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Evidence indicates that sharp-wave ripples (SWRs) are primary network events supporting memory processes. However, some studies demonstrate that even after disruption of awake SWRs the animal can still learn spatial task or that SWRs may be not necessary to establish a cognitive map of the environment. Moreover, we have found recently that despite a deficit of sleep SWRs the APP/PS1 mice, a model of Alzheimer's disease, show undisturbed spatial reference memory. Searching for a learning-related alteration of SWRs that could account for the efficiency of memory in these mice we use convolutional neural networks (CNN) to discriminate pre- and post-learning 256 ms samples of LFP signals, containing individual SWRs. We found that the fraction of samples that were correctly recognized by CNN in majority of discrimination sessions was equal to ~ 50% in the wild-type (WT) and only 14% in APP/PS1 mice. Moreover, removing signals generated in a close vicinity of SWRs significantly diminished the number of such highly recognizable samples in the WT but not in APP/PS1 group. These results indicate that in WT animals a large subset of SWRs and signals generated in their proximity may contain learning-related information whereas such information seem to be limited in the AD mice. [ABSTRACT FROM AUTHOR]

Hippocampal-cortical dialogue, during which hippocampal ripple oscillations support information transfer, is necessary for long-term consolidation of spatial memories. Whereas a vast amount of work has been carried out to understand the cellular and molecular mechanisms involved in the impairments of memory formation in Alzheimer's disease (AD), far less work has been accomplished to understand these memory deficiencies at the network-level interaction that may underlie memory processing. We recently demonstrated that freely moving 8 to 9-month-old APP/PS1 mice, a model of AD, are able to learn a spatial reference memory task despite a major deficit in Sharp-Wave Ripples (SWRs), the integrity of which is considered to be crucial for spatial memory formation. In order to test whether reconfiguration of hippocampal-cortical dialogue could be responsible for the maintenance of this ability for memory formation, we undertook a study to identify causal relations between hippocampal and cortical circuits in epochs when SWRs are generated in hippocampus. We analyzed the data set obtained from multielectrode intracranial recording of transgenic and wild-type mice undergoing consolidation of spatial memory reported in our previous study. We applied Directed Transfer Function, a connectivity measure based on Granger causality, in order to determine effective coupling between distributed circuits which express oscillatory activity in multiple frequency bands. Our results showed that hippocampal-cortical coupling in epochs containing SWRs was expressed in the two frequency ranges corresponding to ripple (130–180 Hz) and slow gamma (20–60 Hz) band. The general features of connectivity patterns were similar in the 8 to 9-month-old APP/PS1 and wild-type animals except that the coupling in the slow gamma range was stronger and spread to more cortical sites in APP/PS1 mice than in the wild-type group. During the occurrence of SWRs, the strength of effective coupling from the cortex to hippocampus (CA1) in the ripple band undergoes sharp increase, involving cortical areas that were different in the two groups of animals. In the wild-type group, retrosplenial cortex and posterior cingulate cortex interacted with the hippocampus most strongly, whereas in the APP/PS1 group more anterior structures interacted with the hippocampus, that is, anterior cingulate cortex and prefrontal cortex. This reconfiguration of cortical-hippocampal interaction pattern may be an adaptive mechanism responsible for supporting spatial memory consolidation in AD mice model. [ABSTRACT FROM AUTHOR]

General theory of declarative memory formation posits a cortical-hippocampal dialog during which hippocampal ripple oscillations support information transfer and long-term consolidation of hippocampus dependent memories. Brain dementia, as Alzheimer disease (AD), is accompanied by memory loss and inability to form new memories. A large body of work has shown variety of mechanisms acting at cellular and molecular levels which can putatively play an important role in the impairment of memory formation. However, far less is known about changes occurring at the network-level activity patterns that support memory processing. Using freely moving APP/PS1 mice, a model of AD, we undertook a study to unravel the alterations of the activity of hippocampal and cortical circuits during generation of ripples in the transgenic and wild-type mice undergoing encoding and consolidation of spatial information. We report that APP/PS1 animals are able to consolidate spatial memory despite a major deficit of hippocampal ripples occurrence rate and learning dependent dynamics. We propose that these impairments may be compensated by an increase of the occurrence of cortical ripples and reorganization of cortical-hippocampal interaction. [ABSTRACT FROM AUTHOR]

Altruism and inequity aversion are often conceptually interrelated, which implies that altruistic and selfish humans may respond differently to disadvantageous inequity conditions. However, a correlation between altruism and inequity responses has thus far not been directly tested experimentally. We have addressed this question using an experimental paradigm inspired by animal experiments in which adult humans work for real food rewards. We have studied whether subjects' responses to different reward distributions were altered by being exposed to equitable or non-equitable situations. In the control conditions, subjects expressed either a strong altruistic attitude, choosing to work for their partner's welfare in the majority of trials, or mostly rejected this course of action. These purely altruistic and selfish behaviors were also expressed after being exposed to disadvantageous inequity, but priming with equitable conditions significantly reduced their occurrence. This implies an important role of inequity pressure, which is presumably present in modern society, in shaping human-helping attitudes. [ABSTRACT FROM AUTHOR]

