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A single intra-peritoneal injection of valproic acid (VPA) on embryonic day (ED) 11.5 to pregnant rats has been shown to produce severe autistic-like symptoms in the offspring. Previous studies showed that the microcircuitry is hyperreactive due to hyperconnectivity of glutamatergic synapses and hyperplastic due to over-expression of NMDA receptors. These changes were restricted to the dimensions of a minicolumn (<50 μm). In the present study, we explored whether Long Term Microcircuit Plasticity (LTMP) was altered in this animal model. We performed multi-neuron patch-clamp recordings on clusters of layer 5 pyramidal cells in somatosensory cortex brain slices (PN 12-15), mapped the connectivity and characterized the synaptic properties for connected neurons. Pipettes were then withdrawn and the slice was perfused with 100 μM sodium glutamate in artificial cerebrospinal fluid in the recording chamber for 12 h. When we re-patched the same cluster of neurons, we found enhanced LTMP only at inter-somatic distances beyond minicolumnar dimensions. These data suggest that hyperconnectivity is already near its peak within the dimensions of the minicolumn in the treated animals and that LTMP, which is normally restricted to within a minicolumn, spills over to drive hyperconnectivity across the dimensions of a minicolumn. This study provides further evidence to support the notion that the neocortex is highly plastic in response to new experiences in this animal model of autism.

As neuronal simulations approach larger scales with increasing levels of detail, the neurosimulator software represents only a part of a chain of tools ranging from setup, simulation, interaction with virtual environments to analysis and visualizations. Previously published approaches to abstracting simulator engines have not received wide-spread acceptance, which in part may be to the fact that they tried to address the challenge of solving the model specification problem. Here, we present an approach that uses a neurosimulator, in this case NEURON, to describe and instantiate the network model in the simulator's native model language but then replaces the main integration loop with its own. Existing parallel network models are easily adopted to run in the presented framework. The presented approach is thus an extension to NEURON but uses a component-based architecture to allow for replaceable spike exchange components and pluggable components for monitoring, analysis, or control that can run in this framework alongside with the simulation.

Carbon nanotubes have been applied in several areas of nerve tissue engineering to probe and augment cell behaviour, to label and track subcellular components, and to study the growth and organization of neural networks. Recent reports show that nanotubes can sustain and promote neuronal electrical activity in networks of cultured cells, but the ways in which they affect cellular function are still poorly understood. Here, we show, using single-cell electrophysiology techniques, electron microscopy analysis and theoretical modelling, that nanotubes improve the responsiveness of neurons by forming tight contacts with the cell membranes that might favour electrical shortcuts between the proximal and distal compartments of the neuron. We propose the 'electrotonic hypothesis' to explain the physical interactions between the cell and nanotube, and the mechanisms of how carbon nanotubes might affect the collective electrical activity of cultured neuronal networks. These considerations offer a perspective that would allow us to predict or engineer interactions between neurons and carbon nanotubes.

The design of novel bidirectional interfaces for in vivo and in vitro nervous systems is an important step towards future functional neuroprosthetics. Small electrodes, structures and devices are necessary to achieve high-resolution and target-selectivity during stimulation and recording of neuronal networks, while significant charge transfer and large signal-to-noise ratio are required for accurate time resolution. In addition, the physical properties of the interface should remain stable across time, especially when chronic in vivo applications or in vitro long-term studies are considered, unless a procedure to actively compensate for degradation is provided. In this short report, we describe the use and fabrication of arrays of 120 planar microelectrodes (MEAs) of sputtered Iridium Oxide (IrOx). The effective surface area of individual microelectrodes is significantly increased using electrochemical activation, a procedure that may also be employed to restore the properties of the electrodes as required. The electrode activation results in a very low interface impedance, especially in the lower frequency domain, which was characterized by impedance spectroscopy. The increase in the roughness of the microelectrodes surface was imaged using digital holographic microscopy and electron microscopy. Aging of the activated electrodes was also investigated, comparing storage in saline with storage in air. Demonstration of concept was achieved by recording multiple single-unit spike activity in acute brain slice preparations of rat neocortex. Data suggests that extracellular recording of action potentials can be achieved with planar IrOx MEAs with good signal-to-noise ratios.

