Neural dynamics in cortex-striatum co-cultures--II. Spatiotemporal characteristics of neuronal activity

On the Role of Speed in Technological and Biological Information Transfer for Computations

In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so in real-world implementations, the propagation speed of information cannot exceed the speed of its carrier. Because of this limitation, one must also consider the transfer time between computing units for any implementation. We need a different mathematical method to consider this limitation: classic mathematics can only describe infinitely fast and small computing system implementations. The difference between mathematical handling methods leads to different descriptions of the computing features of the systems. The proposed handling also explains why biological implementations can have lifelong learning and technological ones cannot. Our conclusion about learning matches published experimental evidence, both in biological and technological computing.

Partial information decomposition reveals that synergistic neural integration is greater downstream of recurrent information flow in organotypic cortical cultures

The directionality of network information flow dictates how networks process information. A central component of information processing in both biological and artificial neural networks is their ability to perform synergistic integration–a type of computation. We established previously that synergistic integration varies directly with the strength of feedforward information flow. However, the relationships between both recurrent and feedback information flow and synergistic integration remain unknown. To address this, we analyzed the spiking activity of hundreds of neurons in organotypic cultures of mouse cortex. We asked how empirically observed synergistic integration–determined from partial information decomposition–varied with local functional network structure that was categorized into motifs with varying recurrent and feedback information flow. We found that synergistic integration was elevated in motifs with greater recurrent information flow beyond that expected from the local feedforward information flow. Feedback information flow was interrelated with feedforward information flow and was associated with decreased synergistic integration. Our results indicate that synergistic integration is distinctly influenced by the directionality of local information flow.

Networks compute information. That is, they modify inputs to generate distinct outputs. These computations are an important component of network information processing. Knowing how the routing of information in a network influences computation is therefore crucial. Here we asked how a key form of computation—synergistic integration—is related to the direction of local information flow in networks of spiking cortical neurons. Specifically, we asked how information flow between input neurons (i.e., recurrent information flow) and information flow from output neurons to input neurons (i.e., feedback information flow) was related to the amount of synergistic integration performed by output neurons. We found that greater synergistic integration occurred where there was more recurrent information flow. And, lesser synergistic integration occurred where there was more feedback information flow relative to feedforward information flow. These results show that computation, in the form of synergistic integration, is distinctly influenced by the directionality of local information flow. Such work is valuable for predicting where and how network computation occurs and for designing networks with desired computational abilities.

Correlated activity favors synergistic processing in local cortical networks in vitro at synaptically relevant timescales

Neural information processing is widely understood to depend on correlations in neuronal activity. However, whether correlation is favorable or not is contentious. Here, we sought to determine how correlated activity and information processing are related in cortical circuits. Using recordings of hundreds of spiking neurons in organotypic cultures of mouse neocortex, we asked whether mutual information between neurons that feed into a common third neuron increased synergistic information processing by the receiving neuron. We found that mutual information and synergistic processing were positively related at synaptic timescales (0.05-14 ms), where mutual information values were low. This effect was mediated by the increase in information transmission-of which synergistic processing is a component-that resulted as mutual information grew. However, at extrasynaptic windows (up to 3,000 ms), where mutual information values were high, the relationship between mutual information and synergistic processing became negative. In this regime, greater mutual information resulted in a disproportionate increase in redundancy relative to information transmission. These results indicate that the emergence of synergistic processing from correlated activity differs according to timescale and correlation regime. In a low-correlation regime, synergistic processing increases with greater correlation, and in a high-correlation regime, synergistic processing decreases with greater correlation.

Striatal network modeling in Huntington's Disease

Medium spiny neurons (MSNs) comprise over 90% of cells in the striatum. In vivo MSNs display coherent burst firing cell assembly activity patterns, even though isolated MSNs do not burst fire intrinsically. This activity is important for the learning and execution of action sequences and is characteristically dysregulated in Huntington's Disease (HD). However, how dysregulation is caused by the various neural pathologies affecting MSNs in HD is unknown. Previous modeling work using simple cell models has shown that cell assembly activity patterns can emerge as a result of MSN inhibitory network interactions. Here, by directly estimating MSN network model parameters from single unit spiking data, we show that a network composed of much more physiologically detailed MSNs provides an excellent quantitative fit to wild type (WT) mouse spiking data, but only when network parameters are appropriate for the striatum. We find the WT MSN network is situated in a regime close to a transition from stable to strongly fluctuating network dynamics. This regime facilitates the generation of low-dimensional slowly varying coherent activity patterns and confers high sensitivity to variations in cortical driving. By re-estimating the model on HD spiking data we discover network parameter modifications are consistent across three very different types of HD mutant mouse models (YAC128, Q175, R6/2). In striking agreement with the known pathophysiology we find feedforward excitatory drive is reduced in HD compared to WT mice, while recurrent inhibition also shows phenotype dependency. We show that these modifications shift the HD MSN network to a sub-optimal regime where higher dimensional incoherent rapidly fluctuating activity predominates. Our results provide insight into a diverse range of experimental findings in HD, including cognitive and motor symptoms, and may suggest new avenues for treatment.

An Improved in vitro Model of Cortical Tissue

Intracortical electrodes for brain-machine interfaces rely on intimate contact with tissues for recording signals and stimulating neurons. However, the long-term viability of intracortical electrodes in vivo is poor, with a major contributing factor being the development of a glial scar. In vivo approaches for evaluating responses to intracortical devices are resource intensive and complex, making statistically significant, high throughput data difficult to obtain. In vitro models provide an alternative to in vivo studies; however, existing approaches have limitations which restrict the translation of the cellular reactions to the implant scenario. Notably, there is no current robust model that includes astrocytes, microglia, oligodendrocytes and neurons, the four principle cell types, critical to the health, function and wound responses of the central nervous system (CNS). In previous research a co-culture of primary mouse mature mixed glial cells and immature neural precursor cells were shown to mimic several key properties of the CNS response to implanted electrode materials. However, the method was not robust and took up to 63 days, significantly affecting reproducibility and widespread use for assessing brain-material interactions. In the current research a new co-culture approach has been developed and evaluated using immunocytochemistry and quantitative polymerase chain reaction (qPCR). The resulting method reduced the time in culture significantly and the culture model was shown to have a genetic signature similar to that of healthy adult mouse brain. This new robust CNS culture model has the potential to significantly improve the capacity to translate in vitro data to the in vivo responses.

