Dream contents and failing memories

Mentation during sleep states is thought to originate in an activation of brain circuits that encode inherited and experiential memories. Spontaneous degradation of the strengths of synapses occurs in all brain circuits because of "turnover" of molecules essential for synaptic function. In circuits employed frequently during waking, synaptic strengths are refreshed and maintained in their dedicated or functional ranges largely through use, by virtue of activity-dependent synaptic plasticity. In circuits employed infrequently during waking, synaptic strengths are refreshed largely during sleep, by circuit activations induced by spontaneous, self-generated, largely low-frequency brain waves, also by virtue of activity-dependent synaptic plasticity. The outputs of circuits activated during sleep do not necessarily rise to the level of 'unconscious' awareness. Such an absence of awareness of the outputs of individual circuits, that is, an absence of dreaming, is proposed to be the primitive condition in animals that sleep. On the other hand, temporal binding of these outputs is accompanied by the thoughts and perceptions of dreams, which is proposed to be the advanced condition. Linking or serial ordering of otherwise 'static' thoughts and perceptions gives rise to continuous, often narrative and veridical, dreams. In all cases, dream contents are derived from the memories--not necessarily veridical--encoded in the reinforced circuitry. In the absence of synaptic strength refreshments during sleep, synaptic strengths in infrequently used circuits would weaken and the circuits would become incompetent, with their encoded memories degraded or lost. Maintenance of synaptic strengths in infrequently used circuitry during sleep apparently does not always achieve perfection. Weakened synapses begin to occur in circuits in appreciable numbers in children after the age of about 5 years. When these 'incompetent' circuits (with weakened synapses) are activated during sleep, there are minimal influences on dream contents, namely, distortions that make some objects, such as animals, faces, and scenes, unrecognizable. As weakened synapses increase in numbers with age, the numbers of distorted objects in dreams also increase. In adults, people in as many as 80% of dreams may be unrecognizable. Besides the normal weakening of synaptic strengths, some synapses become defective, in consequence of deleterious, adventitious, exogenous influences, for example, radiation. As these faulty synapses accumulate in old memories, activation of circuits incorporating them during sleep leads to dreams with incoherent, bizarre, or impossible contents. The infrequent activation of such old, incompetent circuits in some waking conditions leads to false memories, delusions, or hallucinations.     

A Dynamical Role for Acetylcholine in Synaptic Renormalization

Although sleep is a fundamental behavior observed in virtually all animal species, its functions remain unclear. One leading proposal, known as the synaptic renormalization hypothesis, suggests that sleep is necessary to counteract a global strengthening of synapses that occurs during wakefulness. Evidence for sleep-dependent synaptic downscaling (or synaptic renormalization) has been observed experimentally, but the physiological mechanisms which generate this phenomenon are unknown. In this study, we propose that changes in neuronal membrane excitability induced by acetylcholine may provide a dynamical mechanism for both wake-dependent synaptic upscaling and sleep-dependent downscaling. We show in silico that cholinergically-induced changes in network firing patterns alter overall network synaptic potentiation when synaptic strengths evolve through spike-timing dependent plasticity mechanisms. Specifically, network synaptic potentiation increases dramatically with high cholinergic concentration and decreases dramatically with low levels of acetylcholine. We demonstrate that this phenomenon is robust across variation of many different network parameters.     

