Salva Ardid

An Integrated Microcircuit Model of Attentional Processing in the Neocortex

Selective attention is a fundamental cognitive function that uses top-down signals to orient and prioritize information processing in the brain. Single-cell recordings from behaving monkeys have revealed a number of attention-induced effects on sensory neurons, and have given rise to contrasting viewpoints about the neural underpinning of attentive processing. Moreover, there is evidence that attentional signals originate from the prefrontoparietal working memory network, but precisely how a source area of attention interacts with a sensory system remains unclear. To address these questions, we investigated a biophysically based network model of spiking neurons composed of a reciprocally connected loop of two (sensory and working memory) networks. We found that a wide variety of physiological phenomena induced by selective attention arise naturally in such a system. In particular, our work demonstrates a neural circuit that instantiates the “feature-similarity gain modulation principle,” according to which the attentional gain effect on sensory neuronal responses is a graded function of the difference between the attended feature and the preferred feature of the neuron, independent of the stimulus. Furthermore, our model identifies key circuit mechanisms that underlie feature-similarity gain modulation, multiplicative scaling of tuning curve, and biased competition, and provide specific testable predictions. These results offer a synthetic account of the diverse attentional effects, suggesting a canonical neural circuit for feature-based attentional processing in the cortex.

Reconciling coherent oscillation with modulation of irregular spiking activity in selective attention: gamma-range synchronization between sensory and executive cortical areas.

In this computational work, we investigated gamma-band synchronization across cortical circuits associated with selective attention. The model explicitly instantiates a reciprocally connected loop of spiking neurons between a sensory-type (area MT) and an executive-type (prefrontal/parietal) cortical circuit (the source area for top-down attentional signaling). Moreover, unlike models in which neurons behave as clock-like oscillators, in our model single-cell firing is highly irregular (close to Poisson), while local field potential exhibits a population rhythm. In this “sparsely synchronized oscillation” regime, the model reproduces and clarifies multiple observations from behaving animals. Top-down attentional inputs have a profound effect on network oscillatory dynamics while only modestly affecting single-neuron spiking statistics. In addition, attentional synchrony modulations are highly selective: interareal neuronal coherence occurs only when there is a close match between the preferred feature of neurons, the attended feature, and the presented stimulus, a prediction that is experimentally testable. When interareal coherence was abolished, attention-induced gain modulations of sensory neurons were slightly reduced. Therefore, our model reconciles the rate and synchronization effects, and suggests that interareal coherence contributes to large-scale neuronal computation in the brain through modest enhancement of rate modulations as well as a pronounced attention-specific enhancement of neural synchrony.

Anterior Cingulate Cortex Cells Identify Process-Specific Errors of Attentional Control Prior to Transient Prefrontal-Cingulate Inhibition

Errors indicate the need to adjust attention for improved future performance. Detecting errors is thus a fundamental step to adjust and control attention. These functions have been associated with the dorsal anterior cingulate cortex (dACC), predicting that dACC cells should track the specific processing states giving rise to errors in order to identify which processing aspects need readjustment. Here, we tested this prediction by recording cells in the dACC and lateral prefrontal cortex (latPFC) of macaques performing an attention task that dissociated 3 processing stages. We found that, across prefrontal subareas, the dACC contained the largest cell populations encoding errors indicating (1) failures of inhibitory control of the attentional focus, (2) failures to prevent bottom-up distraction, and (3) lapses when implementing a choice. Error-locked firing in the dACC showed the earliest latencies across the PFC, emerged earlier than reward omission signals, and involved a significant proportion of putative inhibitory interneurons. Moreover, early onset error-locked response enhancement in the dACC was followed by transient prefrontal-cingulate inhibition, possibly reflecting active disengagement from task processing. These results suggest a functional specialization of the dACC to track and identify the actual processes that give rise to erroneous task outcomes, emphasizing its role to control attentional performance.