Computation is concentrated in rich clubs of local cortical networks

To understand how neural circuits process information, it is essential to identify the relationship between computation and circuit organization. Rich clubs, highly interconnected sets of neurons, are known to propagate a disproportionate amount of information within cortical circuits. Here, we test the hypothesis that rich clubs also perform a disproportionate amount of computation. To do so, we recorded the spiking activity of on average ∼300 well-isolated individual neurons from organotypic cortical cultures. We then constructed weighted, directed networks reflecting the effective connectivity between the neurons. For each neuron, we quantified the amount of computation it performed based on its inputs. We found that rich-club neurons compute ∼160% more information than neurons outside of the rich club. The amount of computation performed in the rich club was proportional to the amount of information propagation by the same neurons. This suggests that in these circuits, information propagation drives computation. In total, our findings indicate that rich-club organization in effective cortical circuits supports not only information propagation but also neural computation.

Highly Heterogeneous Excitatory Connections Require Less Amount of Noise to Sustain Firing Activities in Cortical Networks

Cortical networks both in vivo and in vitro sustain asynchronous irregular firings with extremely low frequency. To realize such self-sustained activity in neural network models, balance between excitatory and inhibitory activities is known to be one of the keys. In addition, recent theoretical studies have revealed that another feature commonly observed in cortical networks, i.e., sparse but strong connections and dense weak connections, plays an essential role. The previous studies, however, have not thoroughly considered the cooperative dynamics between a network of such heterogeneous synaptic connections and intrinsic noise. The noise stimuli, representing inherent nature of the neuronal activities, e.g., variability of presynaptic discharges, should be also of significant importance for sustaining the irregular firings in cortical networks. Here, we numerically demonstrate that highly heterogeneous distribution, typically a lognormal type, of excitatory-to-excitatory connections, reduces the amount of noise required to sustain the network firing activities. In the sense that noise consumes an energy resource, the heterogeneous network receiving less amount of noise stimuli is considered to realize an efficient dynamics in cortex. A noise-driven network of bi-modally distributed synapses further shows that many weak and a few very strong synapses are the key feature of the synaptic heterogeneity, supporting the network firing activity.

Computation is concentrated in rich clubs of local cortical networks

To understand how neural circuits process information, it is essential to identify the relationship between computation and circuit organization. Rich clubs, highly interconnected sets of neurons, are known to propagate a disproportionate amount of information within cortical circuits. Here, we test the hypothesis that rich clubs also perform a disproportionate amount of computation. To do so, we recorded the spiking activity of on average ∼300 well-isolated individual neurons from organotypic cortical cultures. We then constructed weighted, directed networks reflecting the effective connectivity between the neurons. For each neuron, we quantified the amount of computation it performed based on its inputs. We found that rich-club neurons compute ∼160% more information than neurons outside of the rich club. The amount of computation performed in the rich club was proportional to the amount of information propagation by the same neurons. This suggests that in these circuits, information propagation drives computation. In total, our findings indicate that rich-club organization in effective cortical circuits supports not only information propagation but also neural computation.

Brain active transmembrane water cycling measured by MR is associated with neuronal activity

PURPOSE

fMRI is widely used to study brain activity. Unfortunately, conventional fMRI methods assess neuronal activity only indirectly, through hemodynamic coupling. Here, we show that active, steady-state transmembrane water cycling (AWC) could serve as a basis for a potential fMRI mechanism for direct neuronal activity detection.

METHODS

AWC and neuronal actitivity in rat organotypic cortical cultures were simultaneously measured with a hybrid MR-fluorescence system. Perfusion with a paramagnetic MRI contrast agent, Gadoteridol, allows NMR determination of the kinetics of transcytolemmal water exchange. Changes in intracellular calcium concentration, [Ca_i ²⁺ ] were used as a proxy of neuronal activity and were monitored by fluorescence imaging.

RESULTS

When we alter neuronal activity by titrating with extracellular [K⁺ ] near the normal value, we see an AWC response resembling Na⁺ -K⁺ -ATPase (NKA) Michaelis-Menten behavior. When we treat with the voltage-gated sodium channel inhibitor, or with an excitatory postsynaptic inhibitor cocktail, we see AWC decrease by up to 71%. AWC was found also to be positively correlated with the basal level of spontaneous activity, which varies in different cultures.

CONCLUSIONS

These results suggest that AWC is associated with neuronal activity and NKA activity is a major contributor in coupling AWC to neuronal activity. Although AWC comprises steady-state, homeostatic transmembrane water exchange, our analysis also yields a simultaneous measure of the average cell volume, which reports any slower net transmembrane water transport.

Trichloroethylene and Parkinson's Disease: Risk Assessment

This study was conducted to investigate the mechanism of action and extent of selective dopaminergic neurodegeneration caused by exposure to trichloroethylene (TCE) leading to the endogenous formation of the neurotoxin 1-trichloromethyl-1,2,3,4-tetrahydro-β-carboline (TaClo) in rodents. Beginning at 3 months of age, male C57BL/6 mice received oral TCE dissolved in vehicle for 8 months. Dopaminergic neuronal loss was assessed by nigral tyrosine hydroxylase (TH) immunoreactivity. Selective dopaminergic neurodegeneration was determined based on histological analysis of non-dopaminergic neurons in the brain. Behavioral assays were evaluated using open field activity and rotarod tests. Mitochondrial complex I activity, oxidative stress markers, and microglial activation were also examined in the substantia nigra. The level of TaClo was detected using HPLC-electrospray ionization tandem mass spectrometry. Dopaminergic neurotoxicity of TaClo was determined in midbrain organotypic cultures from rat pups. Following 8 months of TCE treatment, there was a progressive and selective loss of 50% of the dopaminergic neurons in mouse substantia nigra (SN) and about 50% loss of dopamine and 72% loss of 3,4-dihydroxyphenylacetic acid in the striatum, respectively. In addition, motor deficits, mitochondrial impairment, oxidative stress, and inflammation were measured. TaClo content was quantified in the brain after TCE treatment. In organotypic cultures, TaClo rather than TCE induced dopaminergic neuronal loss, similar to MPP⁺. TCE exposure may stimulate the endogenous formation of TaClo, which is responsible for dopaminergic neurodegeneration. However, even prolonged administration of TCE was insufficient for producing a greater than 50% loss of nigral dopamine neurons, indicating that additional co-morbid factors would be needed for mimicking the profound loss of dopamine neurons seen in Parkinson's disease.