Coupled Flip-Flop Model for REM Sleep Regulation in the Rat

Recent experimental studies investigating the neuronal regulation of rapid eye movement (REM) sleep have identified mutually inhibitory synaptic projections among REM sleep-promoting (REM-on) and REM sleep-inhibiting (REM-off) neuronal populations that act to maintain the REM sleep state and control its onset and offset. The control mechanism of mutually inhibitory synaptic interactions mirrors the proposed flip-flop switch for sleep-wake regulation consisting of mutually inhibitory synaptic projections between wake- and sleep-promoting neuronal populations. While a number of synaptic projections have been identified between these REM-on/REM-off populations and wake/sleep-promoting populations, the specific interactions that govern behavioral state transitions have not been completely determined. Using a minimal mathematical model, we investigated behavioral state transition dynamics dictated by a system of coupled flip-flops, one to control transitions between wake and sleep states, and another to control transitions into and out of REM sleep. The model describes the neurotransmitter-mediated inhibitory interactions between a wake- and sleep-promoting population, and between a REM-on and REM-off population. We proposed interactions between the wake/sleep and REM-on/REM-off flip-flops to replicate the behavioral state statistics and probabilities of behavioral state transitions measured from experimental recordings of rat sleep under ad libitum conditions and after 24 h of REM sleep deprivation. Reliable transitions from REM sleep to wake, as dictated by the data, indicated the necessity of an excitatory projection from the REM-on population to the wake-promoting population. To replicate the increase in REM-wake-REM transitions observed after 24 h REM sleep deprivation required that this excitatory projection promote transient activation of the wake-promoting population. Obtaining the reliable wake-nonREM sleep transitions observed in the data required that activity of the wake-promoting population modulated the interaction between the REM-on and REM-off populations. This analysis suggests neuronal processes to be targeted in further experimental studies of the regulatory mechanisms of REM sleep.     

Impact of intrinsic properties and synaptic factors on the activity of neocortical networks in vivo.

To investigate the relative impact of intrinsic and synaptic factors in the maintenance of the membrane potential of cat neocortical neurons in various states of the network, we performed intracellular recordings in vivo. Experiments were done in the intact cortex and in isolated neocortical slabs of anesthetized animals, and in naturally sleeping and awake cats. There are at least four different electrophysiological cell classes in the neocortex. The responses of different neuronal classes to direct depolarization result in significantly different responses in postsynaptic cells. The activity patterns observed in the intact cortex of anesthetized cats depended mostly on the type of anesthesia. The intracellular activity in small neocortical slabs was composed of silent periods, lasting for tens of seconds, during which only small depolarizing potentials (SDPs, presumed miniature synaptic potentials) were present, and relatively short-lasting (a few hundred milliseconds) active periods. Our data suggest that minis might be amplified by intrinsically-bursting neurons and that the persistent Na+ current brings neurons to firing threshold, thus triggering active periods. The active periods in neurons were composed of the summation of synaptic events and intrinsic depolarizing currents. In chronically-implanted cats, slow-wave sleep was characterized by active (depolarizing) and silent (hyperpolarizing) periods. The silent periods were absent in awake cats. We propose that both intrinsic and synaptic factors are responsible for the transition from silent to active states found in naturally sleeping cats and that synaptic depression might be responsible for the termination of active states during sleep. In view of the unexpected high firing rates of neocortical neurons during the depolarizing epochs in slow-wave sleep, we suggest that cortical neurons are implicated in short-term plasticity processes during this state, in which the brain is disconnected from the outside world, and that memory traces acquired during wakefulness may be consolidated during sleep.     

A thalamo-cortical neural mass model for the simulation of brain rhythms during sleep

Cortico-thalamic interactions are known to play a pivotal role in many brain phenomena, including sleep, attention, memory consolidation and rhythm generation. Hence, simple mathematical models that can simulate the dialogue between the cortex and the thalamus, at a mesoscopic level, have a great cognitive value. In the present work we describe a neural mass model of a cortico-thalamic module, based on neurophysiological mechanisms. The model includes two thalamic populations (a thalamo-cortical relay cell population, TCR, and its related thalamic reticular nucleus, TRN), and a cortical column consisting of four connected populations (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow and fast kinetics). Moreover, thalamic neurons exhibit two firing modes: bursting and tonic. Finally, cortical synapses among pyramidal neurons incorporate a disfacilitation mechanism following prolonged activity. Simulations show that the model is able to mimic the different patterns of rhythmic activity in cortical and thalamic neurons (beta and alpha waves, spindles, delta waves, K-complexes, slow sleep waves) and their progressive changes from wakefulness to deep sleep, by just acting on modulatory inputs. Moreover, simulations performed by providing short sensory inputs to the TCR show that brain rhythms during sleep preserve the cortex from external perturbations, still allowing a high cortical activity necessary to drive synaptic plasticity and memory consolidation. In perspective, the present model may be used within larger cortico-thalamic networks, to gain a deeper understanding of mechanisms beneath synaptic changes during sleep, to investigate the specific role of brain rhythms, and to explore cortical synchronization achieved via thalamic influences.     

