Constraints on persistent activity in a biologically detailed network model of the prefrontal cortex with heterogeneities.

Persistent activity, the maintenance of neural activation over short periods of time in cortical networks, is widely thought to underlie the cognitive function of working memory. A large body of modeling studies has reproduced this kind of activity using cell assemblies with strengthened synaptic connections. However, almost all of these studies have considered persistent activity within networks with homogeneous neurons and synapses, making it difficult to judge the validity of such model results for cortical dynamics, which is based on highly heterogeneous neurons. Here, we consider persistent activity in a detailed, strongly data-driven network model of the prefrontal cortex with heterogeneous neuron and synapse parameters. Surprisingly, persistent activity could not be reproduced in this model without incorporating further constraints. We identified three factors that prevent successful persistent activity: heterogeneity in the cell parameters of interneurons, heterogeneity in the parameters of short-term synaptic plasticity and heterogeneity in the synaptic weights. We also discovered a general dynamic mechanism that prevents persistent activity in the presence of heterogeneities, namely a gradual drop-out of cell assembly neurons out of a bistable regime as input variability increases. Based on this mechanism, we found that persistent activity is recovered if heterogeneity is compensated, e.g., by a homeostatic plasticity mechanism. Cell assemblies shaped in this way may be potentially targeted by distinct inputs or become more responsive to specific tuning or spectral properties. Finally, we show that persistent activity in the model is robust against external noise, but the compensation of heterogeneities may prevent the dynamic generation of intrinsic in vivo-like irregular activity. These results may help informing the ongoing debate about the neural basis of working memory.

Effective connectivity of the human mirror neuron system during social cognition.

The human mirror neuron system (MNS) can be considered the neural basis of social cognition. Identifying the global network structure of this system can provide significant progress in the field. In this study, we use dynamic causal modeling (DCM) to determine the effective connectivity between central regions of the MNS for the first time during different social cognition tasks. Sixty-seven healthy participants completed fMRI scanning while performing social cognition tasks, including imitation, empathy and theory of mind. Superior temporal sulcus (STS), inferior parietal lobule (IPL) and Brodmann area 44 (BA44) formed the regions of interest for DCM. Varying connectivity patterns, 540 models were built and fitted for each participant. By applying group-level analysis, Bayesian model selection and Bayesian model averaging, the optimal family and model for all experimental tasks were found. For all social-cognitive processes, effective connectivity from STS to IPL and from STS to BA44 was found. For imitation, additional mutual connections occurred between STS and BA44, as well as BA44 and IPL. The results suggest inverse models in which the motor regions BA44 and IPL receive sensory information from the STS. In contrast, for imitation, a sensory loop with an exchange of motor-to-sensory and sensory-to-motor information seems to exist.

Family-centered music therapy-Empowering premature infants and their primary caregivers through music: Results of a pilot study.

BACKGROUND: In Neonatal Intensive Care Units (NICUs) premature infants are exposed to various acoustic, environmental and emotional stressors which have a negative impact on their development and the mental health of their parents. Family-centred music therapy bears the potential to positively influence these stressors. The few existing studies indicate that interactive live-improvised music therapy interventions both reduce parental stress factors and support preterm infants' development. METHODS: The present randomized controlled longitudinal study (RCT) with very low and extremely low birth weight infants (born <30+0 weeks of gestation) and their parents analyzed the influence of music therapy on both the physiological development of premature infants and parental stress factors. In addition, possible interrelations between infant development and parental stress were explored. 65 parent-infant-pairs were enrolled in the study. The treatment group received music therapy twice a week from the 21st day of life till discharge from hospital. The control group received treatment as usual. RESULTS: Compared to the control group, infants in the treatment group showed a 11.1 days shortening of caffeine therapy, 12.1 days shortening of nasogastric/ orogastric tube feed and 15.5 days shortening of hospitalization, on average. While these differences were not statistically significant, a factor-analytical compound measure of all three therapy durations was. From pre-to-post-intervention, parents showed a significant reduction in stress factors. However, there were no differences between control and treatment group. A regression analysis showed links between parental stress factors and physiological development of the infants. CONCLUSION: This pilot study suggests that a live-improvised interactive music therapy intervention for extremely and very preterm infants and their parents may have a beneficial effect on the therapy duration needed for premature infants before discharge from hospital is possible. The study identified components of the original physiological variables of the infants as appropriate endpoints and suggested a slight change in study design to capture possible effects of music therapy on infants' development as well. Further studies should assess both short-term and long-term effects on premature infants as well as on maternal and paternal health outcomes, to determine whether a family-centered music therapy, actually experienced as an added value to developmental care, should be part of routine care at the NICU.

The human mirror neuron system-A common neural basis for social cognition?

According to the theory of embodied simulation, mirror neurons (MN) in our brain's motor system are the neuronal basis of all social-cognitive processes. The assumption of such a mirroring process in humans could be supported by results showing that within one person the same region is involved in different social cognition tasks. We conducted an fMRI-study with 75 healthy participants who completed three tasks: imitation, empathy, and theory of mind. We analyzed the data using group conjunction analyses and individual shared voxel counts. Across tasks, across and within participants, we find common activation in inferior frontal gyrus, inferior parietal cortex, fusiform gyrus, posterior superior temporal sulcus, and amygdala. Our results provide evidence for a shared neural basis for different social-cognitive processes, indicating that interpersonal understanding might occur by embodied simulation.

Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations.

Dynamic causal modeling (DCM) is an analysis technique that has been successfully used to infer about directed connectivity between brain regions based on imaging data such as functional magnetic resonance imaging (fMRI). Most variants of DCM for fMRI rely on a simple bilinear differential equation for neural activation, making it difficult to interpret the results in terms of local neural dynamics. In this work, we introduce a modification to DCM for fMRI by replacing the bilinear equation with a non-linear Wilson-Cowan based equation and use Bayesian Model Comparison (BMC) to show that this modification improves the model evidences. Improved model evidence of the non-linear model is shown for our empirical data (imitation of facial expressions) and validated by synthetic data as well as an empirical test dataset (attention to visual motion) used in previous foundational papers. For our empirical data, we conduct the analysis for a group of 42 healthy participants who performed an imitation task, activating regions putatively containing the human mirror neuron system (MNS). In this regard, we build 540 models as one family for comparing the standard bilinear with the modified Wilson-Cowan models on the family-level. Using this modification, we can interpret the sigmoid transfer function as an averaged f-I curve of many neurons in a single region with a sigmoidal format. In this way, we can make a direct inference from the macroscopic model to detailed microscopic models. The new DCM variant shows superior model evidence on all tested data sets.

fMRI adaptation reveals: The human mirror neuron system discriminates emotional valence.

Our ability to infer other individuals' emotions is central for successful social interactions. Based on the theory of embodied simulation, our mirror neuron system (MNS) provides the essential link between the observed facial configuration of another individual and our inference of the emotion by means of common neuronal activation. However, so far it is unknown, whether the MNS differentiates the valence of facial configurations. To increase the precision of our fMRI measurement, we used an adaptation design, which allows insights into whether the same neuronal population is active for subsequent stimuli of facial configurations. 76 participants were shown congruent, or incongruent consecutive pairs of facial configurations expressing fear or happiness. Significant activation for changes in emotional valence from adaptor to target was revealed in fusiform gyrus, superior temporal sulcus, amygdala, insula, inferior parietal lobe and Brodmann area 44. In addition, activation change was higher in superior temporal sulcus, insula and inferior frontal gyrus for a switch from happiness to fear than for fear to happiness. Our results suggest an involvement of the MNS in valence discrimination, and a higher sensitivity of the MNS to negative than positive valence. These findings point to a role of the MNS that goes beyond the mere coding of a motor state.

Hyperfunctioning of the right posterior superior temporal sulcus in response to neutral facial expressions presents an endophenotype of schizophrenia.

Deficits in social cognition have been proposed as a marker of schizophrenia. Growing evidence suggests especially hyperfunctioning of the right posterior superior temporal sulcus (pSTS) in response to neutral social stimuli reflecting the neural correlates of social-cognitive impairments in schizophrenia. We characterized healthy participants according to schizotypy (n = 74) and the single-nucleotide polymorphism rs1344706 in ZNF804A (n = 73), as they represent risk variants for schizophrenia from the perspectives of personality traits and genetics, respectively. A social-cognitive fMRI task was applied to investigate the association of right pSTS hyperfunctioning in response to neutral face stimuli with schizotypy and rs1344706. Higher right pSTS activation in response to neutral facial expressions was found in individuals with increased positive (trend) and disorganization symptoms, as well as in carriers of the risk allele of rs1344706. In addition, a positive association between right-left pSTS connectivity and disorganization symptoms during neutral face processing was revealed. Although these findings warrant replication, we suggest that right pSTS hyperfunctioning in response to neutral facial expressions presents an endophenotype of schizophrenia. We assume that right pSTS hyperfunctioning is a vulnerability to perceive neutral social stimuli as emotionally or intentionally salient, probably contributing to the emergence of symptoms of schizophrenia.

Biased competition in the absence of input bias revealed through corticostriatal computation.

Classical accounts of biased competition require an input bias to resolve the competition between neuronal ensembles driving downstream processing. However, flexible and reliable selection of behaviorally relevant ensembles can occur with unbiased stimulation: striatal D1 and D2 spiny projection neurons (SPNs) receive balanced cortical input, yet their activity determines the choice between GO and NO-GO pathways in the basal ganglia. We here present a corticostriatal model identifying three mechanisms that rely on physiological asymmetries to effect rate- and time-coded biased competition in the presence of balanced inputs. First, tonic input strength determines which one of the two SPN phenotypes exhibits a higher mean firing rate. Second, low-strength oscillatory inputs induce higher firing rate in D2 SPNs but higher coherence between D1 SPNs. Third, high-strength inputs oscillating at distinct frequencies can preferentially activate D1 or D2 SPN populations. Of these mechanisms, only the latter accommodates observed rhythmic activity supporting rule-based decision making in prefrontal cortex.

Flexible resonance in prefrontal networks with strong feedback inhibition.

Oscillations are ubiquitous features of brain dynamics that undergo task-related changes in synchrony, power, and frequency. The impact of those changes on target networks is poorly understood. In this work, we used a biophysically detailed model of prefrontal cortex (PFC) to explore the effects of varying the spike rate, synchrony, and waveform of strong oscillatory inputs on the behavior of cortical networks driven by them. Interacting populations of excitatory and inhibitory neurons with strong feedback inhibition are inhibition-based network oscillators that exhibit resonance (i.e., larger responses to preferred input frequencies). We quantified network responses in terms of mean firing rates and the population frequency of network oscillation; and characterized their behavior in terms of the natural response to asynchronous input and the resonant response to oscillatory inputs. We show that strong feedback inhibition causes the PFC to generate internal (natural) oscillations in the beta/gamma frequency range (>15 Hz) and to maximize principal cell spiking in response to external oscillations at slightly higher frequencies. Importantly, we found that the fastest oscillation frequency that can be relayed by the network maximizes local inhibition and is equal to a frequency even higher than that which maximizes the firing rate of excitatory cells; we call this phenomenon population frequency resonance. This form of resonance is shown to determine the optimal driving frequency for suppressing responses to asynchronous activity. Lastly, we demonstrate that the natural and resonant frequencies can be tuned by changes in neuronal excitability, the duration of feedback inhibition, and dynamic properties of the input. Our results predict that PFC networks are tuned for generating and selectively responding to beta- and gamma-rhythmic signals due to the natural and resonant properties of inhibition-based oscillators. They also suggest strategies for optimizing transcranial stimulation and using oscillatory networks in neuromorphic engineering.

A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity.

The prefrontal cortex is centrally involved in a wide range of cognitive functions and their impairment in psychiatric disorders. Yet, the computational principles that govern the dynamics of prefrontal neural networks, and link their physiological, biochemical and anatomical properties to cognitive functions, are not well understood. Computational models can help to bridge the gap between these different levels of description, provided they are sufficiently constrained by experimental data and capable of predicting key properties of the intact cortex. Here, we present a detailed network model of the prefrontal cortex, based on a simple computationally efficient single neuron model (simpAdEx), with all parameters derived from in vitro electrophysiological and anatomical data. Without additional tuning, this model could be shown to quantitatively reproduce a wide range of measures from in vivo electrophysiological recordings, to a degree where simulated and experimentally observed activities were statistically indistinguishable. These measures include spike train statistics, membrane potential fluctuations, local field potentials, and the transmission of transient stimulus information across layers. We further demonstrate that model predictions are robust against moderate changes in key parameters, and that synaptic heterogeneity is a crucial ingredient to the quantitative reproduction of in vivo-like electrophysiological behavior. Thus, we have produced a physiologically highly valid, in a quantitative sense, yet computationally efficient PFC network model, which helped to identify key properties underlying spike time dynamics as observed in vivo, and can be harvested for in-depth investigation of the links between physiology and cognition.

Neurocomputational models of time perception.

Mathematical modeling is a useful tool for understanding the neurodynamical and computational mechanisms of cognitive abilities like time perception, and for linking neurophysiology to psychology. In this chapter, we discuss several biophysical models of time perception and how they can be tested against experimental evidence. After a brief overview on the history of computational timing models, we list a number of central psychological and physiological findings that such a model should be able to account for, with a focus on the scaling of the variability of duration estimates with the length of the interval that needs to be estimated. The functional form of this scaling turns out to be predictive of the underlying computational mechanism for time perception. We then present four basic classes of timing models (ramping activity, sequential activation of neuron populations, state space trajectories and neural oscillators) and discuss two specific examples in more detail. Finally, we review to what extent existing theories of time perception adhere to the experimental constraints.

Method for stationarity-segmentation of spike train data with application to the Pearson cross-correlation.

Correlations among neurons are supposed to play an important role in computation and information coding in the nervous system. Empirically, functional interactions between neurons are most commonly assessed by cross-correlation functions. Recent studies have suggested that pairwise correlations may indeed be sufficient to capture most of the information present in neural interactions. Many applications of correlation functions, however, implicitly tend to assume that the underlying processes are stationary. This assumption will usually fail for real neurons recorded in vivo since their activity during behavioral tasks is heavily influenced by stimulus-, movement-, or cognition-related processes as well as by more general processes like slow oscillations or changes in state of alertness. To address the problem of nonstationarity, we introduce a method for assessing stationarity empirically and then "slicing" spike trains into stationary segments according to the statistical definition of weak-sense stationarity. We examine pairwise Pearson cross-correlations (PCCs) under both stationary and nonstationary conditions and identify another source of covariance that can be differentiated from the covariance of the spike times and emerges as a consequence of residual nonstationarities after the slicing process: the covariance of the firing rates defined on each segment. Based on this, a correction of the PCC is introduced that accounts for the effect of segmentation. We probe these methods both on simulated data sets and on in vivo recordings from the prefrontal cortex of behaving rats. Rather than for removing nonstationarities, the present method may also be used for detecting significant events in spike trains.

An Approximation to the Adaptive Exponential Integrate-and-Fire Neuron Model Allows Fast and Predictive Fitting to Physiological Data.

For large-scale network simulations, it is often desirable to have computationally tractable, yet in a defined sense still physiologically valid neuron models. In particular, these models should be able to reproduce physiological measurements, ideally in a predictive sense, and under different input regimes in which neurons may operate in vivo. Here we present an approach to parameter estimation for a simple spiking neuron model mainly based on standard f-I curves obtained from in vitro recordings. Such recordings are routinely obtained in standard protocols and assess a neuron's response under a wide range of mean-input currents. Our fitting procedure makes use of closed-form expressions for the firing rate derived from an approximation to the adaptive exponential integrate-and-fire (AdEx) model. The resulting fitting process is simple and about two orders of magnitude faster compared to methods based on numerical integration of the differential equations. We probe this method on different cell types recorded from rodent prefrontal cortex. After fitting to the f-I current-clamp data, the model cells are tested on completely different sets of recordings obtained by fluctuating ("in vivo-like") input currents. For a wide range of different input regimes, cell types, and cortical layers, the model could predict spike times on these test traces quite accurately within the bounds of physiological reliability, although no information from these distinct test sets was used for model fitting. Further analyses delineated some of the empirical factors constraining model fitting and the model's generalization performance. An even simpler adaptive LIF neuron was also examined in this context. Hence, we have developed a "high-throughput" model fitting procedure which is simple and fast, with good prediction performance, and which relies only on firing rate information and standard physiological data widely and easily available.

