as GABA concentration increases. Inter-individual variations in GABA were correlated both with the inhibition strength of the model (mimicking the effect of GABA) and, inversely, with the magnitude of the response criterion. This enhanced suppression has the dual effect of suppressing noise and reducing the gain of the neural response. Our findings thus suggest that the changes in performance conferred by high GABA concentration are mediated by both a reduction of noise and, paradoxically, a reduction in neural, but not perceptual, sensitivity.

Tinnitus Does Not Interfere with Auditory and Speech Perception

Tinnitus is a sound heard by 15% of the general population in the absence of any external sound. Because external sounds can sometimes mask tinnitus, tinnitus is assumed to affect the perception of external sounds, leading to hypotheses such as "tinnitus filling in the temporal gap" in animal models and "tinnitus inducing hearing difficulty" in human subjects. Here we compared performance in temporal, spectral, intensive, masking and speech-in-noise perception tasks between 45 human listeners with chronic tinnitus (18 females and 27 males with a range of ages and degrees of hearing loss) and 27 young, normal-hearing listeners without tinnitus (11 females and 16 males). After controlling for age, hearing loss, and stimulus variables, we discovered that, contradictory to the widely held assumption, tinnitus does not interfere with the perception of external sounds in 32 of the 36 measures. We interpret the present result to reflect a bottom-up pathway for the external sound and a separate top-down pathway for tinnitus. We propose that these two perceptual pathways can be independently modulated by attention, which leads to the asymmetrical interaction between external and internal sounds, and several other puzzling tinnitus phenomena such as discrepancy in loudness between tinnitus rating and matching. The present results suggest not only a need for new theories involving attention and central noise in animal tinnitus models but also a shift in focus from treating tinnitus to managing its comorbid conditions when addressing complaints about hearing difficulty in individuals with tinnitus.SIGNIFICANCE STATEMENT Tinnitus, or ringing in the ears, is a neurologic disorder that affects 15% of the general population. Here we discovered an asymmetrical relationship between tinnitus and external sounds: although external sounds have been widely used to cover up tinnitus, tinnitus does not impair, and sometimes even improves, the perception of external sounds. This counterintuitive discovery contradicts the general belief held by scientists, clinicians, and even individuals with tinnitus themselves, who often report hearing difficulty, especially in noise. We attribute the counterintuitive discovery to two independent pathways: the bottom-up perception of external sounds and the top-down perception of tinnitus. Clinically, the present work suggests a shift in focus from treating tinnitus itself to treating its comorbid conditions and secondary effects.

Neuronal Transmission of Subthreshold Periodic Stimuli Via Symbolic Spike Patterns

We study how sensory neurons detect and transmit a weak external stimulus. We use the FitzHugh-Nagumo model to simulate the neuronal activity. We consider a sub-threshold stimulus, i.e., the stimulus is below the threshold needed for triggering action potentials (spikes). However, in the presence of noise the neuron that perceives the stimulus fires a sequence of action potentials (a spike train) that carries the stimulus' information. To yield light on how the stimulus' information can be encoded and transmitted, we consider the simplest case of two coupled neurons, such that one neuron (referred to as neuron 1) perceives a subthreshold periodic signal but the second neuron (neuron 2) does not perceive the signal. We show that, for appropriate coupling and noise strengths, both neurons fire spike trains that have symbolic patterns (defined by the temporal structure of the inter-spike intervals), whose frequencies of occurrence depend on the signal's amplitude and period, and are similar for both neurons. In this way, the signal information encoded in the spike train of neuron 1 propagates to the spike train of neuron 2. Our results suggest that sensory neurons can exploit the presence of neural noise to fire spike trains where the information of a subthreshold stimulus is encoded in over expressed and/or in less expressed symbolic patterns.

Play Sports for a Quieter Brain: Evidence From Division I Collegiate Athletes

BACKGROUND

Playing sports has many benefits, including boosting physical, cardiovascular, and mental fitness. We tested whether athletic benefits extend to sensory processing-specifically auditory processing-as measured by the frequency-following response (FFR), a scalp-recorded electrophysiological potential that captures neural activity predominately from the auditory midbrain to complex sounds.

