How can squint change the spacing of ocular dominance columns?

The pattern of ocular dominance columns in primary visual cortex of mammals such as cats and macaque monkeys arises during development by the activity-dependent refinement of thalamocortical connections. Manipulating visual experience in kittens by the induction of squint leads to the emergence of ocular dominance columns with a larger size and larger column-to-column spacing than in normally raised animals. The mechanism underlying this phenomenon is presently unknown. Theory suggests that experience cannot influence the spacing of columns if the development proceeds through purely Hebbian mechanisms. Here we study a developmental model in which Hebbian mechanisms are complemented by activity-dependent regulation of the total strength of afferent synapses converging onto a cortical neurone. We show that this model implies an influence of visual experience on the spacing of ocular dominance columns and provides a conceptually simple explanation for the emergence of larger sized columns in squinting animals. Assuming that during development cortical neurones become active in local groups, which we call co-activated cortical domains (CCDs), ocular dominance segregation is controlled by the size of these groups: (1) Size and spacing of ocular dominance columns are proportional to the size sigma of CCDs. (2) There is a critical size sigma* of CCDs such that ocular dominance columns form if sigmasigma*. This critical size of CCDs is determined by the correlation functions of activity patterns in the two eyes and specifies the influence of experience on ocular dominance segregation. We show that sigma* is larger with squint than with normal visual experience. Since experimental evidence indicates that the size of CCDs decreases during development, ocular dominance columns are predicted to form earlier and with a larger spacing in squinters compared to normal animals.     

Sleep as a fundamental property of neuronal assemblies

Sleep is vital to cognitive performance, productivity, health and well-being. Earlier theories of sleep presumed that it occurred at the level of the whole organism and that it was governed by central control mechanisms. However, evidence now indicates that sleep might be regulated at a more local level in the brain: it seems to be a fundamental property of neuronal networks and is dependent on prior activity in each network. Such local-network sleep might be initiated by metabolically driven changes in the production of sleep-regulatory substances. We discuss a mathematical model which illustrates that the sleep-like states of individual cortical columns can be synchronized through humoral and electrical connections, and that whole-organism sleep occurs as an emergent property of local-network interactions.     

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.     

Maximizing Sensory Dynamic Range by Tuning the Cortical State to Criticality

Modulation of interactions among neurons can manifest as dramatic changes in the state of population dynamics in cerebral cortex. How such transitions in cortical state impact the information processing performed by cortical circuits is not clear. Here we performed experiments and computational modeling to determine how somatosensory dynamic range depends on cortical state. We used microelectrode arrays to record ongoing and whisker stimulus-evoked population spiking activity in somatosensory cortex of urethane anesthetized rats. We observed a continuum of different cortical states; at one extreme population activity exhibited small scale variability and was weakly correlated, the other extreme had large scale fluctuations and strong correlations. In experiments, shifts along the continuum often occurred naturally, without direct manipulation. In addition, in both the experiment and the model we directly tuned the cortical state by manipulating inhibitory synaptic interactions. Our principal finding was that somatosensory dynamic range was maximized in a specific cortical state, called criticality, near the tipping point midway between the ends of the continuum. The optimal cortical state was uniquely characterized by scale-free ongoing population dynamics and moderate correlations, in line with theoretical predictions about criticality. However, to reproduce our experimental findings, we found that existing theory required modifications which account for activity-dependent depression. In conclusion, our experiments indicate that in vivo sensory dynamic range is maximized near criticality and our model revealed an unanticipated role for activity-dependent depression in this basic principle of cortical function.     

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.     

Dense Neuron Clustering Explains Connectivity Statistics in Cortical Microcircuits

Local cortical circuits appear highly non-random, but the underlying connectivity rule remains elusive. Here, we analyze experimental data observed in layer 5 of rat neocortex and suggest a model for connectivity from which emerge essential observed non-random features of both wiring and weighting. These features include lognormal distributions of synaptic connection strength, anatomical clustering, and strong correlations between clustering and connection strength. Our model predicts that cortical microcircuits contain large groups of densely connected neurons which we call clusters. We show that such a cluster contains about one fifth of all excitatory neurons of a circuit which are very densely connected with stronger than average synapses. We demonstrate that such clustering plays an important role in the network dynamics, namely, it creates bistable neural spiking in small cortical circuits. Furthermore, introducing local clustering in large-scale networks leads to the emergence of various patterns of persistent local activity in an ongoing network activity. Thus, our results may bridge a gap between anatomical structure and persistent activity observed during working memory and other cognitive processes.     