When a multi-compartment neuron is divided into subtrees such that no subtree has more than two connection points to other subtrees, the subtrees can be on different processors and the entire system remains amenable to direct Gaussian elimination with only a modest increase in complexity. Accuracy is the same as with standard Gaussian elimination on a single processor. It is often feasible to divide a 3-D reconstructed neuron model onto a dozen or so processors and experience almost linear speedup. We have also used the method for purposes of load balance in network simulations when some cells are so large that their individual computation time is much longer than the average processor computation time or when there are many more processors than cells. The method is available in the standard distribution of the NEURON simulation program.

We review here the development of Hodgkin-Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are "fast spiking", "regular spiking", "intrinsically bursting" and "low-threshold spike" cells. For each class, we fit "minimal" HH type models to experimental data. The models contain the minimal set of voltage-dependent currents to account for the data. To obtain models as generic as possible, we used data from different preparations in vivo and in vitro, such as rat somatosensory cortex and thalamus, guinea-pig visual and frontal cortex, ferret visual cortex, cat visual cortex and cat association cortex. For two cell classes, we used automatic fitting procedures applied to several cells, which revealed substantial cell-to-cell variability within each class. The selection of such cellular models constitutes a necessary step towards building network simulations of the thalamocortical system with realistic cellular dynamical properties.

Neuron models, in particular conductance-based compartmental models, often have numerous parameters that cannot be directly determined experimentally and must be constrained by an optimization procedure. A common practice in evaluating the utility of such procedures is using a previously developed model to generate surrogate data (e.g., traces of spikes following step current pulses) and then challenging the algorithm to recover the original parameters (e.g., the value of maximal ion channel conductances) that were used to generate the data. In this fashion, the success or failure of the model fitting procedure to find the original parameters can be easily determined. Here we show that some model fitting procedures that provide an excellent fit in the case of such model-to-model comparisons provide ill-balanced results when applied to experimental data. The main reason is that surrogate and experimental data test different aspects of the algorithm's function. When considering model-generated surrogate data, the algorithm is required to locate a perfect solution that is known to exist. In contrast, when considering experimental target data, there is no guarantee that a perfect solution is part of the search space. In this case, the optimization procedure must rank all imperfect approximations and ultimately select the best approximation. This aspect is not tested at all when considering surrogate data since at least one perfect solution is known to exist (the original parameters) making all approximations unnecessary. Furthermore, we demonstrate that distance functions based on extracting a set of features from the target data (such as time-to-first-spike, spike width, spike frequency, etc.)--rather than using the original data (e.g., the whole spike trace) as the target for fitting-are capable of finding imperfect solutions that are good approximations of the experimental data.

The prefrontal cortex has been extensively implicated in autism to explain deficits in executive and other higher-order functions related to cognition, language, sociability and emotion. The possible changes at the level of the neuronal microcircuit are however not known. We studied microcircuit alterations in the prefrontal cortex in the valproic acid rat model of autism and found that the layer 5 pyramidal neurons are connected to significantly more neighbouring neurons than in controls. These excitatory connections are more plastic displaying enhanced long-term potentiation of the strength of synapses. The microcircuit alterations found in the prefrontal cortex are therefore similar to the alterations previously found in the somatosensory cortex. Hyper-connectivity and hyper-plasticity in the prefrontal cortex implies hyper-functionality of one of the highest order processing regions in the brain, and stands in contrast to the hypo-functionality that is normally proposed in this region to explain some of the autistic symptoms. We propose that a number of deficits in autism such as sociability, attention, multi-tasking and repetitive behaviours, should be re-interpreted in the light of a hyper-functional prefrontal cortex.

The synchronous oscillatory activity characterizing many neurons in a network is often considered to be a mechanism for representing, binding, conveying, and organizing information. A number of models have been proposed to explain high-frequency oscillations, but the mechanisms that underlie slow oscillations are still unclear. Here, we show by means of analytical solutions and simulations that facilitating excitatory (E(f)) synapses onto interneurons in a neural network play a fundamental role, not only in shaping the frequency of slow oscillations, but also in determining the form of the up and down states observed in electrophysiological measurements. Short time constants and strong E(f) synapse-connectivity were found to induce rapid alternations between up and down states, whereas long time constants and weak E(f) synapse connectivity prolonged the time between up states and increased the up state duration. These results suggest a novel role for facilitating excitatory synapses onto interneurons in controlling the form and frequency of slow oscillations in neuronal circuits.