Global and local excitation and inhibition shape the dynamics of the cortico-striatal-thalamo-cortical pathway

The cortico-striatal-thalamo-cortical (CSTC) pathway is a brain circuit that controls movement execution, habit formation and reward. Hyperactivity in the CSTC pathway is involved in obsessive compulsive disorder (OCD), a neuropsychiatric disorder characterized by the execution of repetitive involuntary movements. The striatum shapes the activity of the CSTC pathway through the coordinated activation of two classes of medium spiny neurons (MSNs) expressing D1 or D2 dopamine receptors. The exact mechanisms by which balanced excitation/inhibition (E/I) of these cells controls the network dynamics of the CSTC pathway remain unclear. Here we use non-linear modeling of neuronal activity and bifurcation theory to investigate how global and local changes in E/I of MSNs regulate the activity of the CSTC pathway. Our findings indicate that a global and proportionate increase in E/I pushes the system to states of generalized hyper-activity throughout the entire CSTC pathway. Certain disproportionate changes in global E/I trigger network oscillations. Local changes in the E/I of MSNs generate specific oscillatory behaviors in MSNs and in the CSTC pathway. These findings indicate that subtle changes in the relative strength of E/I of MSNs can powerfully control the network dynamics of the CSTC pathway in ways that are not easily predicted by its synaptic connections.

Frequency and function in the basal ganglia: the origins of beta and gamma band activity

KEY POINTS

Neuronal oscillations in the basal ganglia have been observed to correlate with behaviours, although the causal mechanisms and functional significance of these oscillations remain unknown. We present a novel computational model of the healthy basal ganglia, constrained by single unit recordings from non-human primates. When the model is run using inputs that might be expected during performance of a motor task, the network shows emergent phenomena: it functions as a selection mechanism and shows spectral properties that match those seen in vivo. Beta frequency oscillations are shown to require pallido-striatal feedback, and occur with behaviourally relevant cortical input. Gamma oscillations arise in the subthalamic-globus pallidus feedback loop, and occur during movement. The model provides a coherent framework for the study of spectral, temporal and functional analyses of the basal ganglia and lays the foundation for an integrated approach to study basal ganglia pathologies such as Parkinson's disease in silico.

ABSTRACT

Neural oscillations in the basal ganglia (BG) are well studied yet remain poorly understood. Behavioural correlates of spectral activity are well described, yet a quantitative hypothesis linking time domain dynamics and spectral properties to BG function has been lacking. We show, for the first time, that a unified description is possible by interpreting previously ignored structure in data describing globus pallidus interna responses to cortical stimulation. These data were used to expose a pair of distinctive neuronal responses to the stimulation. This observation formed the basis for a new mathematical model of the BG, quantitatively fitted to the data, which describes the dynamics in the data, and is validated against other stimulus protocol experiments. A key new result is that when the model is run using inputs hypothesised to occur during the performance of a motor task, beta and gamma frequency oscillations emerge naturally during static-force and movement, respectively, consistent with experimental local field potentials. This new model predicts that the pallido-striatum connection has a key role in the generation of beta band activity, and that the gamma band activity associated with motor task performance has its origins in the pallido-subthalamic feedback loop. The network's functionality as a selection mechanism also occurs as an emergent property, and closer fits to the data gave better selection properties. The model provides a coherent framework for the study of spectral, temporal and functional analyses of the BG and therefore lays the foundation for an integrated approach to study BG pathologies such as Parkinson's disease in silico.

A Low-Correlation Resting State of the Striatum during Cortical Avalanches and Its Role in Movement Suppression

During quiet resting behavior, involuntary movements are suppressed. Such movement control is attributed to cortico-basal ganglia loops, yet population dynamics within these loops during resting and their relation to involuntary movements are not well characterized. Here, we show by recording cortical and striatal ongoing population activity in awake rats during quiet resting that intrastriatal inhibition maintains a low-correlation striatal resting state in the presence of cortical neuronal avalanches. Involuntary movements arise from disturbed striatal resting activity through two different population dynamics. Nonselectively reducing intrastriatal γ-aminobutyric acid (GABA) receptor-A inhibition synchronizes striatal dynamics, leading to involuntary movements at low rate. In contrast, reducing striatal interneuron (IN)-mediated inhibition maintains decorrelation and induces intermittent involuntary movements at high rate. This latter scenario was highly effective in modulating cortical dynamics at a subsecond timescale. To distinguish intrastriatal processing from loop dynamics, cortex-striatum-midbrain cultures, which lack feedback to cortex, were used. Cortical avalanches in vitro were accompanied by low-correlated resting activity in the striatum and nonselective reduction in striatal inhibition synchronized striatal neurons similar to in vivo. Importantly, reduction of inhibition from striatal INs maintained low correlations in the striatum while reorganizing functional connectivities among striatal neurons. Our results demonstrate the importance of two major striatal microcircuits in distinctly regulating striatal and cortical resting state dynamics. These findings suggest that specific functional connectivities of the striatum that are maintained by local inhibition are important in movement control.

Why don’t neuronal “avalanches” in resting-state cortex cause involuntary movements? This study shows that a low-correlation striatal resting state suppresses such movements and explores mechanisms that disrupt this inhibition.

Even in the absence of apparent motor output, the brain produces a rich repertoire of neuronal activity patterns known as “resting state” activity. In the outer layer of the cortex, resting state patterns emerge as neuronal avalanches, precisely scale-invariant spatiotemporal bursts that often engage large populations of neurons. Little is known about how the brain suppresses involuntary movements during such activity. Here, we show that the striatum, which is part of the cortex-basal ganglia loop, maintains a low-correlation state during resting activity. By using a combination of in vivo and in vitro approaches with pharmacological manipulations, we demonstrate that the precise configuration of this low-correlation state effectively contributes to involuntary movements. Nonselective blockade of intra-striatal inhibition abolished the low-correlation striatal resting state, barely affected cortical avalanches, and led to involuntary movements at low rate. In contrast, selectively reducing striatal interneuron inhibition strongly affected cortical avalanches and triggered involuntary movements at high rate while maintaining a relatively decorrelated striatal resting state. Our results demonstrate the importance of different inhibitory striatal circuits in the suppression of involuntary movements and suggest that the precise spatiotemporal configuration of striatal activity plays an active role in the regulation of cortical resting state activity and motor control.