The ATP-cytokine-adenosine hypothesis: How the brain translates past activity into sleep

Tumor necrosis factor alpha (TNF), interleukin-1 beta (IL1), and other cytokines are involved in non-rapid eye movement sleep (NREM) regulation under physiological and inflammatory conditions. Brain levels of IL1 and TNF increase with prolonged wakefulness. Injection of exogenous IL1 or TNF, mimicking sleep loss, induces sleepiness, excess sleep, fatigue, poor cognition, and enhanced sensitivity to pain. These symptoms characterize the syndrome associated with sleep loss. Extracellular ATP released during neuro- and glio-transmission, acting via purine P2 receptors on glia, releases IL1 and TNF. This extracellular ATP mechanism may provide an index of activity used by the brain to keep track of prior wakefulness. Prolonged wakefulness is associated with enhanced neuronal activity. TNF and IL1, in turn, act on neurons to change their intrinsic properties and sensitivities to neurotransmitters and neuromodulators such as adenosine and glutamate. Such actions change network input–output properties (i.e. state shift). State oscillations, for instance, occur within cortical columns and are responsive to TNF. Sleep is thus viewed as a local usedependent process regulated in part by cytokines. Further, state oscillations are viewed as a fundamental process of any neuronal/glia network. To investigate these hypotheses we developed an in vitro neuronal/glia culture system exhibiting field potential oscillations and have mathematically modeled the local use-dependent view of sleep initiation. These views have profound implications for sleep pathologies and function.

     

Methods for predicting cortical UP and DOWN states from the phase of deep layer local field potentials

During anesthesia, slow-wave sleep and quiet wakefulness, neuronal membrane potentials collectively switch between de- and hyperpolarized levels, the cortical UP and DOWN states. Previous studies have shown that these cortical UP/DOWN states affect the excitability of individual neurons in response to sensory stimuli, indicating that a significant amount of the trial-to-trial variability in neuronal responses can be attributed to ongoing fluctuations in network activity. However, as intracellular recordings are frequently not available, it is important to be able to estimate their occurrence purely from extracellular data. Here, we combine in vivo whole cell recordings from single neurons with multi-site extracellular microelectrode recordings, to quantify the performance of various approaches to predicting UP/DOWN states from the deep-layer local field potential (LFP). We find that UP/DOWN states in deep cortical layers of rat primary auditory cortex (A1) are predictable from the phase of LFP at low frequencies (< 4 Hz), and that the likelihood of a given state varies sinusoidally with the phase of LFP at these frequencies. We introduce a novel method of detecting cortical state by combining information concerning the phase of the LFP and ongoing multi-unit activity.     

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.     

Acetylcholine-dependent potentiation of temporal frequency representation in the barrel cortex does not depend on response magnitude during conditioning

The response properties of neurons of the postero-medial barrel sub-field of the somatosensory cortex (the cortical structure receiving information from the mystacial vibrissae can be modified as a consequence of peripheral manipulations of the afferent activity. This plasticity depends on the integrity of the cortical cholinergic innervation, which originates at the nucleus basalis magnocellularis (NBM). The activity of the NBM is related to the behavioral state of the animal and the putative cholinergic neurons are activated by specific events, such as reward-related signals, during behavioral learning. Experimental studies on acetylcholine (ACh)-dependent cortical plasticity have shown that ACh is needed for both the induction and the expression of plastic modifications induced by sensory-cholinergic pairings. Here we review and discuss ACh-dependent plasticity and activity-dependent plasticity and ask whether these two mechanisms are linked. To address this question, we analyzed our data and tested whether changes mediated by ACh were activity-dependent. We show that ACh-dependent potentiation of response in the barrel cortex of rats observed after sensory-cholinergic pairing was not correlated to the changes in activity induced during pairing. Since these results suggest that the effect of ACh during pairing is not exerted through a direct control of the post-synaptic activity, we propose that ACh might induce its effect either pre- or post-synaptically through activation of second messenger cascades.     