Anatomical and functional changes in the upper airways of sleep apnea patients due to mandibular repositioning: a large scale study

The obstructive sleep apnea-hypopnea syndrome (OSAHS) is a sleep related breathing disorder. A popular treatment is the use of a mandibular repositioning appliance (MRA) which advances the mandibula during the sleep and decreases the collapsibility of the upper airway. The success rate of such a device is, however, limited and very variable within a population of patients. Previous studies using computational fluid dynamics have shown that there is a decrease in upper airway resistance in patients who improve clinically due to an MRA. In this article, correlations between patient-specific anatomical and functional parameters are studied to examine how MRA induced biomechanical changes will have an impact on the upper airway resistance. Low-dose computed tomography (CT) scans are made from 143 patients suffering from OSAHS. A baseline scan and a scan after mandibular repositioning (MR) are performed in order to study variations in parameters. It is found that MR using a simulation bite is able to induce resistance changes by changing the pharyngeal lumen. The change in minimal cross-sectional area is the best parameter to predict the change in upper airway resistance. Looking at baseline values, the ideal patients for MR induced resistance decrease seem to be women with short airways, high initial resistance and no baseline occlusion.     

Synaptic Homeostasis and Restructuring across the Sleep-Wake Cycle

Sleep is critical for hippocampus-dependent memory consolidation. However, the underlying mechanisms of synaptic plasticity are poorly understood. The central controversy is on whether long-term potentiation (LTP) takes a role during sleep and which would be its specific effect on memory. To address this question, we used immunohistochemistry to measure phosphorylation of Ca²⁺/calmodulin-dependent protein kinase II (pCaMKIIα) in the rat hippocampus immediately after specific sleep-wake states were interrupted. Control animals not exposed to novel objects during waking (WK) showed stable pCaMKIIα levels across the sleep-wake cycle, but animals exposed to novel objects showed a decrease during subsequent slow-wave sleep (SWS) followed by a rebound during rapid-eye-movement sleep (REM). The levels of pCaMKIIα during REM were proportional to cortical spindles near SWS/REM transitions. Based on these results, we modeled sleep-dependent LTP on a network of fully connected excitatory neurons fed with spikes recorded from the rat hippocampus across WK, SWS and REM. Sleep without LTP orderly rescaled synaptic weights to a narrow range of intermediate values. In contrast, LTP triggered near the SWS/REM transition led to marked swaps in synaptic weight ranking. To better understand the interaction between rescaling and restructuring during sleep, we implemented synaptic homeostasis and embossing in a detailed hippocampal-cortical model with both excitatory and inhibitory neurons. Synaptic homeostasis was implemented by weakening potentiation and strengthening depression, while synaptic embossing was simulated by evoking LTP on selected synapses. We observed that synaptic homeostasis facilitates controlled synaptic restructuring. The results imply a mechanism for a cognitive synergy between SWS and REM, and suggest that LTP at the SWS/REM transition critically influences the effect of sleep: Its lack determines synaptic homeostasis, its presence causes synaptic restructuring.     

A Machine Learning Method for the Prediction of Receptor Activation in the Simulation of Synapses

Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of synapses and it is therefore an excellent tool for multi-scale simulations.     

Characteristics of the hippocampal formation functioning during wakefulness and paradoxical sleep

In view of the available published data concerning various concentration of neuromodulators in the brain during paradoxical sleep and wakefulness and the evidence for the influences of neuromodulators on efficiency of synaptic inputs to hippocampal neurons it is concluded that during paradoxical sleep, increase in concentrations of acetylcholine, cortisol, and dopamine and simultaneous decrease in serotonin and noradrenaline levels could synergistically lead to essential depression of efficacy of synaptic transmission in the polysynaptic pathway through the hippocampus (i.e. in the perforant path to dentate gyrus, from the dentate gyrus to CA3 area, from CA3 to CA1 area and from CA1 to the subiculum) but potentiation of the efficacy of the perforant input to pyramids of CA1 and CA3 areas and increase in efficacy of associative connections between CA3 neurones. The specified changes in functioning of the hippocampal loop can underlie differences in storing and extraction of information from memory during paradoxical sleep as compared to wakefulness.     

Going Beyond a Mean-field Model for the Learning Cortex: Second-Order Statistics

Mean-field models of the cortex have been used successfully to interpret the origin of features on the electroencephalogram under situations such as sleep, anesthesia, and seizures. In a mean-field scheme, dynamic changes in synaptic weights can be considered through fluctuation-based Hebbian learning rules. However, because such implementations deal with population-averaged properties, they are not well suited to memory and learning applications where individual synaptic weights can be important. We demonstrate that, through an extended system of equations, the mean-field models can be developed further to look at higher-order statistics, in particular, the distribution of synaptic weights within a cortical column. This allows us to make some general conclusions on memory through a mean-field scheme. Specifically, we expect large changes in the standard deviation of the distribution of synaptic weights when fluctuation in the mean soma potentials are large, such as during the transitions between the “up” and “down” states of slow-wave sleep. Moreover, a cortex that has low structure in its neuronal connections is most likely to decrease its standard deviation in the weights of excitatory to excitatory synapses, relative to the square of the mean, whereas a cortex with strongly patterned connections is most likely to increase this measure. This suggests that fluctuations are used to condense the coding of strong (presumably useful) memories into fewer, but dynamic, neuron connections, while at the same time removing weaker (less useful) memories.     

New predictors of sleep efficiency

Sleep efficiency is a commonly and widely used measure to objectively evaluate sleep quality. Monitoring sleep efficiency can provide significant information about health conditions. As an attempt to facilitate less cumbersome monitoring of sleep efficiency, our study aimed to suggest new predictors of sleep efficiency that enable reliable and unconstrained estimation of sleep efficiency during awake resting period. We hypothesized that the autonomic nervous system activity observed before falling asleep might be associated with sleep efficiency. To assess autonomic activity, heart rate variability and breathing parameters were analyzed for 5 min. Using the extracted parameters as explanatory variables, stepwise multiple linear regression analyses and k-fold cross-validation tests were performed with 240 electrocardiographic and thoracic volume change signal recordings to develop the sleep efficiency prediction model. The developed model’s sleep efficiency predictability was evaluated using 60 piezoelectric sensor signal recordings. The regression model, established using the ratio of the power of the low- and high-frequency bands of the heart rate variability signal and the average peak inspiratory flow value, provided an absolute error (mean ± SD) of 2.18% ± 1.61% and a Pearson’s correlation coefficient of 0.94 (p < 0.01) between the sleep efficiency predictive values and the reference values. Our study is the first to achieve reliable and unconstrained prediction of sleep efficiency without overnight recording. This method has the potential to be utilized for home-based, long-term monitoring of sleep efficiency and to support reasonable decision-making regarding the execution of sleep efficiency improvement strategies.     

Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise

During slow-wave sleep, brain electrical activity is dominated by the slow (< 1 Hz) electroencephalogram (EEG) oscillations characterized by the periodic transitions between active (or Up) and silent (or Down) states in the membrane voltage of the cortical and thalamic neurons. Sleep slow oscillation is believed to play critical role in consolidation of recent memories. Past computational studies, based on the Hodgkin-Huxley type neuronal models, revealed possible intracellular and network mechanisms of the neuronal activity during sleep, however, they failed to explore the large-scale cortical network dynamics depending on collective behavior in the large populations of neurons. In this new study, we developed a novel class of reduced discrete time spiking neuron models for large-scale network simulations of wake and sleep dynamics. In addition to the spiking mechanism, the new model implemented nonlinearities capturing effects of the leak current, the Ca²⁺ dependent K⁺ current and the persistent Na⁺ current that were found to be critical for transitions between Up and Down states of the slow oscillation. We applied the new model to study large-scale two-dimensional cortical network activity during slow-wave sleep. Our study explained traveling wave dynamics and characteristic synchronization properties of transitions between Up and Down states of the slow oscillation as observed in vivo in recordings from cats. We further predict a critical role of synaptic noise and slow adaptive currents for spike sequence replay as found during sleep related memory consolidation.     

