Attractor Decision Network with Selective Inhibition

The ability to decide swiftly and accurately in an urgent scenario is crucial for an organism's survival. The neural mechanisms underlying the perceptual decision and trade-off between speed and accuracy have been extensively studied in the past few decades. Among several theoretical models, the attractor neural network model has successfully captured both behavioral and neuronal data observed in many decision experiments. However, a recent experimental study revealed additional details that were not considered in the original attractor model. In particular, the study shows that the inhibitory neurons in the posterior parietal cortex of mice are as selective to decision making results as the excitatory neurons, whereas the original attractor model assumes the inhibitory neurons to be unselective. In this study, we investigate a more general attractor model with selective inhibition, and analyze in detail how the computational ability of the network changes with selectivity. We proposed a reduced model for the selective model, and showed that selectivity adds a time-varying component to the energy landscape. This time dependence of the energy landscape allows the selective model to integrate information carefully in initial stages, then quickly converge to an attractor once the choice is clear. This results in the selective model having a more efficient speed-accuracy trade-off that is unreachable by unselective models.

Functional coupling between target selection and acquisition in the superior colliculus

Survival in unpredictable environments requires that animals continuously evaluate their surroundings for behavioral targets, direct their movements towards those targets, and terminate movements once a target is reached. The ability to select, move toward, and acquire spatial targets depends on a network of brain regions, but it remains unknown how these goal-directed processes are linked by neural circuits. Within this network, common circuits in the midbrain superior colliculus (SC) mediate the selection of, and initiation of movements to, spatial targets. However, SC activity often persists throughout movement, suggesting that the same SC circuits underlying target selection and movement initiation may also contribute to target acquisition: stopping the movement at the selected target. Here, we examine the hypothesis that SC functional circuitry couples target selection and acquisition using a default motor plan generated by selection-related neuronal activity. Recordings from intermediate and deep layer SC neurons in mice performing a spatial choice task demonstrate that choice-predictive neurons, including optogenetically identified GABAergic neurons whose activity mediates target selection, exhibit increased activity during movement to the target. By recording from rostral and caudal SC in separate groups of mice, we also revealed higher activity in rostral than caudal neurons during target acquisition. Finally, we used an attractor model to examine how, invoking only SC circuitry, caudal SC activity related to selecting an eccentric target could generate higher rostral than caudal acquisition-related activity. Overall, our results suggest a functional coupling between SC circuits for target selection and acquisition, elucidating a key mechanism for goal-directed behavior.

A metastable attractor model of self–other integration (MEAMSO) in rhythmic synchronization

Human interaction is often accompanied by synchronized bodily rhythms. Such synchronization may emerge spontaneously as when a crowd's applause turns into a steady beat, be encouraged as in nursery rhymes, or be intentional as in the case of playing music together. The latter has been extensively studied using joint finger-tapping paradigms as a simplified version of rhythmic interpersonal synchronization. A key finding is that synchronization in such cases is multifaceted, with synchronized behaviour resting upon different synchronization strategies such as mutual adaptation, leading–following and leading–leading. However, there are multiple open questions regarding the mechanism behind these strategies and how they develop dynamically over time. Here, we propose a metastable attractor model of self–other integration (MEAMSO). This model conceptualizes dyadic rhythmic interpersonal synchronization as a process of integrating and segregating signals of self and other. Perceived sounds are continuously evaluated as either being attributed to self -produced or other -produced actions. The model entails a metastable system with two particular attractor states: one where an individual maintains two separate predictive models for self - and other -produced actions, and the other where these two predictive models integrate into one. The MEAMSO explains the three known synchronization strategies and makes testable predictions about the dynamics of interpersonal synchronization both in behaviour and the brain. This article is part of the theme issue ‘Synchrony and rhythm interaction: from the brain to behavioural ecology’.

Modeling Changes in the Enterprise Information Capital in the Digital Economy

The global COVID-19 pandemic has led to the self-isolation of people and the transformation of many economic and social processes into an electronic version thus contributing to the digitalization of all spheres. Being part of this environment, enterprises generate information resources to develop their desired image, which may vary according to the factors characterizing the information environment. Information capital is a comprehensive characteristic of an enterprise and determines its effectiveness and sustainability. The purpose of this study is to develop a toolkit that allows one to assess the information capital of an enterprise, reflecting its perception within the digital information environment. It is necessary to develop the methodology for the formation of such tools. As a result, a fuzzy-plural approach has been developed to evaluate the index of external information capital. This model allows us to assess the external information capital and to simulate its changes caused by various kinds of information events. The study of key elements, for example, the stability and tonality indices, index of target perception made it possible to systematize chaotic changes in the external environment and describe them using the Chen–Lee attractor model. The results of this study can be useful for researchers in the field of digital information analysis, in particular for the comparative analysis of enterprises and the assessment of their information capital.

Functional coupling between target selection and acquisition in the superior colliculus

To survive in unpredictable environments, animals must continuously evaluate their surroundings for behavioral targets, such as food and shelter, and direct their movements to acquire those targets. Although the ability to accurately select and acquire spatial targets depends on a shared network of brain regions, how these processes are linked by neural circuits remains unknown. The superior colliculus (SC) mediates the selection of spatial targets and remains active during orienting movements to acquire targets, which suggests the underexamined possibility that common SC circuits underie both selection and acquisition processes. Here, we test the hypothesis that SC functional circuitry couples target selection and acquisition using a default motor plan generated by selection-related neuronal activity. Single-unit recordings from intermediate and deep layer SC neurons in male mice performing a spatial choice task demonstrated that choice-predictive neurons, including optogenetically identified GABAergic SC neurons whose activity was causally related to target selection, exhibit increased activity during movement to the target. By strategically recording from both rostral and caudal SC neurons, we also revealed an overall caudal-to-rostral shift in activity as targets were acquired. Finally, we used an attractor model to examine how target selection activity in the SC could generate a rostral shift in activity during target acquisition using only intrinsic SC circuitry. Overall, our results suggest a functional coupling between SC circuits that underlie target selection and acquisition, elucidating a key mechanism for goal-directed behavior.

Learning accurate path integration in a ring attractor model of the head direction system

SummaryRing attractor models for angular path integration have recently received strong experimental support. To function as integrators, head-direction (HD) circuits require precisely tuned connectivity, but it is currently unknown how such tuning could be achieved. Here, we propose a network model in which a local, biologically plausible learning rule adjusts synaptic efficacies during development, guided by supervisory allothetic cues. Applied to the Drosophila HD system, the model learns to path-integrate accurately and develops a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading, and where the network remaps to integrate with different gains. Our model predicts that path integration requires supervised learning during a developmental phase. The model setting is general and also applies to architectures that lack the physical topography of a ring, like the mammalian HD system.

Flexible categorization in perceptual decision making

Perceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.