The effects of probabilistic context inference on motor adaptation

Humans have been shown to adapt their movements when a sudden or gradual change to the dynamics of the environment are introduced, a phenomenon called motor adaptation. If the change is reverted, the adaptation is also quickly reverted. Humans are also able to adapt to multiple changes in dynamics presented separately, and to be able to switch between adapted movements on the fly. Such switching relies on contextual information which is often noisy or misleading, affecting the switch between known adaptations. Recently, computational models for motor adaptation and context inference have been introduced, which contain components for context inference and Bayesian motor adaptation. These models were used to show the effects of context inference on learning rates across different experiments. We expanded on these works by using a simplified version of the recently-introduced COIN model to show that the effects of context inference on motor adaptation and control go even further than previously shown. Here, we used this model to simulate classical motor adaptation experiments from previous works and showed that context inference, and how it is affected by the presence and reliability of feedback, effect a host of behavioral phenomena that had so far required multiple hypothesized mechanisms, lacking a unified explanation. Concretely, we show that the reliability of direct contextual information, as well as noisy sensory feedback, typical of many experiments, effect measurable changes in switching-task behavior, as well as in action selection, that stem directly from probabilistic context inference.

Abstract rules drive adaptation in the subcortical sensory pathway

The subcortical sensory pathways are the fundamental channels for mapping the outside world to our minds. Sensory pathways efficiently transmit information by adapting neural responses to the local statistics of the sensory input. The long-standing mechanistic explanation for this adaptive behaviour is that neural activity decreases with increasing regularities in the local statistics of the stimuli. An alternative account is that neural coding is directly driven by expectations of the sensory input. Here, we used abstract rules to manipulate expectations independently of local stimulus statistics. The ultra-high-field functional-MRI data show that abstract expectations can drive the response amplitude to tones in the human auditory pathway. These results provide first unambiguous evidence of abstract processing in a subcortical sensory pathway. They indicate that the neural representation of the outside world is altered by our prior beliefs even at initial points of the processing hierarchy.

Addiction Research Consortium: Losing and regaining control over drug intake (ReCoDe)-From trajectories to mechanisms and interventions

One of the major risk factors for global death and disability is alcohol, tobacco, and illicit drug use. While there is increasing knowledge with respect to individual factors promoting the initiation and maintenance of substance use disorders (SUDs), disease trajectories involved in losing and regaining control over drug intake (ReCoDe) are still not well described. Our newly formed German Collaborative Research Centre (CRC) on ReCoDe has an interdisciplinary approach funded by the German Research Foundation (DFG) with a 12-year perspective. The main goals of our research consortium are (i) to identify triggers and modifying factors that longitudinally modulate the trajectories of losing and regaining control over drug consumption in real life, (ii) to study underlying behavioral, cognitive, and neurobiological mechanisms, and (iii) to implicate mechanism-based interventions. These goals will be achieved by: (i) using mobile health (m-health) tools to longitudinally monitor the effects of triggers (drug cues, stressors, and priming doses) and modify factors (eg, age, gender, physical activity, and cognitive control) on drug consumption patterns in real-life conditions and in animal models of addiction; (ii) the identification and computational modeling of key mechanisms mediating the effects of such triggers and modifying factors on goal-directed, habitual, and compulsive aspects of behavior from human studies and animal models; and (iii) developing and testing interventions that specifically target the underlying mechanisms for regaining control over drug intake.

Modulation of tonotopic ventral medial geniculate body is behaviorally relevant for speech recognition

Sensory thalami are central sensory pathway stations for information processing. Their role for human cognition and perception, however, remains unclear. Recent evidence suggests an involvement of the sensory thalami in speech recognition. In particular, the auditory thalamus (medial geniculate body, MGB) response is modulated by speech recognition tasks and the amount of this task-dependent modulation is associated with speech recognition abilities. Here, we tested the specific hypothesis that this behaviorally relevant modulation is present in the MGB subsection that corresponds to the primary auditory pathway (i.e., the ventral MGB [vMGB]). We used ultra-high field 7T fMRI to identify the vMGB, and found a significant positive correlation between the amount of task-dependent modulation and the speech recognition performance across participants within left vMGB, but not within the other MGB subsections. These results imply that modulation of thalamic driving input to the auditory cortex facilitates speech recognition.

Active Inference, Belief Propagation, and the Bethe Approximation

When modeling goal-directed behavior in the presence of various sources of uncertainty, planning can be described as an inference process. A solution to the problem of planning as inference was previously proposed in the active inference framework in the form of an approximate inference scheme based on variational free energy. However, this approximate scheme was based on the mean-field approximation, which assumes statistical independence of hidden variables and is known to show overconfidence and may converge to local minima of the free energy. To better capture the spatiotemporal properties of an environment, we reformulated the approximate inference process using the so-called Bethe approximation. Importantly, the Bethe approximation allows for representation of pairwise statistical dependencies. Under these assumptions, the minimizer of the variational free energy corresponds to the belief propagation algorithm, commonly used in machine learning. To illustrate the differences between the mean-field approximation and the Bethe approximation, we have simulated agent behavior in a simple goal-reaching task with different types of uncertainties. Overall, the Bethe agent achieves higher success rates in reaching goal states. We relate the better performance of the Bethe agent to more accurate predictions about the consequences of its own actions. Consequently, active inference based on the Bethe approximation extends the application range of active inference to more complex behavioral tasks.

Active Inference, Belief Propagation, and the Bethe Approximation

When modeling goal-directed behavior in the presence of various sources of uncertainty, planning can be described as an inference process. A solution to the problem of planning as inference was previously proposed in the active inference framework in the form of an approximate inference scheme based on variational free energy. However, this approximate scheme was based on the mean-field approximation, which assumes statistical independence of hidden variables and is known to show overconfidence and may converge to local minima of the free energy. To better capture the spatiotemporal properties of an environment, we reformulated the approximate inference process using the so-called Bethe approximation. Importantly, the Bethe approximation allows for representation of pairwise statistical dependencies. Under these assumptions, the minimizer of the variational free energy corresponds to the belief propagation algorithm, commonly used in machine learning. To illustrate the differences between the mean-field approximation and the Bethe approximation, we have simulated agent behavior in a simple goal-reaching task with different types of uncertainties. Overall, the Bethe agent achieves higher success rates in reaching goal states. We relate the better performance of the Bethe agent to more accurate predictions about the consequences of its own actions. Consequently, active inference based on the Bethe approximation extends the application range of active inference to more complex behavioral tasks.

Altered Medial Frontal Feedback Learning Signals in Anorexia Nervosa

BACKGROUND

In their relentless pursuit of thinness, individuals with anorexia nervosa (AN) engage in maladaptive behaviors (restrictive food choices and overexercising) that may originate in altered decision making and learning.

METHODS

In this functional magnetic resonance imaging study, we employed computational modeling to elucidate the neural correlates of feedback learning and value-based decision making in 36 female patients with AN and 36 age-matched healthy volunteers (12-24 years). Participants performed a decision task that required adaptation to changing reward contingencies. Data were analyzed within a hierarchical Gaussian filter model that captures interindividual variability in learning under uncertainty.

RESULTS

Behaviorally, patients displayed an increased learning rate specifically after punishments. At the neural level, hemodynamic correlates for the learning rate, expected value, and prediction error did not differ between the groups. However, activity in the posterior medial frontal cortex was elevated in AN following punishment.

CONCLUSIONS

Our findings suggest that the neural underpinning of feedback learning is selectively altered for punishment in AN.

Predictive coding under the free-energy principle

This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain. We assume that the brain models the world as a hierarchy or cascade of dynamical systems that encode causal structure in the sensorium. Perception is equated with the optimization or inversion of these internal models, to explain sensory data. Given a model of how sensory data are generated, we can invoke a generic approach to model inversion, based on a free energy bound on the model's evidence. The ensuing free-energy formulation furnishes equations that prescribe the process of recognition, i.e. the dynamics of neuronal activity that represent the causes of sensory input. Here, we focus on a very general model, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states. We first review hierarchical dynamical models and their inversion. We then show that the brain has the necessary infrastructure to implement this inversion and illustrate this point using synthetic birds that can recognize and categorize birdsongs.

Cortical circuits for perceptual inference

This paper assumes that cortical circuits have evolved to enable inference about the causes of sensory input received by the brain. This provides a principled specification of what neural circuits have to achieve. Here, we attempt to address how the brain makes inferences by casting inference as an optimisation problem. We look at how the ensuing recognition dynamics could be supported by directed connections and message-passing among neuronal populations, given our knowledge of intrinsic and extrinsic neuronal connections. We assume that the brain models the world as a dynamic system, which imposes causal structure on the sensorium. Perception is equated with the optimisation or inversion of this internal model, to explain sensory input. Given a model of how sensory data are generated, we use a generic variational approach to model inversion to furnish equations that prescribe recognition; i.e., the dynamics of neuronal activity that represents the causes of sensory input. Here, we focus on a model whose hierarchical and dynamical structure enables simulated brains to recognise and predict sequences of sensory states. We first review these models and their inversion under a variational free-energy formulation. We then show that the brain has the necessary infrastructure to implement this inversion and present stimulations using synthetic birds that generate and recognise birdsongs.

Functional optical signal analysis: a software tool for near-infrared spectroscopy data processing incorporating statistical parametric mapping

Optical topography (OT) relies on the near infrared spectroscopy (NIRS) technique to provide noninvasively a spatial map of functional brain activity. OT has advantages over conventional fMRI in terms of its simple approach to measuring the hemodynamic response, its ability to distinguish between changes in oxy- and deoxy-hemoglobin and the range of human participants that can be readily investigated. We offer a new software tool, functional optical signal analysis (fOSA), for analyzing the spatially resolved optical signals that provides statistical inference capabilities about the distribution of brain activity in space and time and by experimental condition. It does this by mapping the signal into a standard functional neuroimaging analysis software, statistical parametric mapping (SPM), and forms, in effect, a new SPM toolbox specifically designed for NIRS in an OT configuration. The validity of the program has been tested using synthetic data, and its applicability is demonstrated with experimental data.

Bayesian estimation of cerebral perfusion using a physiological model of microvasculature

Perfusion weighted MRI has proven very useful for deriving hemodynamic parameters such as CBF, CBV and MTT. These quantities are important diagnostically, e.g. in acute stroke, where they are used to delineate ischemic regions. Yet the standard method for estimating CBF based on singular value decomposition (SVD) has been demonstrated to underestimate (especially high) flow components and to be sensitive to delays in the arterial input function (AIF). Furthermore, the estimated residue functions often oscillate. This compromises their physiological interpretation/basis and makes estimation of related measures such as flow heterogeneity difficult. In this study, we estimate perfusion parameters based on a vascular model (VM) which represents heterogeneous capillary flow and explicitly leads to monotonically decreasing residue functions. We use a fully Bayesian approach to obtain posterior probability distributions for all parameters. In simulation studies, we show that the VM method has less bias in CBF estimates than the SVD based method for realistic SNRs. This also applies to cases where the AIF is delayed. We employ our method to estimate perfusion maps using data from (i) a healthy volunteer and (ii) from a stroke patient.

Action selectivity in parietal and temporal cortex

The sensory-action theory proposes that the neural substrates underlying action representations are related to a visuomotor action system encompassing the left ventral premotor cortex, the anterior intraparietal (AIP) and left posterior middle temporal gyrus (LPMT). Using fMRI, we demonstrate that semantic decisions on action, relative to non-action words, increased activation in the left AIP and LPMT irrespective of whether the words were presented in a written or spoken form. Left AIP and LPMT might thus play the role of amodal semantic regions that can be activated via auditory as well as visual input. Left AIP and LPMT did not distinguish between different types of actions such as hand actions and whole body movements, although a right STS region responded selectively to whole body movements.

Mixed-effects and fMRI studies

This note concerns mixed-effect (MFX) analyses in multisession functional magnetic resonance imaging (fMRI) studies. It clarifies the relationship between mixed-effect analyses and the two-stage "summary statistics" procedure (Holmes, A.P., Friston, K.J., 1998. Generalisability, random effects and population inference. NeuroImage 7, S754) that has been adopted widely for analyses of fMRI data at the group level. We describe a simple procedure, based on restricted maximum likelihood (ReML) estimates of covariance components, that enables full mixed-effects analyses in the context of statistical parametric mapping. Using this procedure, we compare the results of a full mixed-effects analysis with those obtained from the simpler two-stage procedure and comment on the situations when the two approaches may give different results.

