Emili Balaguer-Ballester

Contextual encoding by ensembles of medial prefrontal cortex neurons

Contextual representations serve to guide many aspects of behavior and influence the way stimuli or actions are encoded and interpreted. The medial prefrontal cortex (mPFC), including the anterior cingulate subregion, has been implicated in contextual encoding, yet the nature of contextual representations formed by the mPFC is unclear. Using multiple single-unit tetrode recordings in rats, we found that different activity patterns emerged in mPFC ensembles when animals moved between different environmental contexts. These differences in activity patterns were significantly larger than those observed for hippocampal ensembles. Whereas ≈11% of mPFC cells consistently preferred one environment over the other across multiple exposures to the same environments, optimal decoding (prediction) of the environmental setting occurred when the activity of up to ≈50% of all mPFC neurons was taken into account. On the other hand, population activity patterns were not identical upon repeated exposures to the very same environment. This was partly because the state of mPFC ensembles seemed to systematically shift with time, such that we could sometimes predict the change in ensemble state upon later reentry into one environment according to linear extrapolation from the time-dependent shifts observed during the first exposure. We also observed that many strongly action-selective mPFC neurons exhibited a significant degree of context-dependent modulation. These results highlight potential differences in contextual encoding schemes by the mPFC and hippocampus and suggest that the mPFC forms rich contextual representations that take into account not only sensory cues but also actions and time.

Attracting dynamics of frontal cortex ensembles during memory-guided decision-making.

A common theoretical view is that attractor-like properties of neuronal dynamics underlie cognitive processing. However, although often proposed theoretically, direct experimental support for the convergence of neural activity to stable population patterns as a signature of attracting states has been sparse so far, especially in higher cortical areas. Combining state space reconstruction theorems and statistical learning techniques, we were able to resolve details of anterior cingulate cortex (ACC) multiple single-unit activity (MSUA) ensemble dynamics during a higher cognitive task which were not accessible previously. The approach worked by constructing high-dimensional state spaces from delays of the original single-unit firing rate variables and the interactions among them, which were then statistically analyzed using kernel methods. We observed cognitive-epoch-specific neural ensemble states in ACC which were stable across many trials (in the sense of being predictive) and depended on behavioral performance. More interestingly, attracting properties of these cognitively defined ensemble states became apparent in high-dimensional expansions of the MSUA spaces due to a proper unfolding of the neural activity flow, with properties common across different animals. These results therefore suggest that ACC networks may process different subcomponents of higher cognitive tasks by transiting among different attracting states.

Understanding Pitch Perception as a Hierarchical Process with Top-Down Modulation

Pitch is one of the most important features of natural sounds, underlying the perception of melody in music and prosody in speech. However, the temporal dynamics of pitch processing are still poorly understood. Previous studies suggest that the auditory system uses a wide range of time scales to integrate pitch-related information and that the effective integration time is both task- and stimulus-dependent. None of the existing models of pitch processing can account for such task- and stimulus-dependent variations in processing time scales. This study presents an idealized neurocomputational model, which provides a unified account of the multiple time scales observed in pitch perception. The model is evaluated using a range of perceptual studies, which have not previously been accounted for by a single model, and new results from a neurophysiological experiment. In contrast to other approaches, the current model contains a hierarchy of integration stages and uses feedback to adapt the effective time scales of processing at each stage in response to changes in the input stimulus. The model has features in common with a hierarchical generative process and suggests a key role for efferent connections from central to sub-cortical areas in controlling the temporal dynamics of pitch processing.

Studying the feasibility of a recommender in a citizen web portal based on user modeling and clustering algorithms

This paper presents a methodology to estimate the future success of a collaborative recommender in a citizen web portal. This methodology consists of four stages, three of them are developed in this study. First of all, a user model, which takes into account some usual characteristics of web data, is developed to produce artificial data sets. These data sets are used to carry out a clustering algorithm comparison in the second stage of our approach. This comparison provides information about the suitability of each algorithm in different scenarios. The benchmarked clustering algorithms are the ones that are most commonly used in the literature: c-Means, Fuzzy c-Means, a set of hierarchical algorithms, Gaussian mixtures trained by the expectation-maximization algorithm, and Kohonens self-organizing maps (SOM). The most accurate clustering is yielded by SOM. Afterwards, we turn to real data. The users of a citizen web portal (Infoville XXI, http://www.infoville.es) are clustered. The clustering achieved enables us to study the future success of a collaborative recommender by means of a prediction strategy. New users are recommended according to the cluster in which they have been classified. The suitability of the recommendation is evaluated by checking whether or not the recommended objects correspond to those actually selected by the user. The results show the relevance of the information provided by clustering algorithms in this web portal, and therefore, the relevance of developing a collaborative recommender for this web site.

