Neural Mechanisms Underlying the Generation of the Lobster Gastric Mill Motor Pattern

The lobster gastric mill central pattern generator (CPG) is located in the stomatogastric ganglion and consists of 11 neurons whose circuitry is well known. Because all of the neurons are identifiable and accessible, it can serve as a prime experimental model for analyzing how microcircuits generate multiphase oscillatory spatiotemporal patterns. The neurons that comprise the gastric mill CPG consist of one interneuron, five burster neurons and six tonically firing neurons. The single interneuron (Int 1) is shared by the medial tooth subcircuit (containing the AM, DG and GMs) and the lateral teeth subcircuit (LG, MG and LPGs). By surveying cell-to-cell connections and the cooperative dynamics of the neurons we find that the medial subcircuit is essentially a feed forward system of oscillators. The Int 1 neuron entrains the DG and AM cells by delayed excitation and this pair then periodically inhibits the tonically firing GMs causing them to burst. The lateral subcircuit consists of two negative feedback loops of reciprocal inhibition from Int 1 to the LG/MG pair and from the LG/MG to the LPGs. Following a fast inhibition from Int 1, the LG/MG neurons receive a slowly developing excitatory input similar to that which Int 1 puts onto DG/AM. Thus Int 1 plays a key role in synchronizing both subcircuits. This coordinating role is assisted by additional, weaker connections between the two subsets but those are not sufficient to synchronize them in the absence of Int 1. In addition to the experiments, we developed a conductance-based model of a slightly simplified gastric circuit. The mathematical model can reproduce the fundamental rhythm and many of the experimentally induced perturbations. Our findings shed light on the functional role of every cell and synapse in this small circuit providing a detailed understanding of the rhythm generation and pattern formation in the gastric mill network

Models wagging the dog: are circuits constructed with disparate parameters?

In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken

Movement Pattern Recognition in Physical Rehabilitation - Cognitive Motivation-based IT Method and Algorithms

In this paper, a solution is presented to support both existing and future movement rehabilitation applications. The presented method combines the advantages of human-computer interaction-based movement therapy, with the cognitive property of intelligent decision-making systems. With this solution, therapy could be fully adapted to the needs of the patients and conditions while maintaining a sense of success in them, thereby motivating them. In our modern digital age, the development of HCI interfaces walks together with the growth of users’ needs. The available technologies have limitations, which can reduce the effectiveness of modern input devices, such as the Kinect sensor or any other similar sensors. In this article, multiple newly developed and modified methods are introduced with the aim to overcome these limitations. These methods can fully adapt the movement pattern recognition to the users' skills. The main goals are to apply this method in movement rehabilitation, where the supervisor, a therapist can personalize the rehabilitation exercises due to the Distance Vector-based Gesture Recognition (DVGR), Reference Distance-based Synchronous/Asynchronous Movement Recognition (RDSMR/RDAMR) and the Real-Time Adaptive Movement Pattern Classification (RAMPC) methods

Dyferencjacja, autonomia dorastających z perspektywy terapii rodzinnej

The paper starts from the premise that late and not always complete differentiation from the family of origin is a significant problem in our society. We intend to discuss this issue by referring to individual developmental and family therapy models. The paper provides an overview of how different schools of family therapy, especially the transgenerational school, treat the question of individual differentiation. The other model to be described is Erik Erikson’s model of the individual psychosocial development. In summary, we aim to provide insights into comprehending the questions and difficulties of differentiation, and suggest possible ways of how the parent – child relations can develop for the child to reach confident adulthood from the inherent immaturity of adolescence.Artykuł wychodzi z założenia, że późna i nie zawsze całkowita dyferencjacja z rodziny pochodzenia jest istotnym problemem w naszym społeczeństwie. Zamierzamy omówić tę kwestię, odnosząc się do modelu terapii indywidualnego rozwoju oraz modelu terapii rodzinnej. Praca ta przedstawia przegląd, jak różne szkoły terapii rodzinnej, szczególnie szkoła międzypokoleniowa, traktują kwestię indywidualnej dyferencjacji. Drugim modelem tu przedstawionym jest model Erika Eriksona, model indywidualnego psychospołecznego rozwoju. Podsumowując, staramy się zapewnić dogłębne zrozumienie problemów i trudności dyferencjacji oraz zasugerować możliwe sposoby, by relacje rodzice – dzieci mogły rozwijać się tak, by dziecko osiągnęło bezpieczną dorosłość z przyrodzonej niedojrzałości dorastania