Mapping of Functionally Characterized Cell Classes onto Canonical Circuit Operations in Primate Prefrontal Cortex

Microcircuits are composed of multiple cell classes that likely serve unique circuit operations. But how cell classes map onto circuit functions is largely unknown, particularly for primate prefrontal cortex during actual goal-directed behavior. One difficulty in this quest is to reliably distinguish cell classes in extracellular recordings of action potentials. Here we surmount this issue and report that spike shape and neural firing variability provide reliable markers to segregate seven functional classes of prefrontal cells in macaques engaged in an attention task. We delineate an unbiased clustering protocol that identifies four broad spiking (BS) putative pyramidal cell classes and three narrow spiking (NS) putative inhibitory cell classes dissociated by how sparse, bursty, or regular they fire. We speculate that these functional classes map onto canonical circuit functions. First, two BS classes show sparse, bursty firing, and phase synchronize their spiking to 3–7 Hz (theta) and 12–20 Hz (beta) frequency bands of the local field potential (LFP). These properties make cells flexibly responsive to network activation at varying frequencies. Second, one NS and two BS cell classes show regular firing and higher rate with only marginal synchronization preference. These properties are akin to setting tonically the excitation and inhibition balance. Finally, two NS classes fired irregularly and synchronized to either theta or beta LFP fluctuations, tuning them potentially to frequency-specific subnetworks. These results suggest that a limited set of functional cell classes emerges in macaque prefrontal cortex (PFC) during attentional engagement to not only represent information, but to subserve basic circuit operations.

A Tweaking Principle for Executive Control: Neuronal Circuit Mechanism for Rule-Based Task Switching and Conflict Resolution

A hallmark of executive control is the brains agility to shift between different tasks depending on the behavioral rule currently in play. In this work, we propose a “tweaking hypothesis” for task switching: a weak rule signal provides a small bias that is dramatically amplified by reverberating attractor dynamics in neural circuits for stimulus categorization and action selection, leading to an all-or-none reconfiguration of sensory-motor mapping. Based on this principle, we developed a biologically realistic model with multiple modules for task switching. We found that the model quantitatively accounts for complex task switching behavior: switch cost, congruency effect, and task-response interaction; as well as monkeys single-neuron activity associated with task switching. The model yields several testable predictions, in particular, that category-selective neurons play a key role in resolving sensory-motor conflict. This work represents a neural circuit model for task switching and sheds insights in the brain mechanism of a fundamental cognitive capability.

What Can Tracking Fluctuations in Dozens of Sensory Neurons Tell about Selective Attention

The brain possesses limited resources and utilizes selective attention as the mechanism to manage the massive influx of sensory information into the cortex. Selective attention strengthens the impact of behaviorally relevant information and diminishes distractions from irrelevant inputs. For instance, in visual discrimination or detection tasks, proper allocation of attention improves performance and shortens response times. At the neural level, there are many different effects of attention on the response of sensory neurons: receptive field shrinkages, modulation of neural synchronization and mean activity, variability reduction, interneuronal decorrelations, and more (Reynolds and Chelazzi, 2004). Yet few experiments have attempted to determine how attentional correlates subserve behavioral benefits (Womelsdorf et al., 2006).

Neuroswarm: A Methodology to Explore the Constraints that Function Imposes on Simulation Parameters in Large-Scale Networks of Biological Neurons

Candidate mechanisms of brain function can potentially be identified using biologically detailed computational models. A critical question that arises from the construction and analysis of such models is whether a particular set of parameters is unique or whether multiple different solutions exist, each capable of reproducing some relevant phenomenology. Addressing this issue is difficult, and systematic procedures have been proposed only recently, targeting small systems such as single neurons or small neural circuits [16] (Marder and Taylor, Nat Neurosci 14:133–138, 2011), [1] (Achard and De Schutter, PLoS Comput Biol 2:e94, 2006). However, how to develop a methodology to address the problem of non-uniqueness of parameters in large-scale biological networks is yet to be developed. Here, we describe a computational strategy to explicitly approach this issue on large-scale neural network models, which has been successfully applied to computational models of working memory (WM) and selective attention [2] (Ardid, J Neurosci Off J Soc Neurosci 30:2856–2870, 2010), [3] (Cano-Colino et al., Cereb Cortex 24:2449–2463, 2014). To illustrate the approach, we show in this chapter how our strategy applies to the problem of identifying different mechanisms underlying visuospatial WM. We use a well-established biological neural circuit model in the literature [6] (Compte et al., Cereb. Cortex 10:910–923, 2000) as a reference point, which we then perturb by using the Swarm Optimization Algorithm. This algorithm explores the space of biologically unconstrained parameters in the model under the constraint of preserving a solution defined here as a network in which the activity of model neurons mimics the properties of neurons in the dorsolateral prefrontal cortex (dlPFC) of monkeys performing a visuospatial WM task [7] (Funahashi et al., J Neurophysiol 61:331–349, 1989). The results are: (1) identification of a set of model solutions, composed of alternative and, in principle, feasible and sufficient mechanisms generating WM function in a cortical network. In particular, we found that the dynamics of interneurons play a main role in distinguishing among potential circuit candidates. Secondly we uncovered compensatory mechanisms in a subset of the parameters in the model. In essence, the compensatory mechanisms we observe in the different solutions are based on correlations between sets of parameters that shift the local Excitatory/Inhibitory balance in opposite directions. In summary, our approach is able to identify distinct mechanisms underlying a same function, as well as to propose a dynamic solution to the problem of fine-tuning. Our results from the proposed workflow would be strengthened by additional biological experiments aimed to refine the validity of the results.