Amphetamine Exerts Dose-Dependent Changes in Prefrontal Cortex Attractor Dynamics during Working Memory

Modulation of neural activity by monoamine neurotransmitters is thought to play an essential role in shaping computational neurodynamics in the neocortex, especially in prefrontal regions. Computational theories propose that monoamines may exert bidirectional (concentration-dependent) effects on cognition by altering prefrontal cortical attractor dynamics according to an inverted U-shaped function. To date, this hypothesis has not been addressed directly, in part because of the absence of appropriate statistical methods required to assess attractor-like behavior in vivo. The present study used a combination of advanced multivariate statistical, time series analysis, and machine learning methods to assess dynamic changes in network activity from multiple single-unit recordings from the medial prefrontal cortex (mPFC) of rats while the animals performed a foraging task guided by working memory after pretreatment with different doses of d-amphetamine (AMPH), which increases monoamine efflux in the mPFC. A dose-dependent, bidirectional effect of AMPH on neural dynamics in the mPFC was observed. Specifically, a 1.0 mg/kg dose of AMPH accentuated separation between task-epoch-specific population states and convergence toward these states. In contrast, a 3.3 mg/kg dose diminished separation and convergence toward task-epoch-specific population states, which was paralleled by deficits in cognitive performance. These results support the computationally derived hypothesis that moderate increases in monoamine efflux would enhance attractor stability, whereas high frontal monoamine levels would severely diminish it. Furthermore, they are consistent with the proposed inverted U-shaped and concentration-dependent modulation of cortical efficiency by monoamines.

Multi-site reproducibility of prefrontal-hippocampal connectivity estimates by stochastic DCM.

This study examined the reproducibility of prefrontal-hippocampal connectivity estimates obtained by stochastic dynamic causal modeling (sDCM). 180 healthy subjects were measured by functional magnetic resonance imaging (fMRI) during a standard working memory N-Back task at three different sites (Mannheim, Bonn, Berlin; each with 60 participants). The reproducibility of regional activations in key regions for working memory (dorsolateral prefrontal cortex, DLPFC; hippocampal formation, HF) was evaluated using conjunction analyses across locations. These analyses showed consistent activation of right DLPFC and deactivation of left HF across all three different sites. The effective connectivity between DLPFC and HF was analyzed using a simple two-region sDCM. For each subject, we evaluated sixty-seven alternative sDCMs and compared their relative plausibility using Bayesian model selection (BMS). Across all locations, BMS consistently revealed the same winning model, with the 2-Back working memory condition as driving input to both DLPFC and HF and with a connection from DLPFC to HF. Statistical tests on the sDCM parameter estimates did not show any significant differences across the three sites. The consistency of both the BMS results and model parameter estimates indicates the reliability of sDCM in our paradigm. This provides a basis for future genetic and clinical studies using this approach.

A cascade autocorrelation model of pitch perception

Autocorrelation algorithms, in combination with computational models of the auditory periphery, have been successfully used to predict the pitch of a wide range of complex stimuli. However, new stimuli are frequently offered as counterexamples to the viability of this approach. This study addresses the issue of whether in the light of these challenges the predictive power of autocorrelation can be preserved by changes to the peripheral model and the computational algorithm. An existing model is extended by the addition of a low-pass filter of the summary integration of the individual within-channel autocorrelations. Other recent developments are also incorporated, including nonlinear processing on the basilar membrane and the use of integration time constants that are proportional to the autocorrelation lags. The modified and extended model predicts with reasonable success the pitches of a range of stimuli that have proved problematic for earlier implementations of the autocorrelation principle. The evaluation stimuli include short tone sequences, click trains consisting of alternating interclick intervals, click trains consisting of mixtures of regular and irregular intervals, shuffled click trains, and transposed tones.

Lateral orbitofrontal cortex anticipates choices and integrates prior with current information

Adaptive behavior requires integrating prior with current information to anticipate upcoming events. Brain structures related to this computation should bring relevant signals from the recent past into the present. Here we report that rats can integrate the most recent prior information with sensory information, thereby improving behavior on a perceptual decision-making task with outcome-dependent past trial history. We find that anticipatory signals in the orbitofrontal cortex about upcoming choice increase over time and are even present before stimulus onset. These neuronal signals also represent the stimulus and relevant second-order combinations of past state variables. The encoding of choice, stimulus and second-order past state variables resides, up to movement onset, in overlapping populations. The neuronal representation of choice before stimulus onset and its build-up once the stimulus is presented suggest that orbitofrontal cortex plays a role in transforming immediate prior and stimulus information into choices using a compact state-space representation.

Assigning discounts in a marketing campaign by using reinforcement learning and neural networks

In this work, RL is used to find an optimal policy for a marketing campaign. Data show a complex characterization of state and action spaces. Two approaches are proposed to circumvent this problem. The first approach is based on the self-organizing map (SOM), which is used to aggregate states. The second approach uses a multilayer perceptron (MLP) to carry out a regression of the action-value function. The results indicate that both approaches can improve a targeted marketing campaign. Moreover, the SOM approach allows an intuitive interpretation of the results, and the MLP approach yields robust results with generalization capabilities.

Can We Identify Non-Stationary Dynamics of Trial-to-Trial Variability?

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rats frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings.