The emergence of Up and Down states in cortical networks

The cerebral cortex is continuously active in the absence of external stimuli. An example of this spontaneous activity is the voltage transition between an Up and a Down state, observed simultaneously at individual neurons. Since this phenomenon could be of critical importance for working memory and attention, its explanation could reveal some fundamental properties of cortical organization. To identify a possible scenario for the dynamics of Up–Down states, we analyze a reduced stochastic dynamical system that models an interconnected network of excitatory neurons with activity-dependent synaptic depression. The model reveals that when the total synaptic connection strength exceeds a certain threshold, the phase space of the dynamical system contains two attractors, interpreted as Up and Down states. In that case, synaptic noise causes transitions between the states. Moreover, an external stimulation producing a depolarization increases the time spent in the Up state, as observed experimentally. We therefore propose that the existence of Up–Down states is a fundamental and inherent property of a noisy neural ensemble with sufficiently strong synaptic connections.     

Exploring the spectrum of dynamical regimes and timescales in spontaneous cortical activity

Rhythms at slow (<1 Hz) frequency of alternating Up and Down states occur during slow-wave sleep states, under deep anaesthesia and in cortical slices of mammals maintained in vitro. Such spontaneous oscillations result from the interplay between network reverberations nonlinearly sustained by a strong synaptic coupling and a fatigue mechanism inhibiting the neurons firing in an activity-dependent manner. Varying pharmacologically the excitability level of brain slices we exploit the network dynamics underlying slow rhythms, uncovering an intrinsic anticorrelation between Up and Down state durations. Besides, a non-monotonic change of Down state duration is also observed, which shrinks the distribution of the accessible frequencies of the slow rhythms. Attractor dynamics with activity-dependent self-inhibition predicts a similar trend even when the system excitability is reduced, because of a stability loss of Up and Down states. Hence, such cortical rhythms tend to display a maximal size of the distribution of Up/Down frequencies, envisaging the location of the system dynamics on a critical boundary of the parameter space. This would be an optimal solution for the system in order to display a wide spectrum of dynamical regimes and timescales.     

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.     

Active hippocampal networks undergo spontaneous synaptic modification

The brain is self-writable; as the brain voluntarily adapts itself to a changing environment, the neural circuitry rearranges its functional connectivity by referring to its own activity. How the internal activity modifies synaptic weights is largely unknown, however. Here we report that spontaneous activity causes complex reorganization of synaptic connectivity without any external (or artificial) stimuli. Under physiologically relevant ionic conditions, CA3 pyramidal cells in hippocampal slices displayed spontaneous spikes with bistable slow oscillations of membrane potential, alternating between the so-called UP and DOWN states. The generation of slow oscillations did not require fast synaptic transmission, but their patterns were coordinated by local circuit activity. In the course of generating spontaneous activity, individual neurons acquired bidirectional long-lasting synaptic modification. The spontaneous synaptic plasticity depended on a rise in intracellular calcium concentrations of postsynaptic cells, but not on NMDA receptor activity. The direction and amount of the plasticity varied depending on slow oscillation patterns and synapse locations, and thus, they were diverse in a network. Once this global synaptic refinement occurred, the same neurons now displayed different patterns of spontaneous activity, which in turn exhibited different levels of synaptic plasticity. Thus, active networks continuously update their internal states through ongoing synaptic plasticity. With computational simulations, we suggest that with this slow oscillation-induced plasticity, a recurrent network converges on a more specific state, compared to that with spike timing-dependent plasticity alone.     

Sex differences in endogenous cortical network activity: spontaneously recurring Up/Down states

Background

Several molecular and cellular processes in the vertebrate brain exhibit differences between males and females, leading to sexual dimorphism in the formation of neural circuits and brain organization. While studies on large-scale brain networks provide ample evidence for both structural and functional sex differences, smaller-scale local networks have remained largely unexplored. In the current study, we investigate sexual dimorphism in cortical dynamics by means of spontaneous Up/Down states, a type of network activity that is exhibited during slow-wave sleep, quiet wakefulness, and anesthesia and is thought to represent the default activity of the cortex.

Methods

Up state activity was monitored by local field potential recordings in coronal brain slices of male and female mice across three ages with distinct secretion profiles of sex hormones: (i) pre-puberty (17–21 days old), (ii) 3–9 adult (months old), and (iii) old (19–24 months old).

Results

Female mice of all ages exhibited longer and more frequent Up states compared to aged-matched male mice. Power spectrum analysis revealed sex differences in the relative power of Up state events, with female mice showing reduced power in the delta range (1–4 Hz) and increased power in the theta range (4–8 Hz) compared to male mice. No sex differences were found in the characteristics of Up state peak voltage and latency.

Conclusions

The present study revealed for the first time sex differences in intracortical network activity, using an ex vivo paradigm of spontaneously occurring Up/Down states. We report significant sex differences in Up state properties that are already present in pre-puberty animals and are maintained through adulthood and old age.

     

Inferring network activity from synaptic noise

During intense network activity in vivo, cortical neurons are in a high-conductance state, in which the membrane potential (V(m)) is subject to a tremendous fluctuating activity. Clearly, this "synaptic noise" contains information about the activity of the network, but there are presently no methods available to extract this information. We focus here on this problem from a computational neuroscience perspective, with the aim of drawing methods to analyze experimental data. We start from models of cortical neurons, in which high-conductance states stem from the random release of thousands of excitatory and inhibitory synapses. This highly complex system can be simplified by using global synaptic conductances described by effective stochastic processes. The advantage of this approach is that one can derive analytically a number of properties from the statistics of resulting V(m) fluctuations. For example, the global excitatory and inhibitory conductances can be extracted from synaptic noise, and can be related to the mean activity of presynaptic neurons. We show here that extracting the variances of excitatory and inhibitory synaptic conductances can provide estimates of the mean temporal correlation-or level of synchrony-among thousands of neurons in the network. Thus, "probing the network" through intracellular V(m) activity is possible and constitutes a promising approach, but it will require a continuous effort combining theory, computational models and intracellular physiology.     

Self-sustained asynchronous irregular states and Up–Down states in thalamic,cortical and thalamocortical networks of nonlinear integrate-and-fire neurons

Randomly-connected networks of integrate-and-fire (IF) neurons are known to display asynchronous irregular (AI) activity states, which resemble the discharge activity recorded in the cerebral cortex of awake animals. However, it is not clear whether such activity states are specific to simple IF models, or if they also exist in networks where neurons are endowed with complex intrinsic properties similar to electrophysiological measurements. Here, we investigate the occurrence of AI states in networks of nonlinear IF neurons, such as the adaptive exponential IF (Brette-Gerstner-Izhikevich) model. This model can display intrinsic properties such as low-threshold spike (LTS), regular spiking (RS) or fast-spiking (FS). We successively investigate the oscillatory and AI dynamics of thalamic, cortical and thalamocortical networks using such models. AI states can be found in each case, sometimes with surprisingly small network size of the order of a few tens of neurons. We show that the presence of LTS neurons in cortex or in thalamus, explains the robust emergence of AI states for relatively small network sizes. Finally, we investigate the role of spike-frequency adaptation (SFA). In cortical networks with strong SFA in RS cells, the AI state is transient, but when SFA is reduced, AI states can be self-sustained for long times. In thalamocortical networks, AI states are found when the cortex is itself in an AI state, but with strong SFA, the thalamocortical network displays Up and Down state transitions, similar to intracellular recordings during slow-wave sleep or anesthesia. Self-sustained Up and Down states could also be generated by two-layer cortical networks with LTS cells. These models suggest that intrinsic properties such as adaptation and low-threshold bursting activity are crucial for the genesis and control of AI states in thalamocortical networks.     

Brain oscillations,medium spiny neurons,and dopamine

1. The striatum is part of a multisynaptic loop involved in translating higher order cognitive activity into action. The main striatal computational unit is the medium spiny neuron, which integrates inputs arriving from widely distributed cortical neurons and provides the sole striatal output.2. The membrane potential of medium spiny neurons' displays shifts between a very negative resting state (down state) and depolarizing plateaus (up states) which are driven by the excitatory cortical inputs.3. Because striatal spiny neurons fire action potentials only during the up state, these plateau depolarizations are perceived as enabling events that allow information processing through cerebral cortex – basal ganglia circuits. In vivo intracellular recording techniques allow to investigate simultaneously the subthreshold behavior of the medium spiny neuron membrane potential (which is a reading of distributed patterns of cortical activity) and medium spiny neuron firing (which is an index of striatal output).4. Recent studies combining intracellular recordings of striatal neurons with field potential recordings of the cerebral cortex illustrate how the analysis of the input–output transformations performed by medium spiny neurons may help to unveil some aspects of information processing in cerebral cortex – basal ganglia circuits, and to understand the origin of the clinical manifestations of Parkinson's disease and other neurologic and neuropsychiatric disorders that result from alterations in dopamine-dependent information processing in the cerebral cortex – basal ganglia circuits.     

Stochastic amplification of fluctuations in cortical up-States

Cortical neurons are bistable; as a consequence their local field potentials can fluctuate between quiescent and active states, generating slow [Formula: see text] Hz oscillations which are widely known as transitions between Up and Down States. Despite a large number of studies on Up-Down transitions, deciphering its nature, mechanisms and function are still today challenging tasks. In this paper we focus on recent experimental evidence, showing that a class of spontaneous oscillations can emerge within the Up states. In particular, a non-trivial peak around [Formula: see text] Hz appears in their associated power-spectra, what produces an enhancement of the activity power for higher frequencies (in the [Formula: see text] Hz band). Moreover, this rhythm within Ups seems to be an emergent or collective phenomenon given that individual neurons do not lock to it as they remain mostly unsynchronized. Remarkably, similar oscillations (and the concomitant peak in the spectrum) do not appear in the Down states. Here we shed light on these findings by using different computational models for the dynamics of cortical networks in presence of different levels of physiological complexity. Our conclusion, supported by both theory and simulations, is that the collective phenomenon of "stochastic amplification of fluctuations" - previously described in other contexts such as Ecology and Epidemiology - explains in an elegant and parsimonious manner, beyond model-dependent details, this extra-rhythm emerging only in the Up states but not in the Downs.     

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.     

Tuning neocortical pyramidal neurons between integrators and coincidence detectors

Do cortical neurons operate as integrators or as coincidence detectors? Despite the importance of this question, no definite answer has been given yet, because each of these two views can find its own experimental support. Here we investigated this question using models of morphologically-reconstructed neocortical pyramidal neurons under in vivo like conditions. In agreement with experiments we find that the cell is capable of operating in a continuum between coincidence detection and temporal integration, depending on the characteristics of the synaptic inputs. Moreover, the presence of synaptic background activity at a level comparable to intracellular measurements in vivo can modulate the operating mode of the cell, and act as a switch between temporal integration and coincidence detection. These results suggest that background activity can be viewed as an important determinant of the integrative mode of pyramidal neurons. Thus, background activity not only sharpens cortical responses but it can also be used to tune an entire network between integration and coincidence detection modes.     

Spiking patterns and their functional implications in the antennal lobe of the tobacco hornworm Manduca sexta

Bursting as well as tonic firing patterns have been described in various sensory systems. In the olfactory system, spontaneous bursts have been observed in neurons distributed across several synaptic levels, from the periphery, to the olfactory bulb (OB) and to the olfactory cortex. Several in vitro studies indicate that spontaneous firing patterns may be viewed as "fingerprints" of different types of neurons that exhibit distinct functions in the OB. It is still not known, however, if and how neuronal burstiness is correlated with the coding of natural olfactory stimuli. We thus conducted an in vivo study to probe this question in the OB equivalent structure of insects, the antennal lobe (AL) of the tobacco hornworm Manduca sexta. We found that in the moth's AL, both projection (output) neurons (PNs) and local interneurons (LNs) are spontaneously active, but PNs tend to produce spike bursts while LNs fire more regularly. In addition, we found that the burstiness of PNs is correlated with the strength of their responses to odor stimulation--the more bursting the stronger their responses to odors. Moreover, the burstiness of PNs was also positively correlated with the spontaneous firing rate of these neurons, and pharmacological reduction of bursting resulted in a decrease of the neurons' responsiveness. These results suggest that neuronal burstiness reflects a physiological state of these neurons that is directly linked to their response characteristics.     

Intracellular and computational evidence for a dominant role of internal network activity in cortical computations

The mammalian cerebral cortex is characterized by intense spontaneous activity, depending on brain region, age, and behavioral state. Classically, the cortex is considered as being driven by the senses, a paradigm which corresponds well to experiments in quiescent or deeply anesthetized states. In awake animals, however, the spontaneous activity cannot be considered as 'background noise', but is of comparable-or even higher-amplitude than evoked sensory responses. Recent evidence suggests that this internal activity is not only dominant, but also it shares many properties with the responses to natural sensory inputs, suggesting that the spontaneous activity is not independent of the sensory input. Such evidence is reviewed here, with an emphasis on intracellular and computational aspects. Statistical measures, such as the spike-triggered average of synaptic conductances, show that the impact of internal network state on spiking activity is major in awake animals. Thus, cortical activity cannot be considered as being driven by the senses, but sensory inputs rather seem to modulate and modify the internal dynamics of cerebral cortex. This view offers an attractive interpretation not only of dreaming activity (absence of sensory input), but also of several mental disorders.     

