Publications

Penfield’s description of the ‘homunculus’, a ‘grotesque creature’ with large lips and hands and small trunk and legs depicting the representation of body-parts within the primary somatosensory cortex (S1), is one of the most prominent contributions to the neurosciences. Since then, numerous studies have identified additional body-parts representations outside of S1. Nevertheless, it has been implicitly assumed that S1’s homunculus is representative of the entire somatosensory cortex. Therefore, the distribution of body-parts representations in other brain regions, the property that gave Penfield’s homunculus its famous ‘grotesque’ appearance, has been overlooked. We used whole-body somatosensory stimulation, functional MRI and a new cortical parcellation to quantify the organization of the cortical somatosensory representation. Our analysis showed first, an extensive somatosensory response over the cortex; and second, that the proportional representation of body parts differs substantially between major neuroanatomical regions and from S1, with, for instance, much larger trunk representation at higher brain regions, potentially in relation to the regions’ functional specialization. These results extend Penfield’s initial findings to the higher level of somatosensory processing and suggest a major role for somatosensation in human cognition.

High-level cognitive capacities that serve communication, reasoning, and calculation are essential for finding our way in the world. But whether and to what extent these complex behaviors share the same neuronal substrate are still unresolved questions. The present study separated the aspects of logic from language and numerosity—mental faculties whose distinctness has been debated for centuries—and identified a new cytoarchitectonic area as correlate for an operation involving logical negation. A novel experimental paradigm that was implemented here in an RT/fMRI study showed a single cluster of activity that pertains to logical negation. It was distinct from clusters that were activated by numerical comparison and from the traditional language regions. The localization of this cluster was described by a newly identified cytoarchitectonic area in the left anterior insula, ventro-medial to Broca’s region. We provide evidence for the congruence between the histologically and functionally defined regions on multiple measures. Its position in the left anterior insula suggests that it functions as a mediator between language and reasoning areas.

The induction of immediate-early gene (IEG) expression in brain nuclei in response to an experience is necessary for the formation of long-term memories. Additionally, the rapid dynamics of IEG induction and decay motivates the common use of IEG expression as markers for identification of neuronal assemblies (“ensembles”) encoding recent experience. However, major gaps remain in understanding the rules governing the distribution of IEGs within neuronal assemblies. Thus, the extent of correlation between coexpressed IEGs, the cell specificity of IEG expression, and the spatial distribution of IEG expression have not been comprehensively studied. To address these gaps, we utilized quantitative multiplexed single-molecule fluorescence in situ hybridization (smFISH) and measured the expression of IEGs (Arc, Egr2, and Nr4a1) within spiny projection neurons (SPNs) in the dorsal striatum of mice following acute exposure to cocaine. Exploring the relevance of our observations to other brain structures and stimuli, we also analyzed data from a study of single-cell RNA sequencing of mouse cortical neurons. We found that while IEG expression is graded, the expression of multiple IEGs is tightly correlated at the level of individual neurons. Interestingly, we observed that region-specific rules govern the induction of IEGs in SPN subtypes within striatal subdomains. We further observed that IEG-expressing assemblies form spatially defined clusters within which the extent of IEG expression correlates with cluster size. Together, our results suggest the existence of IEG-expressing neuronal “superensembles,” which are associated in spatial clusters and characterized by coherent and robust expression of multiple IEGs.

Idiosyncratic tendency to choose one alternative over others in the absence of an identified reason, is a common observation in two-alternative forced-choice experiments. It is tempting to account for it as resulting from the (unknown) participant-specific history and thus treat it as a measurement noise. Indeed, idiosyncratic choice biases are typically considered as nuisance. Care is taken to account for them by adding an ad-hoc bias parameter or by counterbalancing the choices to average them out. Here we quantify idiosyncratic choice biases in a perceptual discrimination task and a motor task. We report substantial and significant biases in both cases. Then, we present theoretical evidence that even in idealized experiments, in which the settings are symmetric, idiosyncratic choice bias is expected to emerge from the dynamics of competing neuronal networks. We thus argue that idiosyncratic choice bias reflects the microscopic dynamics of choice and therefore is virtually inevitable in any comparison or decision task.