Cross-modal distortion of time perception: demerging the effects of observed and performed motion.

Temporal information is often contained in multi-sensory stimuli, but it is currently unknown how the brain combines e.g. visual and auditory cues into a coherent percept of time. The existing studies of cross-modal time perception mainly support the "modality appropriateness hypothesis", i.e. the domination of auditory temporal cues over visual ones because of the higher precision of audition for time perception. However, these studies suffer from methodical problems and conflicting results. We introduce a novel experimental paradigm to examine cross-modal time perception by combining an auditory time perception task with a visually guided motor task, requiring participants to follow an elliptic movement on a screen with a robotic manipulandum. We find that subjective duration is distorted according to the speed of visually observed movement: The faster the visual motion, the longer the perceived duration. In contrast, the actual execution of the arm movement does not contribute to this effect, but impairs discrimination performance by dual-task interference. We also show that additional training of the motor task attenuates the interference, but does not affect the distortion of subjective duration. The study demonstrates direct influence of visual motion on auditory temporal representations, which is independent of attentional modulation. At the same time, it provides causal support for the notion that time perception and continuous motor timing rely on separate mechanisms, a proposal that was formerly supported by correlational evidence only. The results constitute a counterexample to the modality appropriateness hypothesis and are best explained by Bayesian integration of modality-specific temporal information into a centralized "temporal hub".

The neural representation of time: an information-theoretic perspective.

A prominent finding in psychophysical experiments on time perception is Weber's law, the linear scaling of timing errors with duration. The ability to reproduce this scaling has been taken as a criterion for the validity of neurocomputational models of time perception. However, the origin of Weber's law remains unknown, and currently only a few models generically reproduce it. Here, we use an information-theoretical framework that considers the neuronal mechanisms of time perception as stochastic processes to investigate the statistical origin of Weber's law in time perception and also its frequently observed deviations. Under the assumption that the brain is able to compute optimal estimates of time, we find that Weber's law only holds exactly if the estimate is based on temporal changes in the variance of the process. In contrast, the timing errors scale sublinearly with time if the systematic changes in the mean of a process are used for estimation, as is the case in the majority of time perception models, while estimates based on temporal correlations result in a superlinear scaling. This hierarchy of temporal information is preserved if several sources of temporal information are available. Furthermore, we consider the case of multiple stochastic processes and study the examples of a covariance-based model and a model based on synfire chains. This approach reveals that existing neurocomputational models of time perception can be classified as mean-, variance- and correlation-based processes and allows predictions about the scaling of the resulting timing errors.

A neurocomputational model for optimal temporal processing.

Humans can estimate the duration of intervals of time, and psychophysical experiments show that these estimations are subject to timing errors. According to standard theories of timing, these errors increase linearly with the interval to be estimated (Weber's law), and both at longer and shorter intervals, deviations from linearity are reported. This is not easily reconciled with the accumulation of neuronal noise, which would only lead to an increase with the square root of the interval. Here, we offer a neuronal model which explains the form of the error function as a result of a constrained optimization process. The model consists of a number of synfire chains with different transmission times, which project onto a set of readout neurons. We show that an increase in the transmission time corresponds to a superlinear increase of the timing errors. Under the assumption of a fixed chain length, the experimentally observed error function emerges from optimal selection of chains for each given interval. Furthermore, we show how this optimal selection could be implemented by competitive spike-timing dependent plasticity in the connections from the chains to the readout network, and discuss implications of our model on selective temporal learning and possible neural architectures of interval timing.