Effective Suppression of Pathological Synchronization in Cortical Networks by Highly Heterogeneous Distribution of Inhibitory Connections

Even without external random input, cortical networks in vivo sustain asynchronous irregular firing with low firing rate. In addition to detailed balance between excitatory and inhibitory activities, recent theoretical studies have revealed that another feature commonly observed in cortical networks, i.e., long-tailed distribution of excitatory synapses implying coexistence of many weak and a few extremely strong excitatory synapses, plays an essential role in realizing the self-sustained activity in recurrent networks of biologically plausible spiking neurons. The previous studies, however, have not considered highly non-random features of the synaptic connectivity, namely, bidirectional connections between cortical neurons are more common than expected by chance and strengths of synapses are positively correlated between pre- and postsynaptic neurons. The positive correlation of synaptic connections may destabilize asynchronous activity of networks with the long-tailed synaptic distribution and induce pathological synchronized firing among neurons. It remains unclear how the cortical network avoids such pathological synchronization. Here, we demonstrate that introduction of the correlated connections indeed gives rise to synchronized firings in a cortical network model with the long-tailed distribution. By using a simplified feed-forward network model of spiking neurons, we clarify the underlying mechanism of the synchronization. We then show that the synchronization can be efficiently suppressed by highly heterogeneous distribution, typically a lognormal distribution, of inhibitory-to-excitatory connection strengths in a recurrent network model of cortical neurons.

Polysialic Acid Acute Depletion Induces Structural Plasticity in Interneurons and Impairs the Excitation/Inhibition Balance in Medial Prefrontal Cortex Organotypic Cultures

The structure and function of the medial prefrontal cortex (mPFC) is affected in several neuropsychiatric disorders, including schizophrenia and major depression. Recent studies suggest that imbalances between excitatory and inhibitory activity (E/I) may be responsible for this cortical dysfunction and therefore, may underlie the core symptoms of these diseases. This E/I imbalance seems to be correlated with alterations in the plasticity of interneurons but there is still scarce information on the mechanisms that may link these phenomena. The polysialylated form of the neural cell adhesion molecule (PSA-NCAM) is a good candidate, because it modulates the neuronal plasticity of interneurons and its expression is altered in schizophrenia and major depression. To address this question, we have developed an in vitro model using mPFC organotypic cultures of transgenic mice displaying fluorescent spiny interneurons. After enzymatic depletion of PSA, the spine density of interneurons, the number of synaptic puncta surrounding pyramidal neuron somata and the E/I ratio were strongly affected. These results point to the polysialylation of NCAM as an important factor in the maintenance of E/I balance and the structural plasticity of interneurons. This may be particularly relevant for better understanding the etiology of schizophrenia and major depression.

Altered striatal rhythmic activity in cylindromatosis knock-out mice due to enhanced GABAergic inhibition

Despite the highest expression in striatum, physiological function of cylindromatosis (CYLD), a deubiquitinating enzyme, remains unexplored. We found, in the present study, that the duration of spontaneous up-states in the striatum is shorter and membrane potential fluctuation preceding action potential and firing rate are increased in Cyld(-/-) mice. Excess striatal GABAergic inhibition likely plays the major role in this alteration as supported by the findings: (1) the levels of striatal GABAA and GABAB receptors in Cyld(-/-) mice are increased, (2) pharmacological blockade of GABAB receptors rescues the shortened up-state phenotype, and (3) pharmacological blockade of GABAA receptors rescues the power of beta frequency oscillations. Our results indicate that CYLD alters striatal network function by regulating the protein expression levels of GABA receptors.

Temporal correlations in neuronal avalanche occurrence

Ongoing cortical activity consists of sequences of synchronized bursts, named neuronal avalanches, whose size and duration are power law distributed. These features have been observed in a variety of systems and conditions, at all spatial scales, supporting scale invariance, universality and therefore criticality. However, the mechanisms leading to burst triggering, as well as the relationship between bursts and quiescence, are still unclear. The analysis of temporal correlations constitutes a major step towards a deeper understanding of burst dynamics. Here, we investigate the relation between avalanche sizes and quiet times, as well as between sizes of consecutive avalanches recorded in cortex slice cultures. We show that quiet times depend on the size of preceding avalanches and, at the same time, influence the size of the following one. Moreover we evidence that sizes of consecutive avalanches are correlated. In particular, we show that an avalanche tends to be larger or smaller than the following one for short or long time separation, respectively. Our analysis represents the first attempt to provide a quantitative estimate of correlations between activity and quiescence in the framework of neuronal avalanches and will help to enlighten the mechanisms underlying spontaneous activity.

Mechanisms of Self-Sustained Oscillatory States in Hierarchical Modular Networks with Mixtures of Electrophysiological Cell Types

In a network with a mixture of different electrophysiological types of neurons linked by excitatory and inhibitory connections, temporal evolution leads through repeated epochs of intensive global activity separated by intervals with low activity level. This behavior mimics “up” and “down” states, experimentally observed in cortical tissues in absence of external stimuli. We interpret global dynamical features in terms of individual dynamics of the neurons. In particular, we observe that the crucial role both in interruption and in resumption of global activity is played by distributions of the membrane recovery variable within the network. We also demonstrate that the behavior of neurons is more influenced by their presynaptic environment in the network than by their formal types, assigned in accordance with their response to constant current.

Assessing the sensitivity of diffusion MRI to detect neuronal activity directly

Functional MRI (fMRI) is widely used to study brain function in the neurosciences. Unfortunately, conventional fMRI only indirectly assesses neuronal activity via hemodynamic coupling. Diffusion fMRI was proposed as a more direct and accurate fMRI method to detect neuronal activity, yet confirmative findings have proven difficult to obtain. Given that the underlying relation between tissue water diffusion changes and neuronal activity remains unclear, the rationale for using diffusion MRI to monitor neuronal activity has yet to be clearly established. Here, we studied the correlation between water diffusion and neuronal activity in vitro by simultaneous calcium fluorescence imaging and diffusion MR acquisition. We used organotypic cortical cultures from rat brains as a biological model system, in which spontaneous neuronal activity robustly emerges free of hemodynamic and other artifacts. Simultaneous fluorescent calcium images of neuronal activity are then directly correlated with diffusion MR signals now free of confounds typically encountered in vivo. Although a simultaneous increase of diffusion-weighted MR signals was observed together with the prolonged depolarization of neurons induced by pharmacological manipulations (in which cell swelling was demonstrated to play an important role), no evidence was found that diffusion MR signals directly correlate with normal spontaneous neuronal activity. These results suggest that, whereas current diffusion MR methods could monitor pathological conditions such as hyperexcitability, e.g., those seen in epilepsy, they do not appear to be sensitive or specific enough to detect or follow normal neuronal activity.

Rich-Club Organization in Effective Connectivity among Cortical Neurons

The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 μm spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a "rich club." We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory. Significance statement: Many studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several hundred at a time) with such high temporal resolution (so we can know the direction of communication between neurons) for mapping networks within cortex. We found that information was not transferred equally through all neurons. Instead, ∼70% of the information passed through only 20% of the neurons. Network models suggest that this highly concentrated pattern of information transfer would be both efficient and robust to damage. Therefore, this work may help in understanding how the cortex processes information and responds to neurodegenerative diseases.