Behavior deviating from our normative expectations often appears irrational. For example, even though behavior following the so-called matching law can maximize reward in a stationary foraging task, actual behavior commonly deviates from matching. Such behavioral deviations are interpreted as a failure of the subject; however, here we instead suggest that they reflect an adaptive strategy, suitable for uncertain, non-stationary environments. To prove it, we analyzed the behavior of primates that perform a dynamic foraging task. In such nonstationary environment, learning on both fast and slow timescales is beneficial: fast learning allows the animal to react to sudden changes, at the price of large fluctuations (variance) in the estimates of task relevant variables. Slow learning reduces the fluctuations but costs a bias that causes systematic behavioral deviations. Our behavioral analysis shows that the animals solved this bias-variance tradeoff by combining learning on both fast and slow timescales, suggesting that learning on multiple timescales can be a biologically plausible mechanism for optimizing decisions under uncertainty.

Our goal in this study was to behaviorally characterize the property (or properties) that render negative quantifiers more complex in processing compared to their positive counterparts (e.g. the pair few/many). We examined two sources: (i) negative polarity; (ii) entailment reversal (aka downward monotonicity). While negative polarity can be found in other pairs in language such as dimensional adjectives (e.g. the pair small/large), only in quantifiers does negative polarity also reverse the entailment pattern of the sentence. By comparing the processing traits of negative quantifiers with those of non-monotone expressions that contain negative adjectives, using a verification task and measuring reaction times, we found that negative polarity is cognitively costly, but in downward monotone quantifiers it is even more so. We therefore conclude that both negative polarity and downward monotonicity contribute to the processing complexity of negative quantifiers.

Recent experiments demonstrate substantial volatility of excitatory connectivity in the absence of any learning. This challenges the hypothesis that stable synaptic connections are necessary for long-term maintenance of acquired information. Here we measure ongoing synaptic volatility and use theoretical modeling to study its consequences on cortical dynamics. We show that in the balanced cortex, patterns of neural activity are primarily determined by inhibitory connectivity, despite the fact that most synapses and neurons are excitatory. Similarly, we show that the inhibitory network is more effective in storing memory patterns than the excitatory one. As a result, network activity is robust to ongoing volatility of excitatory synapses, as long as this volatility does not disrupt the balance between excitation and inhibition. We thus hypothesize that inhibitory connectivity, rather than excitatory, controls the maintenance and loss of information over long periods of time in the volatile cortex.

It is generally believed that during economic decisions, striatal neurons represent the values associated with different actions. This hypothesis is based on studies, in which the activity of striatal neurons was measured while the subject was learning to prefer the more rewarding action. Here we show that these publications are subject to at least one of two critical confounds. First, we show that even weak temporal correlations in the neuronal data may result in an erroneous identification of action-value representations. Second, we show that experiments and analyses designed to dissociate action-value representation from the representation of other decision variables cannot do so. We suggest solutions to identifying action-value representation that are not subject to these confounds. Applying one solution to previously identified action-value neurons in the basal ganglia we fail to detect action-value representations. We conclude that the claim that striatal neurons encode action-values must await new experiments and analyses.

Exploration is a fundamental aspect of Reinforcement Learning, typically implemented using stochastic action-selection. Exploration, however, can be more efficient if directed toward gaining new world knowledge. Visit-counters have been proven useful both in practice and in theory for directed exploration. However, a major limitation of counters is their locality. While there are a few model-based solutions to this shortcoming, a model-free approach is still missing. We propose E-values, a generalization of counters that can be used to evaluate the propagating exploratory value over state-action trajectories. We compare our approach to commonly used RL techniques, and show that using E-values improves learning and performance over traditional counters. We also show how our method can be implemented with function approximation to efficiently learn continuous MDPs. We demonstrate this by showing that our approach surpasses state of the art performance in the Freeway Atari 2600 game.

According to the synaptic trace theory of memory, activity-induced changes in the pattern of synaptic connections underlie the storage of information for long periods. In this framework, the stability of memory critically depends on the stability of the underlying synaptic connections. Surprisingly however, synaptic connections in the living brain are highly volatile, which poses a fundamental challenge to the synaptic trace theory. Here we review recent experimental evidence that link the initial formation of a memory with changes in the pattern of connectivity, but also evidence that synaptic connections are considerably volatile even in the absence of learning. Then we consider different theoretical models that have been put forward to explain how memory can be maintained with such volatile building blocks.

hough often ignored, many studies have shown that implicit stimulus‐specific expectations play an important role in perception. However, what information about the prior distribution of stimuli is integrated into these perceptual expectations and how this information is utilized in the process of perceptual decision making is not clear.

Here we address this question for the case of a simple two‐tone discrimination task. We find a large perceptual bias favoring the mean of previous stimuli, i.e. “contraction bias” ‐small magnitudes are overestimated and large magnitudes are underestimated. We propose a biologically plausible computational model that accounts for this phenomenon in the general population.

We then apply this proposed model to a specific population ‐ dyslexics ‐ to characterize their poorer performance in this task computationally. Our findings show that dyslexics’ perceptual deficit can be accounted for by inadequate weighting of their implicit memory of past trials relative to their internal noise. Underweighting the stimulus statistics decreases dyslexics’ ability to compensate for noisy observations. This study provides the first description of a specific computational deficit associated with dyslexia.

It has long been known that we subjectively experience longer stimuli as being more intense. A recent study sheds light on the neural mechanisms underlying this bias by tracking the formation of a percept of intensity in the rat brain.

The selection and timing of actions are subject to determinate influences such as sensory cues and internal state as well as to effectively stochastic variability. Although stochastic choice mechanisms are assumed by many theoretical models, their origin and mechanisms remain poorly understood. Here we investigated this issue by studying how neural circuits in the frontal cortex determine action timing in rats performing a waiting task. Electrophysiological recordings from two regions necessary for this behavior, medial prefrontal cortex (mPFC) and secondary motor cortex (M2), revealed an unexpected functional dissociation. Both areas encoded deterministic biases in action timing, but only M2 neurons reflected stochastic trial-by-trial fluctuations. This differential coding was reflected in distinct timescales of neural dynamics in the two frontal cortical areas. These results suggest a two-stage model in which stochastic components of action timing decisions are injected by circuits downstream of those carrying deterministic bias signals.

There is a long history of experiments in which participants are instructed to generate a long sequence of binary random numbers. The scope of this line of research has shifted over the years from identifying the basic psychological principles and/or the heuristics that lead to deviations from randomness, to one of predicting future choices. In this paper, we used generalized linear regression and the framework of Reinforcement Learning in order to address both points. In particular, we used logistic regression analysis in order to characterize the temporal sequence of participants' choices. Surprisingly, a population analysis indicated that the contribution of the most recent trial has only a weak effect on behavior, compared to more preceding trials, a result that seems irreconcilable with standard sequential effects that decay monotonously with the delay. However, when considering each participant separately, we found that the magnitudes of the sequential effect are a monotonous decreasing function of the delay, yet these individual sequential effects are largely averaged out in a population analysis because of heterogeneity. The substantial behavioral heterogeneity in this task is further demonstrated quantitatively by considering the predictive power of the model. We show that a heterogeneous model of sequential dependencies captures the structure available in random sequence generation. Finally, we show that the results of the logistic regression analysis can be interpreted in the framework of reinforcement learning, allowing us to compare the sequential effects in the random sequence generation task to those in an operant learning task. We show that in contrast to the random sequence generation task, sequential effects in operant learning are far more homogenous across the population. These results suggest that in the random sequence generation task, different participants adopt different cognitive strategies to suppress sequential dependencies when generating the "random" sequences.

