Quantum Pattern Retrieval by Qubit Networks with Hebb Interactions

Qubit networks with long-range interactions inspired by the Hebb rule can be used as quantum associative memories. Starting from a uniform superposition, the unitary evolution generated by these interactions drives the network through a quantum phase transition at a critical computation time, after which ferromagnetic order guarantees that a measurement retrieves the stored memory. The maximum memory capacity p of these qubit networks is reached at a memory density p/n=1.Comment: To appear in Physical Review Letter

Semantic similarity dissociates shortfrom long-term recency effects: testing a neurocomputational model of list memory

The finding that recency effects can occur not only in immediate free recall (i.e., short-term recency) but also in the continuous-distractor task (i.e., long-term recency) has led many theorists to reject the distinction between short- and long-term memory stores. Recently, we have argued that long-term recency effects do not undermine the concept of a short-term store, and we have presented a neurocomputational model that accounts for both short- and long-term recency and for a series of dissociations between these two effects. Here, we present a new dissociation between short- and long-term recency based on semantic similarity, which is predicted by our model. This dissociation is due to the mutual support between associated items in the short-term store, which takes place in immediate free recall and delayed free recall but not in continuous-distractor free recall

Neural Networks retrieving Boolean patterns in a sea of Gaussian ones

Restricted Boltzmann Machines are key tools in Machine Learning and are described by the energy function of bipartite spin-glasses. From a statistical mechanical perspective, they share the same Gibbs measure of Hopfield networks for associative memory. In this equivalence, weights in the former play as patterns in the latter. As Boltzmann machines usually require real weights to be trained with gradient descent like methods, while Hopfield networks typically store binary patterns to be able to retrieve, the investigation of a mixed Hebbian network, equipped with both real (e.g., Gaussian) and discrete (e.g., Boolean) patterns naturally arises. We prove that, in the challenging regime of a high storage of real patterns, where retrieval is forbidden, an extra load of Boolean patterns can still be retrieved, as long as the ratio among the overall load and the network size does not exceed a critical threshold, that turns out to be the same of the standard Amit-Gutfreund-Sompolinsky theory. Assuming replica symmetry, we study the case of a low load of Boolean patterns combining the stochastic stability and Hamilton-Jacobi interpolating techniques. The result can be extended to the high load by a non rigorous but standard replica computation argument.Comment: 16 pages, 1 figur

Retrieval from memory: Vulnerable or inviolable?

We show that retrieval from semantic memory is vulnerable even to the mere presence of speech. Irrelevant speech impairs semantic fluency—namely, lexical retrieval cued by a semantic category name—but only if it is meaningful (forward speech compared to reversed speech or words compared to nonwords). Moreover, speech related semantically to the retrieval category is more disruptive than unrelated speech. That phonemic fluency—in which participants are cued with the first letter of words they are to report—was not disrupted by the mere presence of meaningful speech, only by speech in a related phonemic category, suggests that distraction is not mediated by executive processing load. The pattern of sensitivity to different properties of sound as a function of the type of retrieval cue is in line with an interference-by-process approach to auditory distraction

Long Term Effects of Chronic Variable Stress Administered during Different Developmental Stages in Mice

A number of studies have suggested that the occurrence of past trauma can increase an individual\u27s chance of developing PTSD from a new traumatic experience later in life. Trauma that occurs during childhood appears to have a particularly strong effect on this risk increase. Furthermore, conditioned fear responses have been shown to incubate over extended periods of time in animal models. To further investigate the role these phenomena play in the development of PTSD, this study exposed juvenile and adult mice to 7 days of chronic variable stress (CVS). One month later, a Pavlovian delay fear conditioning procedure was used to assess fear learning behavior, and anxiety levels were assessed with an Elevated Plus-Maze (EPM). It was hypothesized that mice who experienced CVS exposure as juveniles would show greater long-term levels of anxiety and long-term sensitization to later fear learning than mice who experienced CVS as adults. Furthermore, mice exposed to CVS, regardless of age, were hypothesized to show significantly enhanced anxiety and fear conditioning relative to control mice. Surprisingly, it was found that stress induced sensitization of fear conditioning deteriorated over the 30-day incubation period for both juvenile and adult mice, leading to no differences between groups, including controls, in fear learning behaviors. Adult stressed mice showed significantly greater anxiety levels than adult controls, while juvenile stressed and control mice showed no difference in anxiety. These results suggest possible neurological differences between juvenile and adult mice in regions involved in fear learning, such as the hippocampus, the central nucleus of the amygdale, and the bed nucleus of the stria terminalis. Alternative interpretations of the data are discussed. Despite failing to support the proposed hypotheses, this study suggests that a successful animal model of PTSD should consider the differential dynamics of associative and non-associative fear learning processes. Furthermore, the moderating effects of developmental stages on the effects of chronic stress should also be acknowledged and investigated further

Eye-movements in implicit artificial grammar learning

Artificial grammar learning (AGL) has been probed with forced-choice behavioral tests (active tests). Recent attempts to probe the outcomes of learning (implicitly acquired knowledge) with eye-movement responses (passive tests) have shown null results. However, these latter studies have not tested for sensitivity effects, for example, increased eye movements on a printed violation. In this study, we tested for sensitivity effects in AGL tests with (Experiment 1) and without (Experiment 2) concurrent active tests (preference- and grammaticality classification) in an eye-tracking experiment. Eye movements discriminated between sequence types in passive tests and more so in active tests. The eye-movement profile did not differ between preference and grammaticality classification, and it resembled sensitivity effects commonly observed in natural syntax processing. Our findings show that the outcomes of implicit structured sequence learning can be characterized in eye tracking. More specifically, whole trial measures (dwell time, number of fixations) showed robust AGL effects, whereas first-pass measures (first-fixation duration) did not. Furthermore, our findings strengthen the link between artificial and natural syntax processing, and they shed light on the factors that determine performance differences in preference and grammaticality classification tests.Max Planck Institute for PsycholinguisticsDonders Institute for Brain, Cognition and BehaviorVetenskapsradetSwedish Dyslexia Foundatio