Selective Increase of Auditory Cortico-Striatal Coherence during Auditory-Cued Go/NoGo Discrimination Learning

Goal directed behavior and associated learning processes are tightly linked to neuronal activity in the ventral striatum. Mechanisms that integrate task relevant sensory information into striatal processing during decision making and learning are implicitly assumed in current reinforcement models, yet they are still weakly understood. To identify the functional activation of cortico-striatal subpopulations of connections during auditory discrimination learning, we trained Mongolian gerbils in a two-way active avoidance task in a shuttlebox to discriminate between falling and rising frequency modulated tones with identical spectral properties. We assessed functional coupling by analyzing the field-field coherence between the auditory cortex and the ventral striatum of animals performing the task. During the course of training, we observed a selective increase of functional coupling during Go-stimulus presentations. These results suggest that the auditory cortex functionally interacts with the ventral striatum during auditory learning and that the strengthening of these functional connections is selectively goal-directed.

Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses

Random networks of integrate-and-fire neurons with strong current-based synapses can, unlike previously believed, assume stable states of sustained asynchronous and irregular firing, even without external random background or pacemaker neurons. We analyze the mechanisms underlying the emergence, lifetime and irregularity of such self-sustained activity states. We first demonstrate how the competition between the mean and the variance of the synaptic input leads to a non-monotonic firing-rate transfer in the network. Thus, by increasing the synaptic coupling strength, the system can become bistable: In addition to the quiescent state, a second stable fixed-point at moderate firing rates can emerge by a saddle-node bifurcation. Inherently generated fluctuations of the population firing rate around this non-trivial fixed-point can trigger transitions into the quiescent state. Hence, the trade-off between the magnitude of the population-rate fluctuations and the size of the basin of attraction of the non-trivial rate fixed-point determines the onset and the lifetime of self-sustained activity states. During self-sustained activity, individual neuronal activity is moreover highly irregular, switching between long periods of low firing rate to short burst-like states. We show that this is an effect of the strong synaptic weights and the finite time constant of synaptic and neuronal integration, and can actually serve to stabilize the self-sustained state.

On the temporal organization of neuronal avalanches

Spontaneous activity of cortex in vitro and in vivo has been shown to organize as neuronal avalanches. Avalanches are cascades of neuronal activity that exhibit a power law in their size and duration distribution, typical features of balanced systems in a critical state. Recently it has been shown that the distribution of quiet times between consecutive avalanches in rat cortex slice cultures displays a non-monotonic behavior with a power law decay at short time scales. This behavior has been attributed to the slow alternation between up and down-states. Here we further characterize the avalanche process and investigate how the functional behavior of the quiet time distribution depends on the fine structure of avalanche sequences. By systematically removing smaller avalanches from the experimental time series we show that size and quiet times are correlated and highlight that avalanche occurrence exhibits the characteristic periodicity of θ and β/γ oscillations, which jointly emerge in most of the analyzed samples. Furthermore, our analysis indicates that smaller avalanches tend to be associated with faster β/γ oscillations, whereas larger ones are associated with slower θ and 1-2 Hz oscillations. In particular, large avalanches corresponding to θ cycles trigger cascades of smaller ones, which occur at β/γ frequency. This temporal structure follows closely the one of nested θ - β/γ oscillations. Finally we demonstrate that, because of the multiple time scales characterizing avalanche dynamics, the distributions of quiet times between avalanches larger than a certain size do not collapse onto a unique function when rescaled by the average occurrence rate. However, when considered separately in the up-state and in the down-state, these distributions are solely controlled by the respective average rate and two different unique function can be identified.

Organotypic slice co-culture systems to study axon regeneration in the dopaminergic system ex vivo

Organotypic slice co-cultures are suitable tools to study axonal regeneration and development (growth or regrowth) of different projection systems of the CNS under ex vivo conditions.In this chapter, we describe in detail the reconstruction of the mesocortical and nigrostriatal dopaminergic projection system culturing tissue slices from the ventral tegmental area/substantia nigra (VTA/SN) with the prefrontal cortex (PFC) or the striatum (STR). The protocol includes the detailed slice preparation and incubation. Moreover, different application possibilities of the ex vivo model are mentioned; as an example, the substance treatment procedure and biocytin tracing are described to reveal the effect of applied substances on fiber outgrowth.

Modeling nigrostriatal degeneration in organotypic cultures, a new ex vivo model of Parkinson's disease

Parkinson's disease (PD) is the second most frequent neurodegenerative disorder afflicting 2% of the population older than 65 years worldwide. Recently, brain organotypic slices have been used to model neurodegenerative disorders, including PD. They conserve brain three-dimensional architecture, synaptic connectivity and its microenvironment. This model has allowed researchers a simple and rapid method to observe cellular interactions and mechanisms. In the present study, we developed an organotypic PD model from rat brains that includes all the areas involved in the nigrostriatal pathway in a single slice preparation, without using neurotoxins to induce the dopaminergic lesion. The mechanical transection of the nigrostriatal pathway obtained during slice preparation induced PD-like histopathology. Progressive nigrostriatal degeneration was monitored combining innovative approaches, such as diffusion tensor magnetic resonance imaging (DT-RMI) to follow fiber degeneration and mass spectrometry to quantify striatal dopamine content, together with bright-field and fluorescence microscopy imaging. A substantia nigra dopaminergic cell number decrease was observed by immunohistochemistry against rat tyrosine hydroxylase (TH) reaching 80% after 2 days in culture associated with a 30% decrease of striatal TH-positive fiber density, a 15% loss of striatal dopamine content quantified by mass spectrometry and a 70% reduction of nigrostriatal fiber fractional anisotropy quantified by DT-RMI. In addition, a significant decline of medium spiny neuron density was observed from days 7 to 16. These sagittal organotypic slices could be used to study the early stage of PD, namely dopaminergic degeneration, and the late stage of the pathology with dopaminergic and GABAergic neuron loss. This novel model might improve the understanding of PD and may represent a promising tool to refine the evaluation of new therapeutic approaches.

Balance between excitation and inhibition controls the temporal organization of neuronal avalanches

Neuronal avalanches, measured in vitro and in vivo, exhibit a robust critical behavior. Their temporal organization hides the presence of correlations. Here we present experimental measurements of the waiting time distribution between successive avalanches in the rat cortex in vitro. This exhibits a nonmonotonic behavior not usually found in other natural processes. Numerical simulations provide evidence that this behavior is a consequence of the alternation between states of high and low activity, named up and down states, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods, both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homeostatic mechanisms.