A zonal model of cortical functions

A model of cortical functions is developed with the object of simulating the observed behavior of individual neurons organized in unit circuits and functional systems of the cerebellum, the cerebrum and the hippocampal formation. The neuronal model is capable of representing refractory and potentiated states, as well as the firing and lowest resting states. The unit circuits of each system consist of all common types of cells with known synaptic connections. In the cerebral system these unit circuits are interconnected to form columns as well as zones. A new discrete neural network equation, which takes account of interactions with the extracellular field, is proposed to simulate electrical activity in these circuits. A coherent theory of cortical activity and functions is derived that accounts for many of the observed phenomena, including those associated with the development of long-term potentiation and sequential memory. Three appendices are devoted to the theory of extracellular interactions, the derivation of non-linear network equations, and a computer program to simulate learning in the cortex.     

Output-mode transitions are controlled by prolonged inactivation of sodium channels in pyramidal neurons of subiculum

Transitions between different behavioral states, such as sleep or wakefulness, quiescence or attentiveness, occur in part through transitions from action potential bursting to single spiking. Cortical activity, for example, is determined in large part by the spike output mode from the thalamus, which is controlled by the gating of low-voltage–activated calcium channels. In the subiculum—the major output of the hippocampus—transitions occur from bursting in the delta-frequency band to single spiking in the theta-frequency band. We show here that these transitions are influenced strongly by the inactivation kinetics of voltage-gated sodium channels. Prolonged inactivation of sodium channels is responsible for an activity-dependent switch from bursting to single spiking, constituting a novel mechanism through which network dynamics are controlled by ion channel gating.     

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.     

Modeling reveals cortical dynein-dependent fluctuations in bipolar spindle length

Proper formation and maintenance of the mitotic spindle is required for faithful cell division. Although much work has been done to understand the roles of the key molecular components of the mitotic spindle, identifying the consequences of force perturbations in the spindle remains a challenge. We develop a computational framework accounting for the minimal force requirements of mitotic progression. To reflect early spindle formation, we model microtubule dynamics and interactions with major force-generating motors, excluding chromosome interactions that dominate later in mitosis. We directly integrate our experimental data to define and validate the model. We then use simulations to analyze individual force components over time and their relationship to spindle dynamics, making it distinct from previously published models. We show through both model predictions and biological manipulation that rather than achieving and maintaining a constant bipolar spindle length, fluctuations in pole-to-pole distance occur that coincide with microtubule binding and force generation by cortical dynein. Our model further predicts that high dynein activity is required for spindle bipolarity when kinesin-14 (HSET) activity is also high. To the best of our knowledge, our results provide novel insight into the role of cortical dynein in the regulation of spindle bipolarity.     

Chaos and synchrony in a model of a hypercolumn in visual cortex

Neurons in cortical slices emit spikes or bursts of spikes regularly in response to a suprathreshold current injection. This behavior is in marked contrast to the behavior of cortical neurons in vivo, whose response to electrical or sensory input displays a strong degree of irregularity. Correlation measurements show a significant degree of synchrony in the temporal fluctuations of neuronal activities in cortex. We explore the hypothesis that these phenomena are the result of the synchronized chaos generated by the deterministic dynamics of local cortical networks. A model of a hypercolumn in the visual cortex is studied. It consists of two populations of neurons, one inhibitory and one excitatory. The dynamics of the neurons is based on a Hodgkin-Huxley type model of excitable voltage-clamped cells with several cellular and synaptic conductances. A slow potassium current is included in the dynamics of the excitatory population to reproduce the observed adaptation of the spike trains emitted by these neurons. The pattern of connectivity has a spatial structure which is correlated with the internal organization of hypercolumns in orientation columns. Numerical simulations of the model show that in an appropriate parameter range, the network settles in a synchronous chaotic state, characterized by a strong temporal variability of the neural activity which is correlated across the hypercolumn. Strong inhibitory feedback is essential for the stabilization of this state. These results show that the cooperative dynamics of large neuronal networks are capable of generating variability and synchrony similar to those observed in cortex. Auto-correlation and cross-correlation functions of neuronal spike trains are computed, and their temporal and spatial features are analyzed. In other parameter regimes, the network exhibits two additional states: synchronized oscillations and an asynchronous state. We use our model to study cortical mechanisms for orientation selectivity. It is shown that in a suitable parameter regime, when the input is not oriented, the network has a continuum of states, each representing an inhomogeneous population activity which is peaked at one of the orientation columns. As a result, when a weakly oriented input stimulates the network, it yields a sharp orientation tuning. The properties of the network in this regime, including the appearance of virtual rotations and broad stimulus-dependent cross-correlations, are investigated. The results agree with the predictions of the mean field theory which was previously derived for a simplified model of stochastic, two-state neurons. The relation between the results of the model and experiments in visual cortex are discussed.     

