1,720,963 research outputs found
The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized Formation and Allocation of Multiple Memory Representations
No full textIt is commonly assumed that memories about experienced stimuli are represented by groups of highly interconnected neurons called cell assemblies. This requires allocating and storing information in the neural circuitry, which happens through synaptic weight adaptations at different types of synapses. In general, memory allocation is associated with synaptic changes at feed-forward synapses while memory storage is linked with adaptation of recurrent connections. It remains, however, largely unknown how memory allocation and storage can be achieved and the adaption of the different synapses involved be coordinated to allow for a faithful representation of multiple memories without disruptive interference between them. In this theoretical study, by using network simulations and phase space analyses, we show that the interplay between long-term synaptic plasticity and homeostatic synaptic scaling organizes simultaneously the adaptations of feed-forward and recurrent synapses such that a new stimulus forms a new memory and where different stimuli are assigned to distinct cell assemblies. The resulting dynamics can reproduce experimental in-vivo data, focusing on how diverse factors, such as neuronal excitability and network connectivity, influence memory formation. Thus, the here presented model suggests that a few fundamental synaptic mechanisms may suffice to implement memory allocation and storage in neural circuitry
Towards a biological plausible model of the interaction of long-term memory and working memory
No full textWorking Memory Requires a Combination of Transient and Attractor-Dominated Dynamics to Process Unreliably Timed Inputs
No full textAbstractWorking memory stores and processes information received as a stream of continuously incoming stimuli. This requires accurate sequencing and it remains puzzling how this can be reliably achieved by the neuronal system as our perceptual inputs show a high degree of temporal variability. One hypothesis is that accurate timing is achieved by purely transient neuronal dynamics; by contrast a second hypothesis states that the underlying network dynamics are dominated by attractor states. In this study, we resolve this contradiction by theoretically investigating the performance of the system using stimuli with differently accurate timing. Interestingly, only the combination of attractor and transient dynamics enables the network to perform with a low error rate. Further analysis reveals that the transient dynamics of the system are used to process information, while the attractor states store it. The interaction between both types of dynamics yields experimentally testable predictions and we show that this way the system can reliably interact with a timing-unreliable Hebbian-network representing long-term memory. Thus, this study provides a potential solution to the long-standing problem of the basic neuronal dynamics underlying working memory.</jats:p
Fast Dynamical Coupling Enhances Frequency Adaptation of Oscillators for Robotic Locomotion Control
Get PDFRhythmic neural signals serve as basis of many brain processes, in particular of locomotion control and generation of rhythmic movements. It has been found that specific neural circuits, named central pattern generators (CPGs), are able to autonomously produce such rhythmic activities. In order to tune, shape and coordinate the produced rhythmic activity, CPGs require sensory feedback, i.e., external signals. Nonlinear oscillators are a standard model of CPGs and are used in various robotic applications. A special class of nonlinear oscillators are adaptive frequency oscillators (AFOs). AFOs are able to adapt their frequency toward the frequency of an external periodic signal and to keep this learned frequency once the external signal vanishes. AFOs have been successfully used, for instance, for resonant tuning of robotic locomotion control. However, the choice of parameters for a standard AFO is characterized by a trade-off between the speed of the adaptation and its precision and, additionally, is strongly dependent on the range of frequencies the AFO is confronted with. As a result, AFOs are typically tuned such that they require a comparably long time for their adaptation. To overcome the problem, here, we improve the standard AFO by introducing a novel adaptation mechanism based on dynamical coupling strengths. The dynamical adaptation mechanism enhances both the speed and precision of the frequency adaptation. In contrast to standard AFOs, in this system, the interplay of dynamics on short and long time scales enables fast as well as precise adaptation of the oscillator for a wide range of frequencies. Amongst others, a very natural implementation of this mechanism is in terms of neural networks. The proposed system enables robotic applications which require fast retuning of locomotion control in order to react to environmental changes or conditions
The Processing and Storage of Information in Neuronal Memory Systems Across Time Scales
Get PDFHumans and animals are able to store and recall information about past experiences across a variety of time scales. This process is accomplished by memory, which is implemented in the neuronal systems of the brain. These neuronal systems consist of a large number of electrically excitable cells, called neurons, which interact via contact points called synapses. The transmission efficacies of these synapses can be adapted by processes summarized as synaptic plasticity.
Working memory (WM) describes the ability to store and to process information on time scales from seconds up to a minute and is important in many cognitive processes. The neuronal mechanisms underlying WM are still not understood. Some experimental and theoretical studies suggest that the neuronal system which implements WM stores information in the form of persistent activity of specific groups of neurons. These stable activity configurations are called attractor states. Other studies suggest that the information is stored in the form of complex temporal sequences of various activity patterns, so called transient trajectories. In this thesis, we show that the neuronal system implementing WM actually depends on both transient neuronal activity as well as distinct attractor states. Furthermore, we demonstrate that these attractor states may emerge in a self-organized way in the neuronal system implementing long-term memory (LTM) that stores information on time scales from hours to years. Finally, we develop a mechanism that may allow transient neuronal activity in the WM system to control long-lasting time-dependent output signals.
First, we show that, different from human subjects, a model of a neuronal system which solely operates on transient activity dynamics is not able to solve a typical WM task with unpredictable temporal structure. Remarkably, the performance of this system is restored by introducing distinct attractor states into the system dynamics. Still, the transient trajectories in between these attractor states are required to enable non-linear time-dependent processing. Thus, the neuronal system which implements WM requires both transient dynamics and distinct attractor states. Second, we demonstrate that these attractor states can be created by groups of strongly interconnected neurons, so called cell assemblies (CAs), formed in the neuronal system implementing LTM. We show that CAs may be reliably formed and allocated to different stimuli by an interplay of two synaptic plasticity processes. Hence, the attractor states required by the WM system may emerge in a self-organized way in the LTM system. Third, we present a mechanism which enables a short transient signal to adapt the autonomously produced periodic output signal of neuronal systems called central pattern generators (CPGs). This mechanism allows to fast and precisely adapt the frequency of general oscillatory systems in a self-organized way based on the frequency of a short periodic stimulation. Thus, it enables short-lasting transient trajectories in the WM system to evoke long-lasting time-dependent neuronal signals.
In summary, we show that to allow for WM that is robust with respect to unpredictable temporal structure and can perform complex non-linear processing, the underlying neuronal system has to rely on a combination of transient trajectories and distinct attractor states. These attractor states may emerge in a self-organized way in the LTM system and in CPGs
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