We studied the dynamics of a large-scale model network comprised of oscillating electrically coupled neurons. Cells are modeled as relaxation oscillators with short duty cycle, so they can be considered either as models of pacemaker cells, spiking cells with fast regenerative and slow recovery variables or firing rate models of excitatory cells with synaptic depression or cellular adaptation. It was already shown that electrically coupled relaxation oscillators exhibit not only synchrony but also anti-phase behavior if electrical coupling is weak. We show that a much wider spectrum of spatiotemporal patterns of activity can emerge in a network of electrically coupled cells as a result of switching from synchrony, produced by short external signals of different spatial profiles. The variety of patterns increases with decreasing rate of neuronal firing (or duty cycle) and with decreasing strength of electrical coupling. We study also the effect of network topology - from all-to-all – to pure ring connectivity, where only the closest neighbors are coupled. We show that the ring topology promotes anti-phase behavior as compared to all-to-all coupling. It also gives rise to a hierarchical organization of activity: during each of the main phases of a given pattern cells fire in a particular sequence determined by the local connectivity. We have analyzed the behavior of the network using geometric phase plane methods and we give heuristic explanations of our findings. Our results show that complex spatiotemporal activity patterns can emerge due to the action of stochastic or sensory stimuli in neural networks without chemical synapses, where each cell is equally coupled to others via gap junctions. This suggests that in developing nervous systems where only electrical coupling is present such a mechanism can lead to the establishment of proto-networks generating premature multiphase oscillations whereas the subsequent emergence of chemical synapses would later stabilize generated patterns. [ABSTRACT FROM AUTHOR]

A model or hybrid network consisting of oscillatory cells interconnected by inhibitory and electrical synapses may express different stable activity patterns without any change of network topology or parameters, and switching between the patterns can be induced by specific transient signals. However, little is known of properties of such signals. In the present study, we employ numerical simulations of neural networks of different size composed of relaxation oscillators, to investigate switching between in-phase (IP) and anti-phase (AP) activity patterns. We show that the time windows of susceptibility to switching between the patterns are similar in 2-, 4- and 6-cell fully-connected networks. Moreover, in a network (N = 4, 6) expressing a given AP pattern, a stimulus with a given profile consisting of depolarizing and hyperpolarizing signals sent to different subpopulations of cells can evoke switching to another AP pattern. Interestingly, the resulting pattern encodes the profile of the switching stimulus. These results can be extended to different network architectures. Indeed, relaxation oscillators are not only models of cellular pacemakers, bursting or spiking, but are also analogous to firing-rate models of neural activity. We show that rules of switching similar to those found for relaxation oscillators apply to oscillating circuits of excitatory cells interconnected by electrical synapses and cross-inhibition. Our results suggest that incoming information, arriving in a proper time window, may be stored in an oscillatory network in the form of a specific spatio-temporal activity pattern which is expressed until new pertinent information arrives. [ABSTRACT FROM AUTHOR]

Mutually inhibitory pacemaker neurons with duty cycle close to 50% operate as a half-center oscillator (anti-phase coordination, i.e., 180 degrees out of phase), even in the presence of weak to modest gap junctional coupling. For electrical coupling strength above a critical value synchronization occurs. But, as shown here with modeling studies, the effects of electrical coupling depend critically on a cell's duty cycle. Instead of oscillating either in-phase or anti-phase, model cells with short duty cycle express additional rhythmic patterns, and different transitions between them, depending on electrical coupling strength. For weak or no electrical coupling, cells do not oscillate in anti-phase but instead exhibit almost in-phase activity. Strengthening this weak coupling leads to stable anti-phase activity. With yet stronger electrical coupling stable inphase (synchrony) emerges but it coexists with the anti-phase pattern. Thus the network shows bistability for an intermediate range of coupling strength. For sufficiently strong electrical coupling synchrony is the network's only attracting rhythmic state. Our results, numerical and analytical (phase plane analysis), are based on a minimal but biophysically motivated pacemaker model for the slowly oscillating envelope of bursting neurons. However, illustrations for an Hodgkin-Huxley model suggest that some of our results for short duty cycle may extend to patterning of repetitive spikes. In particular, electrical coupling of intermediate strength may promote anti-phase activity and provide bistability of anti-phase and in-phase spiking. [ABSTRACT FROM AUTHOR]

Modulatory systems are well known for their roles in tuning the cellular and synaptic properties in the adult neuronal networks, and play a major role in the control of the flexibility of functional outputs. However far less is known concerning their role in the maturation of neural networks during the development. In this review, using the stomatogastric nervous system of lobster, we will show that the neuromodulatory system exerts a powerful influence on developing neural networks. In the adult the number of both motor target neurons and their modulatory neurons is restricted to tens of identifiable cells. They are therefore well characterized in terms of cellular, synaptic and morphological properties. In the embryo, these target cells and their neuromodulatory population are already present from mid-embryonic life. However, the motor output generated by the system is quite different: while in the embryo all the target neurons are organized into a single network generating unique motor pattern, in the adult this population splits into two distinct networks generating separate patterns. This ontogenetic partitioning does not rely on progressive acquisition of adult properties but rather on a switch between two possible network operations. Indeed, adult networks are present early in the embryonic life but their expression is repressed by central modulatory neurons. Moreover, embryonic networks can be revealed in the adult system again by altering modulatory influences. Therefore, independently of the developmental age, two potential network phenotypes co-exist within the same neuronal architecture: when one is expressed, the other one is hidden and vice versa. These transitions do not necessarily need dramatic changes such as growth/retraction of processes, acquisition of new intra-membrane proteins etc. but rather, as shown by modelling studies, it may simply rely on a subtle tuning of pre-existing intercellular electrical coupling. This in turn suggests that progressive ontogenetic alteration may not take place at the level of the target network but rather at the level of modulatory input neurons. [Copyright &y& Elsevier]