Attentional selection can be predicted by reinforcement learning of task-relevant stimulus features weighted by value-independent stickiness

Attention includes processes that evaluate stimuli relevance, select the most relevant stimulus against less relevant stimuli, and bias choice behavior toward the selected information. It is not clear how these processes interact. Here, we captured these processes in a reinforcement learning framework applied to a feature-based attention task that required macaques to learn and update the value of stimulus features while ignoring nonrelevant sensory features, locations, and action plans. We found that value-based reinforcement learning mechanisms could account for feature-based attentional selection and choice behavior but required a value-independent stickiness selection process to explain selection errors while at asymptotic behavior. By comparing different reinforcement learning schemes, we found that trial-by-trial selections were best predicted by a model that only represents expected values for the task-relevant feature dimension, with nonrelevant stimulus features and action plans having only a marginal influence on covert selections. These findings show that attentional control subprocesses can be described by (1) the reinforcement learning of feature values within a restricted feature space that excludes irrelevant feature dimensions, (2) a stochastic selection process on feature-specific value representations, and (3) value-independent stickiness toward previous feature selections akin to perseveration in the motor domain. We speculate that these three mechanisms are implemented by distinct but interacting brain circuits and that the proposed formal account of feature-based stimulus selection will be important to understand how attentional subprocesses are implemented in primate brain networks.

DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation

DynaSim is an open-source MATLAB/GNU Octave toolbox for rapid prototyping of neural models and batch simulation management. It is designed to speed up and simplify the process of generating, sharing, and exploring network models of neurons with one or more compartments. Models can be specified by equations directly (similar to XPP or the Brian simulator) or by lists of predefined or custom model components. The higher-level specification supports arbitrarily complex population models and networks of interconnected populations. DynaSim also includes a large set of features that simplify exploring model dynamics over parameter spaces, running simulations in parallel using both multicore processors and high-performance computer clusters, and analyzing and plotting large numbers of simulated data sets in parallel. It also includes a graphical user interface (DynaSim GUI) that supports full functionality without requiring user programming. The software has been implemented in MATLAB to enable advanced neural modeling using MATLAB, given its popularity and a growing interest in modeling neural systems. The design of DynaSim incorporates a novel schema for model specification to facilitate future interoperability with other specifications (e.g., NeuroML, SBML), simulators (e.g., NEURON, Brian, NEST), and web-based applications (e.g., Geppetto) outside MATLAB. DynaSim is freely available at http://dynasimtoolbox.org. This tool promises to reduce barriers for investigating dynamics in large neural models, facilitate collaborative modeling, and complement other tools being developed in the neuroinformatics community.

Biased competition in the absence of input bias: predictions from corticostriatal computation

Classical accounts of biased competition (BC) 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 projecting neurons (SPNs) receive balanced cortical input, yet their activity determines the choice between GO and NO-GO pathways in the basal ganglia. We present a corticostriatal model identifying three mechanisms that rely on physiological asymmetries to effect rate- and time-coded BC in the presence of balanced inputs. First, tonic input strength determines which SPN phenotype exhibit higher mean firing rate (FR). Second, low strength oscillatory inputs induce higher FR in D2 SPNs but higher coherence between D1 SPNs. Third, high strength inputs oscillating at distinct frequencies preferentially activate D1 or D2 SPN populations. Of these mechanisms, the latter accommodates observed rhythmic activity supporting rule-based decision making in prefrontal cortex.