Synaptic Efficacy as a Function of Ionotropic Receptor Distribution: A Computational Study

Glutamatergic synapses are the most prevalent functional elements of information processing in the brain. Changes in pre-synaptic activity and in the function of various post-synaptic elements contribute to generate a large variety of synaptic responses. Previous studies have explored postsynaptic factors responsible for regulating synaptic strength variations, but have given far less importance to synaptic geometry, and more specifically to the subcellular distribution of ionotropic receptors. We analyzed the functional effects resulting from changing the subsynaptic localization of ionotropic receptors by using a hippocampal synaptic computational framework. The present study was performed using the EONS (Elementary Objects of the Nervous System) synaptic modeling platform, which was specifically developed to explore the roles of subsynaptic elements as well as their interactions, and that of synaptic geometry. More specifically, we determined the effects of changing the localization of ionotropic receptors relative to the presynaptic glutamate release site, on synaptic efficacy and its variations following single pulse and paired-pulse stimulation protocols. The results indicate that changes in synaptic geometry do have consequences on synaptic efficacy and its dynamics.     

Neural reapportionment: an hypothesis to account for the function of sleep

Sleep is a ubiquitous component of animal life, and prolonged sleep deprivation is fatal in both vertebrates and invertebrates. The physiologic function of sleep, however, is not known. We propose here that sleep provides a period of time necessary to reapportion resources within neurons and neural systems that become sub-optimally distributed during active waking. Three specific examples of such reapportionment during sleep are suggested: (1) the return of the neurotransmitter, glutamate, to synaptic vesicles at presynaptic sites most active during waking, (2) the intracellular movement of mitochondria from neuronal processes to the cells soma where mitochondrial replication can occur, and (3) the readjustment of the level and distribution of neurotransmitters within the brainstem modulatory systems and elsewhere that must function in an integrated fashion during waking. Experimental approaches that might be utilized to test these hypotheses are suggested.     

Morning Anaerobic Performance Is Not Altered by Vigilance Impairment

The aim of this study was to determine the role played by vigilance on the anaerobic performance recorded during a Wingate test performed at the bathyphase (nadir) of the circadian rhythmicity. Twenty active male participants performed a 60-s Wingate test at 6 a.m. during 3 test sessions in counter-balanced order the day after either (i) a normal reference night, (ii) a total sleep deprivation night, or (iii) a total sleep deprivation night associated with an extended simulated driving task from 9 p.m. to 5 a.m. During this task, the number of inappropriate line crossings (ILCs) was used to control and quantify the effective decrease in the level of vigilance. The main findings show that (i) vigilance of each participant was significantly altered (i.e., a drastic and progressive increase in ILCs is shown during the 7.5 hours of driving) by the sleep deprivation night associated with an extended driving task; (ii) the subjective evaluation of vigilance performed by self-rated scale revealed an increased impairment of the vigilance level between the normal reference night, the total sleep deprivation night and the total sleep deprivation night associated with an extended driving task; and (iii) the morning following this last condition, during the Wingate test, the recorded cycling biomechanical parameters (peak power, mean power and fatigue index values, power decrease, and cycling kinetic and kinematic patterns) were not significantly different from the two other conditions. Consequently, these results show that anaerobic performances recorded during a Wingate test performed at the bathyphase of the circadian rhythmicity are not altered by a drastic impairment in vigilance. These findings seem to indicate that vigilance is probably not a factor that contributes to circadian variations in anaerobic performance.     

The Influence of Synaptic Size on AMPA Receptor Activation: A Monte Carlo Model

Physiological and electron microscope studies have shown that synapses are functionally and morphologically heterogeneous and that variations in size of synaptic junctions are related to characteristics such as release probability and density of postsynaptic AMPA receptors. The present article focuses on how these morphological variations impact synaptic transmission. We based our study on Monte Carlo computational simulations of simplified model synapses whose morphological features have been extracted from hundreds of actual synaptic junctions reconstructed by three-dimensional electron microscopy. We have examined the effects that parameters such as synaptic size or density of AMPA receptors have on the number of receptors that open after release of a single synaptic vesicle. Our results indicate that the maximum number of receptors that will open after the release of a single synaptic vesicle may show a ten-fold variation in the whole population of synapses. When individual synapses are considered, there is also a stochastical variability that is maximal in small synapses with low numbers of receptors. The number of postsynaptic receptors and the size of the synaptic junction are the most influential parameters, while the packing density of receptors or the concentration of extrasynaptic transporters have little or no influence on the opening of AMPA receptors.     