HYPOTHESIS

Given that FFR amplitude is sensitive to experience, with enrichment enhancing FFRs and injury reducing them, we hypothesized that playing sports is a form of enrichment that results in greater FFR amplitude.

STUDY DESIGN

Cross-sectional study.

LEVEL OF EVIDENCE

Level 3.

METHODS

We measured FFRs to the speech syllable "da" in 495 student-athletes across 19 Division I teams and 493 age- and sex-matched controls and compared them on 3 measures of FFR amplitude: amplitude of the response, amplitude of the background noise, and the ratio of these 2 measures.

RESULTS

Athletes have larger responses to sound than nonathletes, driven by a reduction in their level of background neural noise.

CONCLUSION

These findings suggest that playing sports increases the gain of an auditory signal by turning down the background noise. This mode of enhancement may be tied to the overall fitness level of athletes and/or the heightened need of an athlete to engage with and respond to auditory stimuli during competition.

CLINICAL RELEVANCE

These results motivate athletics overall and engagement in athletic interventions for populations that struggle with sensory processing, such as individuals with language disorders. Also, because head injuries can disrupt these same auditory processes, it is important to consider how auditory processing enhancements may offset injury.

Neural Activation During Visual Attention Differs in Individuals with High versus Low Macular Pigment Density

SCOPE

The neural efficiency hypothesis for lutein (L) and zeaxanthin (Z) suggests that higher levels of L+Z in the central nervous system (CNS) are predictive of stronger stimulus-specific brain responses. Past research suggests that supplementing L+Z can improve neural processing speed and cognitive function across multiple domains, which supports this hypothesis. The purpose of this study is to determine the extent to which CNS L+Z levels predict brain responses using an attentionally taxing task.

METHODS AND RESULTS

Macular pigment optical density (MPOD) is measured at baseline in 85 participants ranging in age from 18-92 years. Brain activation is measured using dense array electroencephalography. Stimuli evoking the signal include a grating array of vertical bars, oscillating at four driving frequencies. Significant stimulus-specific interactions are detected between attend condition, location, and age (p < .002) for unattended image locations, and between age and location (p < .008) for attended locations. Although no differences are found across age by MPOD, this measure is found to be predictive of neural power at parafoveal bar locations (R² .080).

CONCLUSION

CNS L+Z status is related to differences in brain activation in conditions designed to stress visual attention. These differences are strongest for older subjects.

On the interrelation of 1/f neural noise and norepinephrine system activity during motor response inhibition

Several lines of evidence suggest that there is a close interrelation between the degree of noise in neural circuits and the activity of the norepinephrine (NE) system, yet the precise nexus between these aspects is far from being understood during human information processing and cognitive control in particular. We examine this nexus during response inhibition in n = 47 healthy participants. Using high-density EEG recordings, we estimate neural noise by calculating "1/f noise" of those data and integrate these EEG parameters with pupil diameter data as an established indirect index of NE system activity. We show that neural noise is reduced when cognitive control processes to inhibit a prepotent/automated response are exerted. These neural noise variations were confined to the theta frequency band, which has also been shown to play a central role during response inhibition and cognitive control. There were strong positive correlations between the 1/f neural noise parameter and the pupil diameter data within the first 250 ms after the Nogo stimulus presentation at centro-parietal electrode sites. No such correlations were evident during automated responding on Go trials. Source localization analyses using standardized low-resolution brain electromagnetic tomography show that inferior parietal areas are activated in this time period in Nogo trials. The data suggest an interrelation of NE system activity and neural noise within early stages of information processing associated with inferior parietal areas when cognitive control processes are required. The data provide the first direct evidence for the nexus between NE system activity and the modulation of neural noise during inhibitory control in humans. NEW & NOTEWORTHY This is the first study showing that there is a nexus between norepinephrine system activity and the modulation of neural noise or scale-free neural activity during inhibitory control in humans. It does so by integrating pupil diameter data with analysis of EEG neural noise.

Individual differences in sensory sensitivity: A synthesizing framework and evidence from normal variation and developmental conditions

For some people, simple sensory stimuli (e.g., noises, patterns) may reliably evoke intense and aversive reactions. This is common in certain clinical groups (e.g., autism) and varies greatly in the neurotypical population. This paper critically evaluates the concept of individual differences in sensory sensitivity, explores its possible underlying neurobiological basis, and presents a roadmap for future research in this area. A distinction is made between subjective sensory sensitivity (self-reported symptoms); neural sensory sensitivity (the degree of neural activity induced by sensory stimuli); and behavioral sensory sensitivity (detection and discrimination of sensory stimuli). Whereas increased subjective and neural sensory sensitivity are assumed to increase together, the status of behavioral sensory sensitivity depends on the extent to which the increased neural activity is linked to signal or noise. A signal detection framework is presented that offers a unifying framework for exploring sensory sensitivity across different conditions. The framework is discussed, in more concrete terms, by linking it to four existing theoretical accounts of atypical sensory sensitivity (not necessarily mutually exclusive): increased excitation-to-inhibition ratio; predictive coding; increased neural noise; and atypical brain connectivity.

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as indicated by comparison with simulation results of large recurrent networks. Our method can help to elucidate how network heterogeneity shapes the asynchronous state in recurrent neural networks.

The Magnitude of Trial-By-Trial Neural Variability Is Reproducible over Time and across Tasks in Humans

Numerous studies have shown that neural activity in sensory cortices is remarkably variable over time and across trials even when subjects are presented with an identical repeating stimulus or task. This trial-by-trial neural variability is relatively large in the prestimulus period and considerably smaller (quenched) following stimulus presentation. Previous studies have suggested that the magnitude of neural variability affects behavior such that perceptual performance is better on trials and in individuals where variability quenching is larger. To what degree are neural variability magnitudes of individual subjects flexible or static? Here, we used EEG recordings from adult humans to demonstrate that neural variability magnitudes in visual cortex are remarkably consistent across different tasks and recording sessions. While magnitudes of neural variability differed dramatically across individual subjects, they were surprisingly stable across four tasks with different stimuli, temporal structures, and attentional/cognitive demands as well as across experimental sessions separated by one year. These experiments reveal that, in adults, neural variability magnitudes are mostly solidified individual characteristics that change little with task or time, and are likely to predispose individual subjects to exhibit distinct behavioral capabilities.

Internal noise estimates correlate with autistic traits

Previous neuroimaging research has reported increased internal (neural) noise in sensory systems of autistic individuals. However, it is unclear if this difference has behavioural or perceptual consequences, as previous attempts at measuring internal noise in ASD psychophysically have been indirect. Here, we use a "gold standard" psychophysical double-pass paradigm to investigate the relationship between internal noise and autistic traits in the neurotypical population (n = 43). We measured internal noise in three tasks (contrast perception, facial expression intensity perception, and number summation) to estimate a global internal noise factor using principal components analysis. This global internal noise was positively correlated with autistic traits (r_s  = 0.32, P = 0.035). This suggests that increased internal noise is associated with the ASD phenotype even in subclinical populations. The finding is discussed in relation to the neural and genetic basis of internal noise in ASD. Autism Res 2017, 10: 1384-1391. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Neural Control Adaptation to Motor Noise Manipulation

Antagonistic muscular co-activation can compensate for movement variability induced by motor noise at the expense of increased energetic costs. Greater antagonistic co-activation is commonly observed in older adults, which could be an adaptation to increased motor noise. The present study tested this hypothesis by manipulating motor noise in 12 young subjects while they practiced a goal-directed task using a myoelectric virtual arm, which was controlled by their biceps and triceps muscle activity. Motor noise was increased by increasing the coefficient of variation (CV) of the myoelectric signals. As hypothesized, subjects adapted by increasing antagonistic co-activation, and this was associated with reduced noise-induced performance decrements. A second hypothesis was that a virtual decrease in motor noise, achieved by smoothing the myoelectric signals, would have the opposite effect: co-activation would decrease and motor performance would improve. However, the results showed that a decrease in noise made performance worse instead of better, with no change in co-activation. Overall, these findings suggest that the nervous system adapts to virtual increases in motor noise by increasing antagonistic co-activation, and this preserves motor performance. Reducing noise may have failed to benefit performance due to characteristics of the filtering process itself, e.g., delays are introduced and muscle activity bursts are attenuated. The observed adaptations to increased noise may explain in part why older adults and many patient populations have greater antagonistic co-activation, which could represent an adaptation to increased motor noise, along with a desire for increased joint stability.