Multi-electrode array recordings of neuronal avalanches in organotypic cultures

The cortex is spontaneously active, even in the absence of any particular input or motor output. During development, this activity is important for the migration and differentiation of cortex cell types and the formation of neuronal connections¹. In the mature animal, ongoing activity reflects the past and the present state of an animal into which sensory stimuli are seamlessly integrated to compute future actions. Thus, a clear understanding of the organization of ongoing i.e. spontaneous activity is a prerequisite to understand cortex function.

Numerous recording techniques revealed that ongoing activity in cortex is comprised of many neurons whose individual activities transiently sum to larger events that can be detected in the local field potential (LFP) with extracellular microelectrodes, or in the electroencephalogram (EEG), the magnetoencephalogram (MEG), and the BOLD signal from functional magnetic resonance imaging (fMRI). The LFP is currently the method of choice when studying neuronal population activity with high temporal and spatial resolution at the mesoscopic scale (several thousands of neurons). At the extracellular microelectrode, locally synchronized activities of spatially neighbored neurons result in rapid deflections in the LFP up to several hundreds of microvolts. When using an array of microelectrodes, the organizations of such deflections can be conveniently monitored in space and time.

Neuronal avalanches describe the scale-invariant spatiotemporal organization of ongoing neuronal activity in the brain^2,3. They are specific to the superficial layers of cortex as established in vitro^4,5, in vivo in the anesthetized rat ⁶, and in the awake monkey⁷. Importantly, both theoretical and empirical studies^2,8-10 suggest that neuronal avalanches indicate an exquisitely balanced critical state dynamics of cortex that optimizes information transfer and information processing.

In order to study the mechanisms of neuronal avalanche development, maintenance, and regulation, in vitro preparations are highly beneficial, as they allow for stable recordings of avalanche activity under precisely controlled conditions. The current protocol describes how to study neuronal avalanches in vitro by taking advantage of superficial layer development in organotypic cortex cultures, i.e. slice cultures, grown on planar, integrated microelectrode arrays (MEA; see also ^11-14).

Functional integration of grafted neural stem cell-derived dopaminergic neurons monitored by optogenetics in an in vitro Parkinson model

Intrastriatal grafts of stem cell-derived dopamine (DA) neurons induce behavioral recovery in animal models of Parkinson's disease (PD), but how they functionally integrate in host neural circuitries is poorly understood. Here, Wnt5a-overexpressing neural stem cells derived from embryonic ventral mesencephalon of tyrosine hydroxylase-GFP transgenic mice were expanded as neurospheres and transplanted into organotypic cultures of wild type mouse striatum. Differentiated GFP-labeled DA neurons in the grafts exhibited mature neuronal properties, including spontaneous firing of action potentials, presence of post-synaptic currents, and functional expression of DA D₂ autoreceptors. These properties resembled those recorded from identical cells in acute slices of intrastriatal grafts in the 6-hydroxy-DA-induced mouse PD model and from DA neurons in intact substantia nigra. Optogenetic activation or inhibition of grafted cells and host neurons using channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR), respectively, revealed complex, bi-directional synaptic interactions between grafted cells and host neurons and extensive synaptic connectivity within the graft. Our data demonstrate for the first time using optogenetics that ectopically grafted stem cell-derived DA neurons become functionally integrated in the DA-denervated striatum. Further optogenetic dissection of the synaptic wiring between grafted and host neurons will be crucial to clarify the cellular and synaptic mechanisms underlying behavioral recovery as well as adverse effects following stem cell-based DA cell replacement strategies in PD.

Fractals in the nervous system: conceptual implications for theoretical neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power-law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review.

Sequentially switching cell assemblies in random inhibitory networks of spiking neurons in the striatum

The striatum is composed of GABAergic medium spiny neurons with inhibitory collaterals forming a sparse random asymmetric network and receiving an excitatory glutamatergic cortical projection. Because the inhibitory collaterals are sparse and weak, their role in striatal network dynamics is puzzling. However, here we show by simulation of a striatal inhibitory network model composed of spiking neurons that cells form assemblies that fire in sequential coherent episodes and display complex identity-temporal spiking patterns even when cortical excitation is simply constant or fluctuating noisily. Strongly correlated large-scale firing rate fluctuations on slow behaviorally relevant timescales of hundreds of milliseconds are shown by members of the same assembly whereas members of different assemblies show strong negative correlation, and we show how randomly connected spiking networks can generate this activity. Cells display highly irregular spiking with high coefficients of variation, broadly distributed low firing rates, and interspike interval distributions that are consistent with exponentially tailed power laws. Although firing rates vary coherently on slow timescales, precise spiking synchronization is absent in general. Our model only requires the minimal but striatally realistic assumptions of sparse to intermediate random connectivity, weak inhibitory synapses, and sufficient cortical excitation so that some cells are depolarized above the firing threshold during up states. Our results are in good qualitative agreement with experimental studies, consistent with recently determined striatal anatomy and physiology, and support a new view of endogenously generated metastable state switching dynamics of the striatal network underlying its information processing operations.

Dopamine modulates persistent synaptic activity and enhances the signal-to-noise ratio in the prefrontal cortex

Background

The importance of dopamine (DA) for prefrontal cortical (PFC) cognitive functions is widely recognized, but its mechanisms of action remain controversial. DA is thought to increase signal gain in active networks according to an inverted U dose-response curve, and these effects may depend on both tonic and phasic release of DA from midbrain ventral tegmental area (VTA) neurons.

Methodology/Principal Findings

We used patch-clamp recordings in organotypic co-cultures of the PFC, hippocampus and VTA to study DA modulation of spontaneous network activity in the form of Up-states and signals in the form of synchronous EPSP trains. These cultures possessed a tonic DA level and stimulation of the VTA evoked DA transients within the PFC. The addition of high (≥1 µM) concentrations of exogenous DA to the cultures reduced Up-states and diminished excitatory synaptic inputs (EPSPs) evoked during the Down-state. Increasing endogenous DA via bath application of cocaine also reduced Up-states. Lower concentrations of exogenous DA (0.1 µM) had no effect on the up-state itself, but they selectively increased the efficiency of a train of EPSPs to evoke spikes during the Up-state. When the background DA was eliminated by depleting DA with reserpine and alpha-methyl-p-tyrosine, or by preparing corticolimbic co-cultures without the VTA slice, Up-states could be enhanced by low concentrations (0.1–1 µM) of DA that had no effect in the VTA containing cultures. Finally, in spite of the concentration-dependent effects on Up-states, exogenous DA at all but the lowest concentrations increased intracellular current-pulse evoked firing in all cultures underlining the complexity of DA's effects in an active network.