The Cognitive Motivation-based APBMR Algorithm in Physical Rehabilitation

This article presents a new, alternative method of gesture recognition using the cognitive properties of intelligent decision-making systems to support the rehabilitation process of people with disabilities: the Asynchronous Prediction-Based Movement Recognition (APBMR) algorithm. The algorithm “predicts” the next movement of the user by evaluating the previous three with the goal to maintain motivation. Based on the prediction, it creates acceptance domains and decides whether the next user-input gesture can be considered the same movement. For this, the APBMR algorithm uses six mean techniques: the Arithmetic, Geometric, Harmonic, Contrahamonic, Quadratic and the Cubic ones. The purpose of this article besides presenting this new method is to evaluate which mean technique to use with the three different acceptance domains. The authors evaluated the algorithm in real-time using a general and an advanced computer, as well as they tested it by predicting from a file and also compared the algorithm to one of their earlier works. The tests were done by four groups of users, respectively, each group doing four gestures. After analyzing the results, the authors concluded that the Contraharmonic mean technique gives the best average gesture acceptance rates in the ±0.05 m and ±0.1 m acceptance domains, while the Arithmetic mean technique provides the best average gesture acceptance rate in the ±0.15 m acceptance domain when using the APBMR algorithm

StdpC: a modern dynamic clamp

With the advancement of computer technology many novel uses of dynamic clamp have become possible. We have added new features to our dynamic clamp software StdpC (“Spike timing-dependent plasticity Clamp”) allowing such new applications while conserving the ease of use and installation of the popular earlier Dynclamp 2/4 package. Here, we introduce the new features of a waveform generator, freely programmable Hodgkin–Huxley conductances, learning synapses, graphic data displays, and a powerful scripting mechanism and discuss examples of experiments using these features. In the first example we built and ‘voltage clamped’ a conductance based model cell from a passive resistor–capacitor (RC) circuit using the dynamic clamp software to generate the voltage-dependent currents. In the second example we coupled our new spike generator through a burst detection/burst generation mechanism in a phase-dependent way to a neuron in a central pattern generator and dissected the subtle interaction between neurons, which seems to implement an information transfer through intraburst spike patterns. In the third example, making use of the new plasticity mechanism for simulated synapses, we analyzed the effect of spike timing-dependent plasticity (STDP) on synchronization revealing considerable enhancement of the entrainment of a post-synaptic neuron by a periodic spike train. These examples illustrate that with modern dynamic clamp software like StdpC, the dynamic clamp has developed beyond the mere introduction of artificial synapses or ionic conductances into neurons to a universal research tool, which might well become a standard instrument of modern electrophysiology

Homeostatic plasticity and burst activity are mediated by hyperpolarization-activated cation currents and T-type calcium channels in neuronal cultures.

Homeostatic plasticity stabilizes neuronal networks by adjusting the responsiveness of neurons according to their global activity and the intensity of the synaptic inputs. We investigated the homeostatic regulation of hyperpolarization-activated cyclic nucleotide-gated (HCN) and T-type calcium (CaV3) channels in dissociated and organotypic slice cultures. After 48 h blocking of neuronal activity by tetrodotoxin (TTX), our patch-clamp experiments revealed an increase in the depolarizing voltage sag and post-inhibitory rebound mediated by HCN and CaV3 channels, respectively. All HCN subunits (HCN1 to 4) and T-type Ca-channel subunits (CaV3.1, 3.2 and 3.3) were expressed in both control and activity-deprived hippocampal cultures. Elevated expression levels of CaV3.1 mRNA and a selective increase in the expression of TRIP8b exon 4 isoforms, known to regulate HCN channel localization, were also detected in TTX-treated cultured hippocampal neurons. Immunohistochemical staining in TTX-treated organotypic slices verified a more proximal translocation of HCN1 channels in CA1 pyramidal neurons. Computational modeling also implied that HCN and T-type calcium channels have important role in the regulation of synchronized bursting evoked by previous activity-deprivation. Thus, our findings indicate that HCN and T-type Ca-channels contribute to the homeostatic regulation of excitability and integrative properties of hippocampal neurons

Probabilistic modeling of future electricity systems with high renewable energy penetration using machine learning

The increasing penetration of weather-dependent renewable energy generation calls for high-resolution modeling of the possible future energy mixes to support the energy strategy and policy decisions. Simulations relying on the data of only a few years, however, are not only unreliable but also unable to quantify the uncertainty resulting from the year-to-year variability of the weather conditions. This paper presents a new method based on artificial neural networks that map the relationship between the weather data from atmospheric reanalysis and the photovoltaic and wind power generation and the electric load. The regression models are trained based on the data of the last 3 to 6 years, and then they are used to generate synthetic hourly renewable power production and load profiles for 42 years as an ensemble representation of possible outcomes in the future. The modeled profiles are post-processed by a novel variance-correction method that ensures the statistical similarity of the modeled and real data and thus the reliability of the simulation based on these profiles. The probabilistic modeling enabled by the proposed approach is demonstrated in two practical applications for the Hungarian electricity system. First, the so-called Dunkelflaute (dark doldrum) events, are analyzed and categorized. The results reveal that Dunkelflaute events most frequently happen on summer nights, and their typical duration is less than 12 h, even though events ranging through multiple days are also possible. Second, the renewable energy supply is modeled for different photovoltaic and wind turbine installed capacities. Based on our calculations, the share of the annual power consumption that weather-dependent renewable generation can directly cover is up to 60% in Hungary, even with very high installed capacities and overproduction, and higher carbon-free electricity share targets can only be achieved with an energy mix containing nuclear power and renewable sources. The proposed method can easily be extended to other countries and used in more detailed electricity market simulations in the future