The Thalamus as a Low Pass Filter: Filtering at the Cellular Level does Not Equate with Filtering at the Network Level

In the mammalian central nervous system, most sensory information passes through primary sensory thalamic nuclei, however the consequence of this remains unclear. Various propositions exist, likening the thalamus to a gate, or a high pass filter. Here, using a simple leaky integrate and fire model based on physiological parameters, we show that the thalamus behaves akin to a low pass filter. Specifically, as individual cells in the thalamus rely on consistent drive to spike, stimuli that is rapidly and continuously changing over time such that it activates sensory cells with different receptive fields are unable to drive thalamic spiking. This means that thalamic encoding is robust to sensory noise, however it induces a lag in sensory representation. Thus, the thalamus stabilizes encoding of sensory information, at the cost of response rate.

Age-Related Changes in 1/f Neural Electrophysiological Noise

Aging is associated with performance decrements across multiple cognitive domains. The neural noise hypothesis, a dominant view of the basis of this decline, posits that aging is accompanied by an increase in spontaneous, noisy baseline neural activity. Here we analyze data from two different groups of human subjects: intracranial electrocorticography from 15 participants over a 38 year age range (15-53 years) and scalp EEG data from healthy younger (20-30 years) and older (60-70 years) adults to test the neural noise hypothesis from a 1/f noise perspective. Many natural phenomena, including electrophysiology, are characterized by 1/f noise. The defining characteristic of 1/f is that the power of the signal frequency content decreases rapidly as a function of the frequency (f) itself. The slope of this decay, the noise exponent (χ), is often <-1 for electrophysiological data and has been shown to approach white noise (defined as χ = 0) with increasing task difficulty. We observed, in both electrophysiological datasets, that aging is associated with a flatter (more noisy) 1/f power spectral density, even at rest, and that visual cortical 1/f noise statistically mediates age-related impairments in visual working memory. These results provide electrophysiological support for the neural noise hypothesis of aging. Significance statement: Understanding the neurobiological origins of age-related cognitive decline is of critical scientific, medical, and public health importance, especially considering the rapid aging of the world's population. We find, in two separate human studies, that 1/f electrophysiological noise increases with aging. In addition, we observe that this age-related 1/f noise statistically mediates age-related working memory decline. These results significantly add to this understanding and contextualize a long-standing problem in cognition by encapsulating age-related cognitive decline within a neurocomputational model of 1/f noise-induced deficits in neural communication.

Tracking the Brain's Functional Coupling Dynamics over Development

The transition from childhood to adulthood is marked by pronounced functional and structural brain transformations that impact cognition and behavior. Here, we use a functional imaging approach to reveal dynamic changes in coupling strength between networks and the expression of discrete brain configurations over human development during rest and a cognitive control task. Although the brain's repertoire of functional states was generally preserved across ages, state-specific temporal features, such as the frequency of expression and the amount of time spent in select states, varied by age in ways that were dependent on condition. Increasing age was associated with greater variability of connection strengths across time at rest, while there was a selective inversion of this effect in higher-order networks during implementation of cognitive control. The results suggest that development is characterized by the modification of dynamic coupling to both maximize and constrain functional variability in response to ongoing cognitive and behavioral requirements.

Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity

A major source of random variability in cortical networks is the quasi-random arrival of presynaptic action potentials from many other cells. In network studies as well as in the study of the response properties of single cells embedded in a network, synaptic background input is often approximated by Poissonian spike trains. However, the output statistics of the cells is in most cases far from being Poisson. This is inconsistent with the assumption of similar spike-train statistics for pre- and postsynaptic cells in a recurrent network. Here we tackle this problem for the popular class of integrate-and-fire neurons and study a self-consistent statistics of input and output spectra of neural spike trains. Instead of actually using a large network, we use an iterative scheme, in which we simulate a single neuron over several generations. In each of these generations, the neuron is stimulated with surrogate stochastic input that has a similar statistics as the output of the previous generation. For the surrogate input, we employ two distinct approximations: (i) a superposition of renewal spike trains with the same interspike interval density as observed in the previous generation and (ii) a Gaussian current with a power spectrum proportional to that observed in the previous generation. For input parameters that correspond to balanced input in the network, both the renewal and the Gaussian iteration procedure converge quickly and yield comparable results for the self-consistent spike-train power spectrum. We compare our results to large-scale simulations of a random sparsely connected network of leaky integrate-and-fire neurons (Brunel, 2000) and show that in the asynchronous regime close to a state of balanced synaptic input from the network, our iterative schemes provide an excellent approximations to the autocorrelation of spike trains in the recurrent network.