Conclusions/Significance

Taken together, these data show concentration-dependent effects of DA on global PFC network activity and they demonstrate a mechanism through which optimal levels of DA can modulate signal gain to support cognitive functioning.

Dopamine-deprived striatal GABAergic interneurons burst and generate repetitive gigantic IPSCs in medium spiny neurons

Striatal GABAergic microcircuits modulate cortical responses and movement execution in part by controlling the activity of medium spiny neurons (MSNs). How this is altered by chronic dopamine depletion, such as in Parkinson's disease, is not presently understood. We now report that, in dopamine-depleted slices of the striatum, MSNs generate giant spontaneous postsynaptic GABAergic currents (single or in bursts at 60 Hz) interspersed with silent episodes, rather than the continuous, low-frequency GABAergic drive (5 Hz) observed in control MSNs. This shift was observed in one-half of the MSN population, including both "D(1)-negative" and "D(1)-positive" MSNs. Single GABA and NMDA channel recordings revealed that the resting membrane potential and reversal potential of GABA were similar in control and dopamine-depleted MSNs, and depolarizing, but not excitatory, actions of GABA were observed. Glutamatergic and cholinergic antagonists did not block the GABAergic oscillations, suggesting that they were generated by GABAergic neurons. In support of this, cell-attached recordings revealed that a subpopulation of intrastriatal GABAergic interneurons generated bursts of spikes in dopamine-deprived conditions. This subpopulation included low-threshold spike interneurons but not fast-spiking interneurons, cholinergic interneurons, or MSNs. Therefore, a population of local GABAergic interneurons shifts from tonic to oscillatory mode when dopamine deprived and gives rise to spontaneous repetitive giant GABAergic currents in one-half the MSNs. We suggest that this may in turn alter integration of cortical signals by MSNs.

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.

The high-conductance state of cortical networks

We studied the dynamics of large networks of spiking neurons with conductance-based (nonlinear) synapses and compared them to networks with current-based (linear) synapses. For systems with sparse and inhibition-dominated recurrent connectivity, weak external inputs induced asynchronous irregular firing at low rates. Membrane potentials fluctuated a few millivolts below threshold, and membrane conductances were increased by a factor 2 to 5 with respect to the resting state. This combination of parameters characterizes the ongoing spiking activity typically recorded in the cortex in vivo. Many aspects of the asynchronous irregular state in conductance-based networks could be sufficiently well characterized with a simple numerical mean field approach. In particular, it correctly predicted an intriguing property of conductance-based networks that does not appear to be shared by current-based models: they exhibit states of low-rate asynchronous irregular activity that persist for some period of time even in the absence of external inputs and without cortical pacemakers. Simulations of larger networks (up to 350,000 neurons) demonstrated that the survival time of self-sustained activity increases exponentially with network size.

Encoding Network States by Striatal Cell Assemblies

Correlated activity in cortico-basal ganglia circuits plays a key role in the encoding of movement, associative learning and procedural memory. How correlated activity is assembled by striatal microcircuits is not understood. Calcium imaging of striatal neuronal populations, with single-cell resolution, reveals sporadic and asynchronous activity under control conditions. However, N-methyl-d-aspartate (NMDA) application induces bistability and correlated activity in striatal neurons. Widespread neurons within the field of observation present burst firing. Sets of neurons exhibit episodes of recurrent and synchronized bursting. Dimensionality reduction of network dynamics reveals functional states defined by cell assemblies that alternate their activity and display spatiotemporal pattern generation. Recurrent synchronous activity travels from one cell assembly to the other often returning to the original assembly; suggesting a robust structure. An initial search into the factors that sustain correlated activity of neuronal assemblies showed a critical dependence on both intrinsic and synaptic mechanisms: blockage of fast glutamatergic transmission annihilates all correlated firing, whereas blockage of GABAergic transmission locked the network into a single dominant state that eliminates assembly diversity. Reduction of L-type Ca2+-current restrains synchronization. Each cell assembly comprised different cells, but a small set of neurons was shared by different assemblies. A great proportion of the shared neurons was local interneurons with pacemaking properties. The network dynamics set into action by NMDA in the striatal network may reveal important properties of striatal microcircuits under normal and pathological conditions.

Homeostasis of neuronal avalanches during postnatal cortex development in vitro

Cortical networks in vivo and in vitro are spontaneously active in the absence of inputs, generating highly variable bursts of neuronal activity separated by up to seconds of quiescence. Previous measurements in adult rat cortex revealed an intriguing underlying organization of these dynamics, termed neuronal avalanches, which is indicative of a critical network state. Here we demonstrate that neuronal avalanches persist throughout development in cortical slice cultures from newborn rats. More specifically, we find that in spite of large variations of average rate in activity, spontaneous bursts occur with power-law distributed sizes (exponent -1.5) and a critical branching parameter close to 1. Our findings suggest that cortical networks homeostatically regulate a critical state during postnatal maturation.

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.

Gating information by two-state membrane potential fluctuations

Two-state voltage fluctuations between a hyperpolarized down-state and a depolarized up-state have been observed experimentally in a wide variety of neurons across brain regions. Using a biophysical model, we show that synaptic input by NMDA receptors can cause such membrane potential fluctuations. In this model, when a neuron is driven by two input pathways with different AMPA/NMDA receptor content, the NMDA-rich input causes up-state transitions, whereas the AMPA-rich input generates spikes only in the up-state. Therefore the NMDA-rich pathway can gate input from an AMPA pathway in an all-or-none fashion by switching between different membrane potential states. Furthermore, once in the up-state, the NMDA-rich pathway multiplicatively increases the gain of a neuron responding to AMPA-rich input. This proposed mechanism for two-state fluctuations directly suggests specific computations, such as gating and gain modulation based on the distinct receptor composition of different neuronal pathways. The dynamic gating of input by up- and down-states may be an elementary operation for the selective routing of signals in neural circuits, which may explain the ubiquity of two-state fluctuations across brain regions.

