Effect of learning on slow gamma propagation between hippocampus and cortex in the wild-type and AD mice.

Slow gamma oscillations (20-50 Hz) have been suggested to coordinate information transfer between brain structures involved in memory formation. Whereas the involvement of slow gamma in memory processing was studied by means of correlation between the gamma power and the occurrence of a given event (sharp wave ripples (SWRs), cortical transients), our approach consists of the analysis of the transmission of slow gamma itself. We use the method based on Granger causality principle-direct Directed Transfer Function, which allows to determine directed propagation of brain activity, including bidirectional flows. Four cortical sites along with CA1 ipsi- and contralateral were recorded in behaving wild-type and APP/PS1 mice before and after learning session of a spatial memory task. During slow wave sleep propagation of slow gamma was bidirectional, forming multiple loops of interaction which involved both CA1 and some of cortical sites. In episodes coincident with SWRs the number and strength of connectivity pathways increased in both groups compared to episodes without SWRs. The effect of learning was expressed only in APP/PS1 mice and consisted in strengthening of the slow gamma transmission from hippocampus to cortex as well as between both CA1 which may serve more efficient transmission of information from impaired CA1.

Recognition of post-learning alteration of hippocampal ripples by convolutional neural network differs in the wild-type and AD mice.

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.

Reconfiguration of the cortical-hippocampal interaction may compensate for Sharp-Wave Ripple deficits in APP/PS1 mice and support spatial memory formation.

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.

Deficit in hippocampal ripples does not preclude spatial memory formation in APP/PS1 mice.

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.

Soluble amyloid beta oligomers block the learning-induced increase in hippocampal sharp wave-ripple rate and impair spatial memory formation.

Post-learning hippocampal sharp wave-ripples (SWRs) generated during slow wave sleep are thought to play a crucial role in memory formation. While in Alzheimer's disease, abnormal hippocampal oscillations have been reported, the functional contribution of SWRs to the typically observed spatial memory impairments remains unclear. These impairments have been related to degenerative synaptic changes produced by soluble amyloid beta oligomers (Aßos) which, surprisingly, seem to spare the SWR dynamics during routine behavior. To unravel a potential effect of Aßos on SWRs in cognitively-challenged animals, we submitted vehicle- and Aßo-injected mice to spatial recognition memory testing. While capable of forming short-term recognition memory, Aß mice exhibited faster forgetting, suggesting successful encoding but an inability to adequately stabilize and/or retrieve previously acquired information. Without prior cognitive requirements, similar properties of SWRs were observed in both groups. In contrast, when cognitively challenged, the post-encoding and -recognition peaks in SWR occurrence observed in controls were abolished in Aß mice, indicating impaired hippocampal processing of spatial information. These results point to a crucial involvement of SWRs in spatial memory formation and identify the Aß-induced impairment in SWRs dynamics as a disruptive mechanism responsible for the spatial memory deficits associated with Alzheimer's disease.

Variety of alternative stable phase-locking in networks of electrically coupled relaxation oscillators.

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.

Automatic detection and analysis of the EEG sharp wave-slow wave patterns evoked by fluorinated inhalation anesthetics.

OBJECTIVE: The aim of this study was to develop a method for the automatic detection of sharp wave-slow wave (SWSW) patterns evoked in EEG by volatile anesthetics and to identify the patterns' characteristics. METHODS: The proposed method consisted in the k-NN classification with a reference set obtained using expert knowledge, the morphology of the EEG patterns and the condition for their synchronization. The decision rules were constructed and evaluated using 24h EEG records in ten patients. RESULTS: The sensitivity, specificity and selectivity of the method were 0.88 ± 0.10, 0.81 ± 0.13 and 0.42 ± 0.16, respectively. SWSW patterns' recruitment was strictly dependent on anesthetic concentration. SWSW patterns evoked by different types of anesthetics expressed different characteristics. CONCLUSIONS: Synchronization criterion and adequately selected morphological features of "slow wave" were sufficient to achieve the high sensitivity and specificity of the method. SIGNIFICANCE: The monitoring of SWSW patterns is important in view of possible side effects of volatile anesthetics. The analysis of SWSW patterns' recruitment and morphology could be helpful in the diagnosis of the anesthesia effects on the CNS.

Inhibitory network of spiking neurons may express a sharp peak of synchrony at low frequency band.

Spike synchronization remains an important issue in neuroscience, and inhibitory networks are the best candidates to provide such synchrony. Increasing evidence indicates that in many brain area inhibitory interneurons of similar properties make reciprocal connections. We found that a hybrid, as well as model network, consisting of two reciprocally inhibitory spiking neurons may express a peak of synchronization in a narrow range of low spiking frequencies in addition to classically described plateau of synchrony at a wide range of high frequencies. Occurrence of the low frequency peak of synchrony requires a moderate-to-strong inhibitory coupling and relatively fast synapses. This novel possibility of synchronization in a narrow range of network parameters may have an important implication in discrimination and encoding of signals of precise intensity, as well as in altering network ability to process information.

Multi-stability and pattern-selection in oscillatory networks with fast inhibition and electrical synapses.