We investigated the course of language processing in the context of a verification task that required numerical estimation and comparison. Participants listened to sentences with complex quantifiers that contrasted in Polarity, a logical property (e.g., more-than-half, less-than-half), and then performed speeded verification on visual scenarios that displayed a proportion between 2 discrete quantities. We varied systematically not only the sentences, but also the visual materials, in order to study their effect on the verification process. Next, we used the same visual scenarios with analogous non-verbal probes that featured arithmetical inequality symbols (<, >). This manipulation enabled us to measure not only Polarity effects, but also, to compare the effect of different probe types (linguistic, non-linguistic) on processing. Like many previous studies, our results demonstrate that perceptual difficulty affects error rate and reaction time in keeping with Weber's Law. Interestingly, these performance parameters are also affected by the Polarity of the quantifiers used, despite the fact that sentences had the exact same meaning, sentence structure, number of words, syllables, and temporal structure. Moreover, an analogous contrast between the non-linguistic probes (<, >) had no effect on performance. Finally, we observed no interaction between performance parameters governed by Weber's Law and those affected by Polarity. We consider 4 possible accounts of the results (syntactic, semantic, pragmatic, frequency-based), and discuss their relative merit.

Dynamic remodeling of connectivity is a fundamental feature of neocortical circuits. Unraveling the principles underlying these dynamics is essential for the understanding of how neuronal circuits give rise to computations. Moreover, as complete descriptions of the wiring diagram in cortical tissues are becoming available, deciphering the dynamic elements in these diagrams is crucial for relating them to cortical function. Here, we used chronic in vivo two-photon imaging to longitudinally follow a few thousand dendritic spines in the mouse auditory cortex to study the determinants of these spines' lifetimes. We applied nonlinear regression to quantify the independent contribution of spine age and several morphological parameters to the prediction of the future survival of a spine. We show that spine age, size, and geometry are parameters that can provide independent contributions to the prediction of the longevity of a synaptic connection. In addition, we use this framework to emulate a serial sectioning electron microscopy experiment and demonstrate how incorporation of morphological information of dendritic spines from a single time-point allows estimation of future connectivity states. The distinction between predictable and nonpredictable connectivity changes may be used in the future to identify the specific adaptations of neuronal circuits to environmental changes. The full dataset is publicly available for further analysis. Significance statement: The neural architecture in the neocortex exhibits constant remodeling. The functional consequences of these modifications are poorly understood, in particular because the determinants of these changes are largely unknown. Here, we aimed to identify those modifications that are predictable from current network state. To that goal, we repeatedly imaged thousands of dendritic spines in the auditory cortex of mice to assess the morphology and lifetimes of synaptic connections. We developed models based on morphological features of dendritic spines that allow predicting future turnover of synaptic connections. The dynamic models presented in this paper provide a quantitative framework for adding putative temporal dynamics to the static description of a neuronal circuit from single time-point connectomics experiments.

Dyslexics are diagnosed for their poor reading skills, yet they characteristically also suffer from poor verbal memory and often from poor auditory skills. To date, this combined profile has been accounted for in broad cognitive terms. Here we hypothesize that the perceptual deficits associated with dyslexia can be understood computationally as a deficit in integrating prior information with noisy observations. To test this hypothesis we analyzed the performance of human participants in an auditory discrimination task using a two-parameter computational model. One parameter captures the internal noise in representing the current event, and the other captures the impact of recently acquired prior information. Our findings show that dyslexics' perceptual deficit can be accounted for by inadequate adjustment of these components; namely, low weighting of their implicit memory of past trials relative to their internal noise. Underweighting the stimulus statistics decreased dyslexics' ability to compensate for noisy observations. ERP measurements (P2 component) while participants watched a silent movie indicated that dyslexics' perceptual deficiency may stem from poor automatic integration of stimulus statistics. This study provides the first description of a specific computational deficit associated with dyslexia.