Variational Bayesian inference for fMRI time series

We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models with Autoregressive (AR) error processes. We make use of the Variational Bayesian (VB) framework which approximates the true posterior density with a factorised density. The fidelity of this approximation is verified via Gibbs sampling. The VB approach provides a natural extension to previous Bayesian analyses which have used Empirical Bayes. VB has the advantage of taking into account the variability of hyperparameter estimates with little additional computational effort. Further, VB allows for automatic selection of the order of the AR process. Results are shown on simulated data and on data from an event-related fMRI experiment.

Visuomotor control within a distributed parieto-frontal network

The aim of this functional magnetic resonance imaging study was to investigate differences in visuomotor control with increasing task complexity. Twelve right-handed volunteers were asked to perform their signature under different degrees of visual control: internally generated movement with closed eyes, signing with open eyes, tracking the line of the projected signature forwards, and tracking the line of the projected signature backwards. There was a gradual onset and disappearance of activation within a distributed network. Parietal, lateral and medial frontal brain areas were activated during all conditions, confirming the involvement of a parieto-frontal system. The weight of activation shifted with increasing task complexity. Internally generated movements activated predominantly the inferior parietal lobule and the ventral premotor cortex, as well as the rostral cingulate area, pre-supplementary motor area (pre-SMA) and SMA proper. Opening the eyes reduced SMA and cingulate activation and activated increasingly the occipito-parietal areas with higher task complexity. Visually guided movements produced an activation predominantly in the superior parietal lobule and dorsal premotor cortex. This study bridges human activation studies with the results of neurophysiological studies with monkeys. It confirms a gradual transition of visuomotor control with increasing task complexity within a distributed parieto-frontal network.

Anatomically informed basis functions in multisubject studies

We describe the use of anatomically informed basis functions (AIBF) in the analysis of multisubject functional imaging studies. AIBF are used to specify an anatomically informed spatial model that embodies anatomical knowledge for the statistical analysis of neuroimaging data. In a previous communication, we showed how AIBF can be used to incorporate prior anatomical constraints in single subject functional magnetic resonance image (fMRI) analyses to augment their anatomical precision. In this paper, we extend AIBF such that it can be applied to multisubject studies using fMRI or PET. The key concept is that, after spatial normalization, a canonical cortical surface can be used to generate a forward model of signal sources for all subjects. By estimating the hemodynamic signal in this canonical AIBF-space and then projecting it back into the voxel-space, one effectively extracts functional activity that is smooth, within and only within, the cortical sheet while attenuating other components unrelated to the physiological process of interest. The ensuing procedure can be considered as a highly non-stationary, anisotropic anatomically informed [de]convolution or smoothing. It is shown that this procedure offers various advantages compared to existing conventional methods for the analysis of multisubject studies, in particular it is more sensitive to underlying activations.

Classical and Bayesian inference in neuroimaging: applications

In Friston et al. ((2002) Neuroimage 16: 465-483) we introduced empirical Bayes as a potentially useful way to estimate and make inferences about effects in hierarchical models. In this paper we present a series of models that exemplify the diversity of problems that can be addressed within this framework. In hierarchical linear observation models, both classical and empirical Bayesian approaches can be framed in terms of covariance component estimation (e.g., variance partitioning). To illustrate the use of the expectation-maximization (EM) algorithm in covariance component estimation we focus first on two important problems in fMRI: nonsphericity induced by (i) serial or temporal correlations among errors and (ii) variance components caused by the hierarchical nature of multisubject studies. In hierarchical observation models, variance components at higher levels can be used as constraints on the parameter estimates of lower levels. This enables the use of parametric empirical Bayesian (PEB) estimators, as distinct from classical maximum likelihood (ML) estimates. We develop this distinction to address: (i) The difference between response estimates based on ML and the conditional means from a Bayesian approach and the implications for estimates of intersubject variability. (ii) The relationship between fixed- and random-effect analyses. (iii) The specificity and sensitivity of Bayesian inference and, finally, (iv) the relative importance of the number of scans and subjects. The forgoing is concerned with within- and between-subject variability in multisubject hierarchical fMRI studies. In the second half of this paper we turn to Bayesian inference at the first (within-voxel) level, using PET data to show how priors can be derived from the (between-voxel) distribution of activations over the brain. This application uses exactly the same ideas and formalism but, in this instance, the second level is provided by observations over voxels as opposed to subjects. The ensuing posterior probability maps (PPMs) have enhanced anatomical precision and greater face validity, in relation to underlying anatomy. Furthermore, in comparison to conventional SPMs they are not confounded by the multiple comparison problem that, in a classical context, dictates high thresholds and low sensitivity. We conclude with some general comments on Bayesian approaches to image analysis and on some unresolved issues.

Classical and Bayesian inference in neuroimaging: theory

This paper reviews hierarchical observation models, used in functional neuroimaging, in a Bayesian light. It emphasizes the common ground shared by classical and Bayesian methods to show that conventional analyses of neuroimaging data can be usefully extended within an empirical Bayesian framework. In particular we formulate the procedures used in conventional data analysis in terms of hierarchical linear models and establish a connection between classical inference and parametric empirical Bayes (PEB) through covariance component estimation. This estimation is based on an expectation maximization or EM algorithm. The key point is that hierarchical models not only provide for appropriate inference at the highest level but that one can revisit lower levels suitably equipped to make Bayesian inferences. Bayesian inferences eschew many of the difficulties encountered with classical inference and characterize brain responses in a way that is more directly predicated on what one is interested in. The motivation for Bayesian approaches is reviewed and the theoretical background is presented in a way that relates to conventional methods, in particular restricted maximum likelihood (ReML). This paper is a technical and theoretical prelude to subsequent papers that deal with applications of the theory to a range of important issues in neuroimaging. These issues include; (i) Estimating nonsphericity or variance components in fMRI time-series that can arise from serial correlations within subject, or are induced by multisubject (i.e., hierarchical) studies. (ii) Spatiotemporal Bayesian models for imaging data, in which voxels-specific effects are constrained by responses in other voxels. (iii) Bayesian estimation of nonlinear models of hemodynamic responses and (iv) principled ways of mixing structural and functional priors in EEG source reconstruction. Although diverse, all these estimation problems are accommodated by the PEB framework described in this paper.

Structural and functional cortical abnormalities after upper limb amputation during childhood

Functional reorganization has been well documented in the human adult brain after amputation of the arm. To assess the effects of amputation on the developing brain, we investigated six patients with upper limb amputation in early childhood and one with right dysmelia. Transcranial magnetic stimulation indicated contralateral cortical disinhibition and enlargement of the excitable area of the stump. FMRI data corroborated these plastic changes and also showed an ipsilateral functional reorganization. In the T1-weighted MRI, we found structural deformities of the contralateral and ipsilateral central sulcus in three patients and a contralateral atrophic parietal lobule in two patients. Therefore, arm amputation in childhood affects functional organization as well as anatomical structure in both hemispheres.

Multiple somatotopic representations in the human cerebellum

The classic view of representation in the cerebellum assumes two homunculi, one in the anterior lobe and one in the posterior lobe. Functional imaging has confirmed this somatotopy in the human anterior lobe but not, so far, in the posterior lobe. Using fMRI, we found separate peaks of activation for finger and toe in three ipsilateral cerebellar regions. In both the anterior and posterior lobe, the toe representation was semicircular around the finger area, with peaks of activation aligned in accord with the classic homunculi. Also, segregated peaks of activation were found in the pyramis vermis. These results confirm the existence of a second homunculus in the posterior lobe of the human cerebellum and suggest a third one.

A blueprint for movement: functional and anatomical representations in the human motor system

Despite a clear somatotopic organization of the motor cortex, a movement can be learned with one extremity and performed with another. This suggests that there exists a limb-independent coding for movements. To dissociate brain regions coding for movement parameters from those relevant to the chosen effector, subjects wrote their signature with their dominant index finger and ipsilateral big toe, and we determined those areas activated by both conditions using functional magnetic resonance imaging. The results show that movement parameters for this highly trained movement are stored in secondary sensorimotor cortices of the extremity with which it is usually performed, i.e., the dominant hand, including dorsal and ventral lateral premotor cortices. These areas can be accessed by the foot and are therefore functionally independent from the primary representation of the effector. Thus, somatotopy in secondary structures in the human motor system seems to be defined functionally, and not on the basis of anatomical representations.