A detailed anatomical and mathematical model of the hippocampal formation for the generation of sharp-wave ripples and theta-nested gamma oscillations

The mechanisms underlying the broad variety of oscillatory rhythms measured in the hippocampus during the sleep-wake cycle are not yet fully understood. In this article, we propose a computational model of the hippocampal formation based on a realistic topology and synaptic connectivity, and we analyze the effect of different changes on the network, namely the variation of synaptic conductances, the variations of the CAN channel conductance and the variation of inputs. By using a detailed simulation of intracerebral recordings, we show that this is able to reproduce both the theta-nested gamma oscillations that are seen in awake brains and the sharp-wave ripple complexes measured during slow-wave sleep. The results of our simulations support the idea that the functional connectivity of the hippocampus, modulated by the sleep-wake variations in Acetylcholine concentration, is a key factor in controlling its rhythms.     

Formation and Dynamics of Waves in a Cortical Model of Cholinergic Modulation

Acetylcholine (ACh) is a regulator of neural excitability and one of the neurochemical substrates of sleep. Amongst the cellular effects induced by cholinergic modulation are a reduction in spike-frequency adaptation (SFA) and a shift in the phase response curve (PRC). We demonstrate in a biophysical model how changes in neural excitability and network structure interact to create three distinct functional regimes: localized asynchronous, traveling asynchronous, and traveling synchronous. Our results qualitatively match those observed experimentally. Cortical activity during slow wave sleep (SWS) differs from that during REM sleep or waking states. During SWS there are traveling patterns of activity in the cortex; in other states stationary patterns occur. Our model is a network composed of Hodgkin-Huxley type neurons with a M-current regulated by ACh. Regulation of ACh level can account for dynamical changes between functional regimes. Reduction of the magnitude of this current recreates the reduction in SFA the shift from a type 2 to a type 1 PRC observed in the presence of ACh. When SFA is minimal (in waking or REM sleep state, high ACh) patterns of activity are localized and easily pinned by network inhomogeneities. When SFA is present (decreasing ACh), traveling waves of activity naturally arise. A further decrease in ACh leads to a high degree of synchrony within traveling waves. We also show that the level of ACh determines how sensitive network activity is to synaptic heterogeneity. These regimes may have a profound functional significance as stationary patterns may play a role in the proper encoding of external input as memory and traveling waves could lead to synaptic regularization, giving unique insights into the role and significance of ACh in determining patterns of cortical activity and functional differences arising from the patterns.     

Shaping Neuronal Network Activity by Presynaptic Mechanisms

Neuronal microcircuits generate oscillatory activity, which has been linked to basic functions such as sleep, learning and sensorimotor gating. Although synaptic release processes are well known for their ability to shape the interaction between neurons in microcircuits, most computational models do not simulate the synaptic transmission process directly and hence cannot explain how changes in synaptic parameters alter neuronal network activity. In this paper, we present a novel neuronal network model that incorporates presynaptic release mechanisms, such as vesicle pool dynamics and calcium-dependent release probability, to model the spontaneous activity of neuronal networks. The model, which is based on modified leaky integrate-and-fire neurons, generates spontaneous network activity patterns, which are similar to experimental data and robust under changes in the model''s primary gain parameters such as excitatory postsynaptic potential and connectivity ratio. Furthermore, it reliably recreates experimental findings and provides mechanistic explanations for data obtained from microelectrode array recordings, such as network burst termination and the effects of pharmacological and genetic manipulations. The model demonstrates how elevated asynchronous release, but not spontaneous release, synchronizes neuronal network activity and reveals that asynchronous release enhances utilization of the recycling vesicle pool to induce the network effect. The model further predicts a positive correlation between vesicle priming at the single-neuron level and burst frequency at the network level; this prediction is supported by experimental findings. Thus, the model is utilized to reveal how synaptic release processes at the neuronal level govern activity patterns and synchronization at the network level.     

Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity

In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational studies of sensory processing in neocortical network models equipped with synaptic plasticity.