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.

Electrical coupling induces bistability of rhythms in networks of inhibitory spiking neurons.

Information processing in higher brain structures is thought to rely on the synchronization of spiking neurons. Increasing evidence indicates that, within these structures, inhibitory neurons are linked by both chemical and electrical synapses. However, how synchronized states may emerge from such circuits is not fully understood. Using snail neurons interconnected through a dynamic-clamp system, we show that networks of spiking neurons linked by both reciprocal inhibition and electrical coupling can express two coexisting coordination patterns of different rhythms. One of these patterns consists of antiphase firing of the network partners whereas, in the other, neurons fire synchronously. Switching between patterns may be evoked immediately by transient stimuli, demonstrating bistability of the network. Thus electrical coupling can provide a potent way for instantaneous reconfiguration of activity patterns in inhibitory spiking networks without alteration of intrinsic network properties by modulatory processes.

Short duty cycle destabilizes a half-center oscillator, but gap junctions can restabilize the anti-phase pattern.

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.

From swimming to walking: a single basic network for two different behaviors.

In this paper we consider the hypothesis that the spinal locomotor network controlling trunk movements has remained essentially unchanged during the evolutionary transition from aquatic to terrestrial locomotion. The wider repertoire of axial motor patterns expressed by amphibians would then be explained by the influence from separate limb pattern generators, added during this evolution. This study is based on EMG data recorded in vivo from epaxial musculature in the newt Pleurodeles waltl during unrestrained swimming and walking, and on a simplified model of the lamprey spinal pattern generator for swimming. Using computer simulations, we have examined the output generated by the lamprey model network for different input drives. Two distinct inputs were identified which reproduced the main features of the swimming and walking motor patterns in the newt. The swimming pattern is generated when the network receives tonic excitation with local intensity gradients near the neck and girdle regions. To produce the walking pattern, the network must receive (in addition to a tonic excitation at the girdles) a phasic drive which is out of phase in the neck and tail regions in relation to the middle part of the body. To fit the symmetry of the walking pattern, however, the intersegmental connectivity of the network had to be modified by reversing the direction of the crossed inhibitory pathways in the rostral part of the spinal cord. This study suggests that the input drive required for the generation of the distinct walking pattern could, at least partly, be attributed to mechanosensory feedback received by the network directly from the intraspinal stretch-receptor system. Indeed, the input drive required resembles the pattern of activity of stretch receptors sensing the lateral bending of the trunk, as expressed during walking in urodeles. Moreover, our results indicate that a nonuniform distribution of these stretch receptors along the trunk can explain the discontinuities exhibited in the swimming pattern of the newt. Thus, separate limb pattern generators can influence the original network controlling axial movements not only through a direct coupling at the central level but also via a mechanical coupling between trunk and limbs, which in turn influences the sensory signals sent back to the network. Taken together, our findings support the hypothesis of a phylogenetic conservatism of the spinal locomotor networks generating axial motor patterns from agnathans to amphibians.

Maturation of rhythmic neural network: role of central modulatory inputs.

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.

Electrical coupling can prevent expression of adult-like properties in an embryonic neural circuit.

Electrical coupling is widespread in developing nervous systems and plays a major role in circuit formation and patterning of activity. In most reported cases, such coupling between rhythmogenic neurons tends to synchronize and enhance their oscillatory behavior, thereby producing monophasic rhythmic output. However, in many adult networks, such as those responsible for rhythmic motor behavior, oscillatory neurons are linked by synaptic inhibition to produce rhythmic output with multiple phases. The question then arises whether such networks are still able to generate multiphasic output in the early stage of development when electrical coupling is abundant. A suitable model for addressing this issue is the lobster stomatogastric nervous system (STNS). In the adult animal, the STNS consists of three discrete neural networks that are comprised of oscillatory neurons interconnected by reciprocal inhibition. These networks generate three distinct rhythmic motor patterns with large amplitude neuronal oscillations. By contrast, in the embryo the same neuronal population expresses a single multiphasic rhythm with small-amplitude oscillations. Recent findings have revealed that adult-like network properties are already present early in the embryonic system but are masked by an as yet unknown mechanism. Here we use computer simulation to test whether extensive electrical coupling may be involved in masking adult-like properties in the embryonic STNS. Our basic model consists of three different adult-like STNS networks that are built of relaxation oscillators interconnected by reciprocal synaptic inhibition. Individual model cells generate slow membrane potential oscillations without action potentials. The introduction of widespread electrical coupling between members of these networks dampens oscillation amplitudes and, at moderate coupling strengths, may coordinate neuronal activity into a single rhythm with different phases, which is strongly reminiscent of embryonic STNS output. With a further increase in coupling strength, the system reaches one of two final states depending on the relative contribution of inhibition and inherent oscillatory properties within the networks: either fully synchronized and dampened oscillations, or a complete collapse of activity. Our simulations indicate that, beginning from either of these two states, the emergence of distinct adult networks during maturation may arise from a developmental decrease in electrical coupling that unmasks preexisting adult-like network properties.