Training-induced brain plasticity in aphasia

It has long been a matter of debate whether recovery from aphasia after left perisylvian lesions is mediated by the preserved left hemispheric language zones or by the homologous right hemisphere regions. Using PET, we investigated the short-term changes in the cortical network involved in language comprehension during recovery from aphasia. In 12 consecutive measurements of regional cerebral blood flow (rCBF), four patients with Wernicke's aphasia, caused by a posterior left middle cerebral artery infarction, were tested with a language comprehension task. Comprehension was estimated directly after each scan with a modified version of the Token Test. In the interval between the scans, the patients participated in brief, intense language comprehension training. A significant improvement in performance was observed in all patients. We correlated changes in blood flow measured during the language comprehension task with the scores achieved in the Token Test. The regions which best correlated with the training-induced improvement in verbal comprehension were the posterior part of the right superior temporal gyrus and the left precuneus. This study supports the role of the right hemisphere in recovery from aphasia and demonstrates that the improvement in auditory comprehension induced by specific training is associated with functional brain reorganization.

Detecting structural changes in whole brain based on nonlinear deformations-application to schizophrenia research

This paper describes a new method for detecting structural brain differences based on the analysis of deformation fields. Deformations are obtained by an intensity-based nonlinear registration routine that transforms one brain onto another one. We present a general multivariate statistical approach to analyze deformation fields in different subjects. This method was applied to the brains of 85 schizophrenic patients and 75 healthy volunteers to examine whether low frequency deformations are sufficiently sensitive to detect regional deviations in the brains of both groups. We observed significant changes caused by volume reduction in brains of schizophrenics bilaterally in the thalamus and in the superior temporal gyrus. On the left side, the superior frontal gyrus and precentral gyrus are found to be changed, while on the right side, the middle frontal gyrus was altered. In addition, there were significant changes in the occipital lobe (left lingual gyrus) and in the left cerebellum. Volume enlargement in brains of schizophrenics was observed in the right putamen and in the adjacent white matter of the thalamic region. Our data suggest a disturbance in the nodes of a prefrontal-thalamic-cerebellar circuitry. This provides further support for the model of "cognitive dysmetria," which postulates a disruption in these nodes. We have demonstrated the application of deformation-based morphometry by detecting structural changes in the whole brain. This technique is fully automatic, thus allowing for the inclusion of large samples, with no user bias or a priori-defined regions of interest.

Cortical reorganization in patients with facial palsy

Possible changes in the organization of the cortex in patients with facial palsy, serving as a model of peripheral motor deefferentation, were investigated by using transcranial magnetic stimulation (TMS) and positron emission tomography (PET). With TMS, the size of the area producing muscle-evoked potentials (MEPs) of the abductor pollicis brevis muscle, the sum of MEP amplitudes within this area, and the volume over the mapping area were compared between both hemispheres in 8 patients. With PET, increases in regional cerebral blood flow, measured with the standard H2(15)O2 bolus injection technique, were compared between 6 patients and 6 healthy volunteers during sequential finger opposition. Patients moved the hand ipsilateral to the facial palsy, the control subjects the right hand. Of 9 patients in total, 5 participated in both experiments. With both methods, an enlargement of the hand field contralateral to the facial palsy was found, extending in a lateral direction, into the site of the presumed face area. The PET data showed that the enlargement of the hand field in the somatosensory cortex (SMC) is part of a widespread cortical reorganization, including the ipsilateral SMC and bilateral secondary motor and sensory areas. We report for the first time, using two different noninvasive methods, that peripheral, mere motor deefferentation is a sufficient stimulus for reorganizational changes in the healthy adult human cortex.

Brain representation of active and passive movements

During active and passive (driven by a torque motor) flexion and extension of the right elbow, regional cerebral blood flow (rCBF) was measured in six healthy, male volunteers using positron emission tomography and the standard H2(15)O injection technique. During active as well as during passive movements of the right elbow there were strong increases in rCBF, identical in location, amount, and extent in the contralateral sensorimotor cortex. There were activations during both conditions in the supplementary motor area (stronger and more inferior in the active condition) and inferior parietal cortex (on the convexity during active movements and in the depth of the central sulcus during passive movements). During active movements only, activations of the basal ganglia and the cingulate gyrus were found. Brain activations during motor tasks are largely related to the processing of afferent information.