Travelling waves and EEG patterns during epileptic seizure: analysis with an integrate-and-fire neural network

Epilepsy is characterized by paradoxical patterns of neural activity. They may cause different types of electroencephalogram (EEG), which dynamically change in shape and frequency content during the temporal evolution of seizure. It is generally assumed that these epileptic patterns may originate in a network of strongly interconnected neurons, when excitation dominates over inhibition. The aim of this work is to use a neural network composed of 50 x 50 integrate-and-fire neurons to analyse which parameter alterations, at the level of synapse topology, may induce network instability and epileptic-like discharges, and to study the corresponding spatio-temporal characteristics of electrical activity in the network. We assume that a small group of central neurons is stimulated by a depolarizing current (epileptic focus) and that neurons are connected via a Mexican-hat topology of synapses. A signal representative of cortical EEG (ECoG) is simulated by summing the membrane potential changes of all neurons. A sensitivity analysis on the parameters describing the synapse topology shows that an increase in the strength and in spatial extension of excitatory vs. inhibitory synapses may cause the occurrence of travelling waves, which propagate along the network. These propagating waves may cause EEG patterns with different shape and frequency, depending on the particular parameter set used during the simulations. The resulting model EEG signals include irregular rhythms with large amplitude and a wide frequency content, low-amplitude high-frequency rapid discharges, isolated or repeated bursts, and low-frequency quasi-sinusoidal patterns. A slow progressive temporal variation in a single parameter may cause the transition from one pattern to another, thus generating a highly non-stationary signal which resembles that observed during ECoG measurements. These results may help to elucidate the mechanisms at the basis of some epileptic discharges, and to relate rapid changes in EEG patterns with the underlying alterations at the network level.

Beyond bistability: Biophysics and temporal dynamics of working memory

Working memory has often been modeled and conceptualized as a kind of binary (bistable) memory switch, where stimuli turn on plateau-like persistent activity in subsets of cells, in line with many in vivo electrophysiological reports. A potentially related form of bistability, termed up- and down-states, has been studied with regard to its synaptic and ionic basis in vivo and in reduced cortical preparations. Also single cell mechanisms for producing bistability have been proposed and investigated in brain slices and computationally. Recently, however, it has been emphasized that clear plateau-like bistable activity is rather rare during working memory tasks, and that neurons exhibit a multitude of different temporally unfolding activity profiles and temporal structure within their spiking dynamics. Hence, working memory seems to be a highly dynamical neural process with yet unknown mappings from dynamical to computational properties. Empirical findings on ramping activity profiles and temporal structure will be reviewed, as well as neural models that attempt to account for it and its computational significance. Furthermore, recent in vivo, neural culture, and in vitro preparations will be discussed that offer new possibilities for studying the biophysical mechanisms underlying computational processes during working memory. These preparations have revealed additional evidence for temporal structure and spatio-temporally organized attractor states in cortical networks, as well as for specific computational properties that may characterize synaptic processing during high-activity states as during working memory. Together such findings may lay the foundations for highly dynamical theories of working memory based on biophysical principles.

A new probe to monitor the effects of drugs on local field potentials

We describe a new device with which microdialysis can be combined with bipolar local field potential (LFP) recordings. The device consists of a microdialysis probe with two ultra-thin electrical wires located within its tip. The device can be used to monitor LFPs with differential recording methods before, during, and after local application of drugs by reverse microdialysis, while concomitantly sampling biochemical constituents from the area of recording.

Computational and in vitro studies of persistent activity: edging towards cellular and synaptic mechanisms of working memory

Persistent neural activity selective to features of an extinct stimulus has been identified as the neural correlate of working memory processes. The precise nature of the physiological substrate for this self-sustained activity is still unknown. In the last few years, this problem has gathered experimental together with computational neuroscientists in a quest to identify the cellular and network mechanisms involved. I introduce here the attractor theory framework within which current persistent activity computational models are built, and I then review the main physiological mechanisms that have been linked thereby to persistent activity and working memory. Open computational and physiological issues with these models are discussed, together with their potential experimental validation in current in vitro models of persistent activity.

Using potassium currents to solve signal-to-noise problems in inhibitory feedforward networks of the striatum

Fast-spiking (FS) interneurons provide the main route of feedforward inhibition from cortex to spiny projection neurons in the striatum. A steep current-firing frequency curve and a dense local axonal arbor suggest that even small excitatory inputs could translate into powerful feedforward inhibition, although such an arrangement is also sensitive to amplification of spurious synaptic inputs. We show that a transient potassium (KA) current allows the FS interneuron to strike a balance between sensitivity to correlated input and robustness to noise, thereby increasing its signal-to-noise ratio (SNR). First, a compartmental FS neuron model was created to match experimental data from striatal FS interneurons in cortex-striatum-substantia nigra organotypic cultures. Densities of sodium, delayed rectifier, and KA channels were optimized to replicate responses to somatic current injection. Spontaneous alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and gamma-aminobutyric acid (GABA) synaptic currents were adjusted to the experimentally measured amplitude, rise time, and interevent interval histograms. Second, two additional adjustments were required to emulate the remaining experimental observations. GABA channels were localized closer to the soma than AMPA channels to match the synaptic population reversal potential. Correlation among inputs was required to produce the observed firing rate during up-states. In this final model, KA channels were essential for suppressing down-state spikes while allowing reliable spike generation during up-states. This mechanism was particularly important under conditions of high dopamine. Our results suggest that KA channels allow FS interneurons to operate without a decrease in SNR during conditions of increased dopamine, as occurs in response to reward or anticipated reward.

The principal features and mechanisms of dopamine modulation in the prefrontal cortex

Mesocortical [corrected] dopamine (DA) inputs to the prefrontal cortex (PFC) play a critical role in normal cognitive process and neuropsychiatic pathologies. This DA input regulates aspects of working memory function, planning and attention, and its dysfunctions may underlie positive and negative symptoms and cognitive deficits associated with schizophrenia. Despite intense research, there is still a lack of clear understanding of the basic principles of actions of DA in the PFC. In recent years, there has been considerable efforts by many groups to understand the cellular mechanisms of DA modulation of PFC neurons. However, the results of these efforts often lead to contradictions and controversies. One principal feature of DA that is agreed by most researchers is that DA is a neuromodulator and is clearly not an excitatory or inhibitory neurotransmitter. The present article aims to identify certain principles of DA mechanisms by drawing on published, as well as unpublished data from PFC and other CNS sites to shed light on aspects of DA neuromodulation and address some of the existing controversies. Eighteen key features about DA modulation have been identified. These points directly impact on the end result of DA neuromodulation, and in some cases explain why DA does not yield identical effects under all experimental conditions. It will become apparent that DA's actions in PFC are subtle and depend on a variety of factors that can no longer be ignored. Some of these key factors include distinct bell-shaped dose-response profiles of postsynaptic DA effects, different postsynaptic responses that are contingent on the duration of DA receptor stimulation, prolonged duration effects, bidirectional effects following activation of D1 and D2 classes of receptors and membrane potential state and history dependence of subsequent DA actions. It is hoped that these factors will be borne in mind in future research and as a result a more consistent picture of DA neuromodulation in the PFC will emerge. Based on these factors, a theory is proposed for DA's action in PFC. This theory suggests that DA acts to expand or contract the breadth of information held in working memory buffers in PFC networks.