Interactions of glutamate and dopamine in a computational model of the striatum

A network model of simplified striatal principal neurons with mutual inhibition was used to investigate possible interactions between cortical glutamatergic and nigral dopaminergic afferents in the neostriatum. Glutamatergic and dopaminergic inputs were represented by an excitatory synaptic conductance and a slow membrane potassium conductance, respectively. Neuronal activity in the model was characterized by episodes of increased action potential firing rates of variable duration and frequency. Autocorrelation histograms constructed from the action potential activity of striatal model neurons showed that reducing peak excitatory conductance had the effect of increasing interspike intervals. On the other hand, the maximum value of the dopamine-sensitive potassium conductance was inversely related to the duration of firing episodes and the maximal firing rates. A smaller potassium conductance restored normal firing rates in the most active neurons at the expense of a larger proportion of neurons showing reduced activity. Thus, a homogeneous network with mutual inhibition can produce equally complex dynamics as have been proposed to occur in a striatal network with two neuron populations that are oppositely regulated by dopamine. Even without mutual inhibition it appears that increased dopamine concentrations could partially compensate for the effects of reduced glutamatergic input in individual neurons.     

Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks

During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous. In contrast, recent in vivo and in vitro population measures of spontaneous activity, using the LFP, EEG, MEG or fMRI suggest that the default state of the cortex is critical, manifested by spontaneous, scale-invariant, cascades of activity known as neuronal avalanches. Criticality keeps a network poised for optimal information processing, but this view seems to be difficult to reconcile with apparently irregular single neuron spiking. Here, we simulate a 10,000 neuron, deterministic, plastic network of spiking neurons. We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking. Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state. The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1.     

The mechanism of intrinsic amplification of hyperpolarizations and spontaneous bursting in striatal cholinergic interneurons

Striatal cholinergic interneurons pause their ongoing firing in response to sensory stimuli that have acquired meaning as a signal for learned behavior. In slices, these cells exhibit both spontaneous activity patterns and spontaneous pauses very similar to those seen in vivo. The mechanisms responsible for ongoing firing and spontaneous pauses were studied in striatal slices using perforated patch recordings. All hyperpolarizations, whether spontaneous or generated by current injection, were amplified and shaped by two hyperpolarization-activated currents. Hyperpolarization onsets were regeneratively amplified by a potassium current (KIR) whose activation promoted further hyperpolarization. The termination of hyperpolarizations was controlled by a time-dependent nonspecific cation current (HCN). The duration and even the sizes of spontaneous and driven hyperpolarizations and pauses in spontaneous activity in cholinergic interneurons are largely autonomous properties of the neuron, rather than reflections of characteristics of the input eliciting the response.     

In vitro recording of raphe unit activity: evidence for endogenous rhythms in presumed serotonergic neurons

The spontaneous activity of single neurons in the nucleus raphe dorsalis was recorded in vitro in mouse brain slices. The neurons displayed the slow and regular discharge pattern characteristic of raphe neurons recorded in vivo. When magnesium ion was added to increase the medium concentration to 20-30 mM for the purpose of inhibiting all synaptic transmission, raphe neurons continued to display the same discharge pattern and rate. The data suggest that the steady rhythmic firing of nucleus raphe dorsalis neurons is generated by an intracellular pacemaker mechanism.     

State-dependent heterogeneity in synaptic depression between pyramidal cell pairs

Paired recordings between CA3 pyramidal neurons were used to study the properties of synaptic plasticity in active and silent synapses. Synaptic depression is accompanied by decreases in both AMPAR and NMDAR function. The mechanisms of synaptic depression, and the potential to undergo activity-dependent plastic changes in efficacy, differ depending on whether a synapse is active, recently silent, or potentiated. These results suggest that silent and active synapses represent distinct synaptic "states," and that once unsilenced, synapses express plasticity in a graded manner. The state in which a synapse resides, and the states recently visited, determine its potential and mechanism for undergoing subsequent plastic changes.     

Long-lasting increases in intrinsic excitability triggered by inhibition

Although experience-dependent changes in neural circuits are commonly assumed to be mediated by synaptic plasticity, modifications of intrinsic excitability may serve as a complementary mechanism. In whole-cell recordings from spontaneously firing vestibular nucleus neurons, brief periods of inhibitory synaptic stimulation or direct membrane hyperpolarization triggered long-lasting increases in spontaneous firing rates and firing responses to intracellular depolarization. These increases in excitability, termed firing rate potentiation, were induced by decreases in intracellular calcium and expressed as reductions in the sensitivity to the BK-type calcium-activated potassium channel blocker iberiotoxin. Firing rate potentiation is a novel form of cellular plasticity that could contribute to motor learning in the vestibulo-ocular reflex.