A feature-based neurocomputational model of semantic memory

Modeling the contribution of theta-gamma coupling to sequential memory, imagination, and dreaming

Gamma oscillations nested in a theta rhythm are observed in the hippocampus, where are assumed to play a role in sequential episodic memory, i.e., memorization and retrieval of events that unfold in time. In this work, we present an original neurocomputational model based on neural masses, which simulates the encoding of sequences of events in the hippocampus and subsequent retrieval by exploiting the theta-gamma code. The model is based on a three-layer structure in which individual Units oscillate with a gamma rhythm and code for individual features of an episode. The first layer (working memory in the prefrontal cortex) maintains a cue in memory until a new signal is presented. The second layer (CA3 cells) implements an auto-associative memory, exploiting excitatory and inhibitory plastic synapses to recover an entire episode from a single feature. Units in this layer are disinhibited by a theta rhythm from an external source (septum or Papez circuit). The third layer (CA1 cells) implements a hetero-associative net with the previous layer, able to recover a sequence of episodes from the first one. During an encoding phase, simulating high-acetylcholine levels, the network is trained with Hebbian (synchronizing) and anti-Hebbian (desynchronizing) rules. During retrieval (low-acetylcholine), the network can correctly recover sequences from an initial cue using gamma oscillations nested inside the theta rhythm. Moreover, in high noise, the network isolated from the environment simulates a mind-wandering condition, randomly replicating previous sequences. Interestingly, in a state simulating sleep, with increased noise and reduced synapses, the network can "dream" by creatively combining sequences, exploiting features shared by different episodes. Finally, an irrational behavior (erroneous superimposition of features in various episodes, like "delusion") occurs after pathological-like reduction in fast inhibitory synapses. The model can represent a straightforward and innovative tool to help mechanistically understand the theta-gamma code in different mental states.

Different types of abstract concepts: evidence from two neurodegenerative patients

The observation of neurological patients showing selective impairments for specific conceptual categories contributed in the development of semantic memory theories. Here, we studied two patients (P01, P02), affected, respectively, by the semantic variant of Primary Progressive Aphasia (sv-PPA) and Cortico-Basal Syndrome (CBS). An implicit lexical decision task, including concrete (animals, tools) and abstract (emotions, social, quantity) concepts, was administered to patients and healthy controls.P01 and P02 showed an abolished priming effect for social and quantity-related concepts, respectively. This double dissociation suggests a role of different brain areas in representing specific abstract categories, giving insights for current semantic memory theories.

Neural computing in four spatial dimensions

Relationships among near set theory, shape maps and recent accounts of the Quantum Hall effect pave the way to neural networks computations performed in higher dimensions. We illustrate the operational procedure to build a real or artificial neural network able to detect, assess and quantify a fourth spatial dimension. We show how, starting from two-dimensional shapes embedded in a 2D topological charge pump, it is feasible to achieve the corresponding four-dimensional shapes, which encompass a larger amount of information. Synthesis of surface shape components, viewed topologically as shape descriptions in the form of feature vectors that vary over time, leads to a 4D view of cerebral activity. This novel, relatively straightforward architecture permits to increase the amount of available qbits in a fixed volume.

Emoji-based semantic representations for abstract and concrete concepts

An increasingly large body of converging evidence supports the idea that the semantic system is distributed across brain areas and that the information encoded therein is multimodal. Within this framework, feature norms are typically used to operationalize the various parts of meaning that contribute to define the distributed nature of conceptual representations. However, such features are typically collected as verbal strings, elicited from participants in experimental settings. If the semantic system is not only distributed (across features) but also multimodal, a cognitively sound theory of semantic representations should take into account different modalities in which feature-based representations are generated, because not all the relevant semantic information may be easily verbalized into classic feature norms, and different types of concepts (e.g., abstract vs. concrete concepts) may consist of different configurations of non-verbal features. In this paper we acknowledge the multimodal nature of conceptual representations and we propose a novel way of collecting non-verbal semantic features. In a crowdsourcing task we asked participants to use emoji to provide semantic representations for a sample of 300 English nouns referring to abstract and concrete concepts, which account for (machine readable) visual features. In a formal content analysis with multiple annotators we then classified the cognitive strategies used by the participants to represent conceptual content through emoji. The main results of our analyses show that abstract (vs. concrete) concepts are characterized by representations that: 1. consist of a larger number of emoji; 2. include more face emoji (expressing emotions); 3. are less stable and less shared among users; 4. use representation strategies based on figurative operations (e.g., metaphors) and strategies that exploit linguistic information (e.g. rebus); 5. correlate less well with the semantic representations emerging from classic features listed through verbal strings.

Core features: measures and characterization for different languages

According to the feature-based view of semantic representation, concepts can be represented as distributed networks of semantic features, which contribute with different weights to determine the overall meaning of a concept. The study of semantic features, typically collected in property generation tasks, is enriched with measures indicating the informativeness and distinctiveness of a given feature for the related concepts. However, while these measures have been provided in several languages (e.g. Italian, Spanish and English), they have hardly been applied comparatively across languages. The purpose of this paper is to investigate language-related differences and similarities emerging from the semantic representation of aggregated core features. Features with higher salience for a set of concrete concepts are identified and described in terms of their feature type. Then, comparisons are made between domains (natural vs. artefacts) and languages (Italian, Spanish and English) and descriptive statistics are provided. These results show that the characterization of concrete concepts is overall fairly stable across languages, although interesting cross-linguistic differences emerged. We will discuss the implications of our findings in relation to the theoretical paradigm of semantic feature norms, as well as in relation to speakers' mutual understanding in multilingual settings.

Consensus of uncertain multi-agent systems with input delay and disturbances

In this paper, the problem of robust consensus for multi-agent systems affected by external disturbances is discussed. A novel consensus control is developed by using a feedback controller based on disturbance rejection and Smith predictor scheme. Specifically, the disturbance rejection performance of the uncertain multi-agent systems is improved according to the estimation of equivalent-input-disturbance and the effect of time delay in the control system is reduced via Smith predictor scheme by shifting the delay outside the feedback loop. Furthermore, by combining Lyapunov theory, matrix inequality techniques and properties of Kronecker product, a robust feedback controller for each agent is designed such that the desired consensus of the uncertain multi-agent systems affected by external disturbances can be ensured. Finally, to illustrate the validity of the designed control scheme, two numerical examples with simulation results are provided.

Distinctive semantic features in the healthy adult brain

The role of semantic features, which are distinctive (e.g., a zebra's stripes) or shared (e.g. has four legs) for accessing a concept, has been studied in detail in early neurodegenerative disease such as semantic dementia (SD). However, potential neural underpinnings of such processing have not been studied in healthy adults. The current study examines neural activation patterns using fMRI while participants completed a feature verification task, in which they identified shared or distinctive semantic features for a set of natural kinds and man-made artifacts. The results showed that the anterior temporal lobe bilaterally is an important area for processing distinctive features, and that this effect is stronger within natural kinds than man-made artifacts. These findings provide converging evidence from healthy adults that is consistent with SD research, and support a model of semantic memory in which patterns of specificity of semantic information can partially explain differences in neural activation between categories.