Is neural hyperpolarization by cathodal stimulation always detrimental at the behavioral level?

Cathodal transcranial direct current stimulation (c-tDCS) is usually considered an inhibitory stimulation. From a physiological perspective, c-tDCS induces hyperpolarization at the neural level. However, from a behavioral perspective, c-tDCS application does not always result in performance deterioration. In this work, we investigated the role of several important stimulation parameters (i.e., timing, presence of pauses, duration, and intensity) in shaping the behavioral effects of c-tDCS over the primary visual cortex. In Experiment 1, we applied c-tDCS at two different times (before or during an orientation discrimination task). We also studied the effects of pauses during the stimulation. In Experiments 2 and 3, we compared different durations (9 vs. 22 min) and intensities (0.75 vs. 1.5 mA) of stimulation. c-tDCS applied before task execution induced an improvement of performance, highlighting the importance of the activation state of the cortex. However, this result depended on the duration and intensity of stimulation. We suggest that the application of c-tDCS induces depression of cortical activity over a specific stimulated area; but to keep reactivity within given limits, the brain react in order to restore the equilibrium and this might result in increased sensitivity in visual performance. This is a further example of how the nervous system dynamically maintains a condition that permits adequate performance in different environments.

Origin of intrinsic irregular firing in cortical interneurons

Cortical spike trains are highly irregular both during ongoing, spontaneous activity and when driven at high firing rates. There is uncertainty about the source of this irregularity, ranging from intrinsic noise sources in neurons to collective effects in large-scale cortical networks. Cortical interneurons display highly irregular spike times (coefficient of variation of the interspike intervals >1) in response to dc-current injection in vitro. This is in marked contrast to cortical pyramidal cells, which spike highly irregularly in vivo, but regularly in vitro. We show with in vitro recordings and computational models that this is due to the fast activation kinetics of interneuronal K(+) currents. This explanation holds over a wide parameter range and with Gaussian white, power-law, and Ornstein-Uhlenbeck noise. The intrinsically irregular spiking of interneurons could contribute to the irregularity of the cortical network.

An electrophysiological validation of stochastic DCM for fMRI

In this note, we assess the predictive validity of stochastic dynamic causal modeling (sDCM) of functional magnetic resonance imaging (fMRI) data, in terms of its ability to explain changes in the frequency spectrum of concurrently acquired electroencephalography (EEG) signal. We first revisit the heuristic model proposed in Kilner et al. (2005), which suggests that fMRI activation is associated with a frequency modulation of the EEG signal (rather than an amplitude modulation within frequency bands). We propose a quantitative derivation of the underlying idea, based upon a neural field formulation of cortical activity. In brief, dense lateral connections induce a separation of time scales, whereby fast (and high spatial frequency) modes are enslaved by slow (low spatial frequency) modes. This slaving effect is such that the frequency spectrum of fast modes (which dominate EEG signals) is controlled by the amplitude of slow modes (which dominate fMRI signals). We then use conjoint empirical EEG-fMRI data-acquired in epilepsy patients-to demonstrate the electrophysiological underpinning of neural fluctuations inferred from sDCM for fMRI.

Transcranial stimulation and cognition

Noninvasive brain stimulation (NIBS) is a unique method for studying cognitive function. For the study of cognition, NIBS has gained popularity as a complementary method to functional neuroimaging. By bypassing the correlative approaches of standard imaging techniques, it is possible to establish a putative relationship between brain cognition. In fact, functional neuroimaging data cannot demonstrate the actual role of a particular cortical activation in a specific function because an activated area may simply be correlated with task performance, rather than being responsible for it. NIBS can induce a temporary modification of performance only if the stimulated area is causally engaged in the task. In analogy with lesion studies, NIBS can provide information about where and when a particular process occurs. Based on this assumption, NIBS has been used in many different cognitive domains. However, one of the most interesting questions in neuroscience may not be where and when, but how cognitive activity occurs. Beyond localization approaches, NIBS can be employed to study brain mechanisms. NIBS techniques have the potential to influence behavior transiently by altering neuronal activity, which may have facilitatory or inhibitory behavioral effects. NIBS techniques include transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES). TMS has been shown transiently to modulate neural excitability in a manner that is dependent mainly on the timing and frequency of stimulation (high versus low). The mechanism underlying tES is a change in neuronal membrane potentials that appears to be dependent mainly on the direction of current flow (anodal versus cathodal). Nevertheless, the final effects induced by TMS or tES depend on many technical parameters used during stimulation, such as the intensity of stimulation, coil orientation, site of the reference electrode, and time of application. Moreover, an important factor is the possible interactions between these factors and the physiological and cognitive state of the subject. To use NIBS in cognition, it is important to understand not only how NIBS functions but also the brain mechanisms being studied and the features of the area of interest. To describe better the advanced knowledge provided by NIBS in cognition, we will treat each NIBS technique separately and underline the related hypotheses beyond applications.

Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity

One of the reasons the visual cortex has attracted the interest of computational neuroscience is that it has well-defined inputs. The lateral geniculate nucleus (LGN) of the thalamus is the source of visual signals to the primary visual cortex (V1). Most large-scale cortical network models approximate the spike trains of LGN neurons as simple Poisson point processes. However, many studies have shown that neurons in the early visual pathway are capable of spiking with high temporal precision and their discharges are not Poisson-like. To gain an understanding of how response variability in the LGN influences the behavior of V1, we study response properties of model V1 neurons that receive purely feedforward inputs from LGN cells modeled either as noisy leaky integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first demonstrate that the NLIF model is capable of reproducing many experimentally observed statistical properties of LGN neurons. Then we show that a V1 model in which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces higher orientation selectivity than the one with Poisson LGN input. The second result implies that statistical characteristics of LGN spike trains are important for V1's function. We conclude that physiologically motivated models of V1 need to include more realistic LGN spike trains that are less noisy than inhomogeneous Poisson processes.

Biological sources of inflexibility in brain and behavior with aging and neurodegenerative diseases

Almost unequivocally, aging and neurodegeneration lead to deficits in neural information processing. These declines are marked by increased neural noise that is associated with increased variability or inconsistency in behavioral patterns. While it is often viewed that these problems arise from dysregulation of dopamine (DA), a monoamine modulator, glutamate (GLU), an excitatory amino acid that interacts with DA, also plays a role in determining the level of neural noise. We review literature demonstrating that neural noise is highest at both high and low levels of DA and GLU, allowing their interaction to form a many-to-one solution map for neural noise modulation. With aging and neurodegeneration, the range over which DA and GLU can be modulated is decreased leading to inflexibility in brain activity and behavior. As the capacity to modulate neural noise is restricted, the ability to shift noise from one brain region to another is reduced, leading to greater uniformity in signal-to-noise ratios across the entire brain. A negative consequence at the level of behavior is inflexibility that reduces the ability to: (1) switch from one behavior to another; and (2) stabilize a behavioral pattern against external perturbations. In this paper, we develop a theoretical framework where inflexibility across brain and behavior, rather than inconsistency and variability is the more important problem in aging and neurodegeneration. This theoretical framework of inflexibility in aging and neurodegeneration leads to the hypotheses that: (1) dysfunction in either or both of the DA and GLU systems restricts the ability to modulate neural noise; and (2) levels of neural noise and variability in brain activation will be dedifferentiated and more evenly distributed across the brain; and (3) changes in neural noise and behavioral variability in response to different task demands and changes in the environment will be reduced.

Reduced specificity of functional connectivity in the aging brain during task performance

The importance of studying connectivity in the aging brain is increasingly recognized. Recent studies have shown that connectivity within the default mode network is reduced with age and have demonstrated a clear relation of these changes with cognitive functioning. However, research on age-related changes in other functional networks is sparse and mainly focused on prespecified functional networks. Using functional magnetic resonance imaging, we investigated age-related changes in functional connectivity during a visual oddball task in a range of functional networks. It was found that compared with young participants, elderly showed a decrease in connectivity between areas belonging to the same functional network. This was found in the default mode network and the somatomotor network. Moreover, in all identified networks, elderly showed increased connectivity between areas within these networks and areas belonging to different functional networks. Decreased connectivity within functional networks was related to poorer cognitive functioning in elderly. The results were interpreted as a decrease in the specificity of functional networks in older participants.

Modeling pharmacological clock and memory patterns of interval timing in a striatal beat-frequency model with realistic, noisy neurons

In most species, the capability of perceiving and using the passage of time in the seconds-to-minutes range (interval timing) is not only accurate but also scalar: errors in time estimation are linearly related to the estimated duration. The ubiquity of scalar timing extends over behavioral, lesion, and pharmacological manipulations. For example, in mammals, dopaminergic drugs induce an immediate, scalar change in the perceived time (clock pattern), whereas cholinergic drugs induce a gradual, scalar change in perceived time (memory pattern). How do these properties emerge from unreliable, noisy neurons firing in the milliseconds range? Neurobiological information relative to the brain circuits involved in interval timing provide support for an striatal beat frequency (SBF) model, in which time is coded by the coincidental activation of striatal spiny neurons by cortical neural oscillators. While biologically plausible, the impracticality of perfect oscillators, or their lack thereof, questions this mechanism in a brain with noisy neurons. We explored the computational mechanisms required for the clock and memory patterns in an SBF model with biophysically realistic and noisy Morris–Lecar neurons (SBF–ML). Under the assumption that dopaminergic drugs modulate the firing frequency of cortical oscillators, and that cholinergic drugs modulate the memory representation of the criterion time, we show that our SBF–ML model can reproduce the pharmacological clock and memory patterns observed in the literature. Numerical results also indicate that parameter variability (noise) – which is ubiquitous in the form of small fluctuations in the intrinsic frequencies of neural oscillators within and between trials, and in the errors in recording/retrieving stored information related to criterion time – seems to be critical for the time-scale invariance of the clock and memory patterns.

Effects of nonuniform fiber sensitivity, innervation geometry, and noise on information relayed by a population of slowly adapting type I primary afferents from the fingerpad

The capacity of a population of primary afferent fibers to signal information about a sphere indenting the fingerpad is limited by factors such as the inhomogeneity of sensitivity among the afferents, the pattern and density of innervation, and the effects of noise (response variability). Using experimental data recorded from single slowly adapting type I afferents (SAIs), we simulated the response of the SAI population to such a stimulus. The human ability to discriminate stimulus curvature, location, and force has been quantified previously. We devised three neural measures, treating them as surrogates for the real neural measures underlying human performance, and explored how population parameters usually overlooked in neural coding studies affect such measures. Variation in sensitivity among SAIs is large; this distorts population response profiles markedly but has no significant impact on the neural measures. Two classes of noise were introduced, one dependent on and the other independent of the level of neural activity. Resolution of the model was compared with discrimination in humans. Correlation of noise among neurons had different effects for the different measures. An increase in correlation decreased resolution in the measure for force but improved resolution in the measure for position. Increasing innervation density (1) always increased resolution for position and (2) increased resolution for force if noise was uncorrelated but had diminishing effects as correlation increased. Correlation and innervation density had complex effects on the measure for curvature, depending on the class of noise. Nonuniformity in the pattern of innervation had negligible effects on resolution.

The power ratio and the interval map: spiking models and extracellular recordings

We describe a new, computationally simple method for analyzing the dynamics of neuronal spike trains driven by external stimuli. The goal of our method is to test the predictions of simple spike-generating models against extracellularly recorded neuronal responses. Through a new statistic called the power ratio, we distinguish between two broad classes of responses: (1) responses that can be completely characterized by a variable firing rate, (for example, modulated Poisson and gamma spike trains); and (2) responses for which firing rate variations alone are not sufficient to characterize response dynamics (for example, leaky integrate-and-fire spike trains as well as Poisson spike trains with long absolute refractory periods). We show that the responses of many visual neurons in the cat retinal ganglion, cat lateral geniculate nucleus, and macaque primary visual cortex fall into the second class, which implies that the pattern of spike times can carry significant information about visual stimuli. Our results also suggest that spike trains of X-type retinal ganglion cells, in particular, are very similar to spike trains generated by a leaky integrate-and-fire model with additive, stimulus-independent noise that could represent background synaptic activity.