Activated cortical states: Experiments, analyses and models

In awake animals, the cerebral cortex displays an "activated" state, with distinct characteristics compared to other states like slow-wave sleep or anesthesia. These characteristics include a sustained depolarized membrane potential (V(m)) and irregular firing activity. In the present paper, we evaluate our understanding of cortical activated states from a computational neuroscience point of view. We start by reviewing the electrophysiological characteristics of activated cortical states based on recordings and analysis performed in awake cat association cortex. These analyses show that cortical activity is characterized by an apparent Poisson-distributed stochastic dynamics, both at the single-cell and population levels, and that single cells display a high-conductance state dominated by inhibition. We next overview computational models of the "awake" cortex, and perform the same analyses as in the experiments. Many properties identified experimentally are indeed reproduced by models, such as depolarized V(m), irregular firing with apparent Poisson statistics, and the determinant role of inhibitory fluctuations on spiking. However, other features are not well reproduced, such as firing statistics and the conductance state of the membrane, suggesting that the network state displayed by models is not entirely correct. We also show how networks can approach a correct conductance state, suggesting ways by which future models will generate activity fully consistent with experimental data.     

Recovery of rhythmic activity in a central pattern generator: analysis of the role of neuromodulator and activity-dependent mechanisms

The pyloric network of decapods crustaceans can undergo dramatic rhythmic activity changes. Under normal conditions the network generates low frequency rhythmic activity that depends obligatorily on the presence of neuromodulatory input from the central nervous system. When this input is removed (decentralization) the rhythmic activity ceases. In the continued absence of this input, periodic activity resumes after a few hours in the form of episodic bursting across the entire network that later turns into stable rhythmic activity that is nearly indistinguishable from control (recovery). It has been proposed that an activity-dependent modification of ionic conductance levels in the pyloric pacemaker neuron drives the process of recovery of activity. Previous modeling attempts have captured some aspects of the temporal changes observed experimentally, but key features could not be reproduced. Here we examined a model in which slow activity-dependent regulation of ionic conductances and slower neuromodulator-dependent regulation of intracellular Ca²⁺ concentration reproduce all the temporal features of this recovery. Key aspects of these two regulatory mechanisms are their independence and their different kinetics. We also examined the role of variability (noise) in the activity-dependent regulation pathway and observe that it can help to reduce unrealistic constraints that were otherwise required on the neuromodulator-dependent pathway. We conclude that small variations in intracellular Ca²⁺ concentration, a Ca²⁺ uptake regulation mechanism that is directly targeted by neuromodulator-activated signaling pathways, and variability in the Ca²⁺ concentration sensing signaling pathway can account for the observed changes in neuronal activity. Our conclusions are all amenable to experimental analysis.     

Dissociating ocular dominance column development and ocular dominance plasticity: a neurotrophic model

 Recent experimental data indicate that both neurotrophic factors (NTFs) and intracortical inhibitory circuitry are implicated in the development and plasticity of ocular dominance columns. We extend a neurotrophic model of developmental synaptic plasticity, which previously failed to account correctly for the differences between monocular deprivation and binocular deprivation, and show that the inclusion of lateral cortical inhibition is indeed necessary in understanding the effects of visual deprivation in the model. In particular, we argue that monocular deprivation causes a differential shift in the balance between inhibition and excitation in cortical columns, down-regulating NTFs in deprived-eye columns and up-regulating NTFs in undeprived-eye columns; during binocular deprivation, however, no such shift occurs. We thus postulate that the response to visual deprivation is at the level of the cortical circuit, while the mechanisms of afferent segregation are at the molecular or cellular level. Such a dissociation is supported by recent experimental work challenging the assumption that columnar organisation develops in an activity-dependent, competitive fashion. Our extended model also questions recent attempts to distinguish between heterosynaptic and homosynaptic models of synaptic plasticity. Received: 17 April 2001 / Accepted in revised form: 7 November 2001