Neuroscience produces a vast amount of data from an enormous diversity of neurons. A neuronal classification system is essential to organize such data and the knowledge that is derived from them. Classification depends on the unequivocal identification of the features that distinguish one type of neuron from another. The problems inherent in this are particularly acute when studying cortical interneurons. To tackle this, we convened a representative group of researchers to agree on a set of terms to describe the anatomical, physiological and molecular features of GABAergic interneurons of the cerebral cortex. The resulting terminology might provide a stepping stone towards a future classification of these complex and heterogeneous cells. Consistent adoption will be important for the success of such an initiative, and we also encourage the active involvement of the broader scientific community in the dynamic evolution of this project.

Electrical synapses continuously transfer signals bi-directionally from one cell to another, directly or indirectly via intermediate cells. Electrical synapses are common in many brain structures such as the inferior olive, the subcoeruleus nucleus and the neocortex, between neurons and between glial cells. In the cortex, interneurons have been shown to be electrically coupled and proposed to participate in large, continuous cortical syncytia, as opposed to smaller spatial domains of electrically coupled cells. However, to explore the significance of these findings it is imperative to map the electrical synaptic microcircuits, in analogy with in vitro studies on monosynaptic and disynaptic chemical coupling. Since "walking" from cell to cell over large distances with a glass pipette is challenging, microinjection of (fluorescent) dyes diffusing through gap-junctions remains so far the only method available to decipher such microcircuits even though technical limitations exist. Based on circuit theory, we derive analytical descriptions of the AC electrical coupling in networks of isopotential cells. We then suggest an operative electrophysiological protocol to distinguish between direct electrical connections and connections involving one or more intermediate cells. This method allows inferring the number of intermediate cells, generalizing the conventional coupling coefficient, which provides limited information. We validate our method through computer simulations, theoretical and numerical methods and electrophysiological paired recordings.

The quest to understand the way in which neurons interconnect to form circuits that function as a unit began when Ramon y Cajal concluded that axo-dendritic apposition were too conspicuous to be incidental and proposed that two neurons must be communicating through these points of contact (see Shepherd and Erulkar, 1997, Trends Neurosci., 20, 385-392). Lorente de Nó was probably the first to predict that a defined group of vertically displaced neurons in the neocortex could form functional units (Lorente de Nó, 1938, Physiology of the Nervous System, 20, OUP: 291-330) for which Mountcastle found experimental evidence (see Mountcastle, 1997, Brain, 120, 701-722) and which was ultimately demonstrated by Hubel and Wiesel in their elegant discovery of the orientation selective columns (Hubel and Wiesel, 1959, J. Physiol., 148, 574-591). Until today, however, it is still not clear what shapes functional columns. Anatomical units, as in the barrel cortex, would make it easier to explain, but the neocortex is largely a continuous slab of closely packed neurons from which multiple modules emerge that can overlap partially or even completely on the same anatomical space. Are the columns in fixed anatomical locations or are they dynamically assigned and what anatomical and physiological properties are operating to shape their dimensions? A recent study explores how the geometry of single neurons places structural constraints on the dimensions of columns in the visual cortex (Stepanyants et al., 2008, Cereb Cortex, 18, 13-24).

Exposure to valproic acid (VPA) during embryogenesis can cause several teratogenic effects, including developmental delays and in particular autism in humans if exposure occurs during the third week of gestation. We examined the postnatal effects of embryonic exposure to VPA on microcircuit properties of juvenile rat neocortex using in vitro electrophysiology. We found that a single prenatal injection of VPA on embryonic day 11.5 causes a significant enhancement of the local recurrent connectivity formed by neocortical pyramidal neurons. The study of the biophysical properties of these connections revealed weaker excitatory synaptic responses. A marked decrease of the intrinsic excitability of pyramidal neurons was also observed. Furthermore, we demonstrate a diminished number of putative synaptic contacts in connection between layer 5 pyramidal neurons. Local hyperconnectivity may render cortical modules more sensitive to stimulation and once activated, more autonomous, isolated, and more difficult to command. This could underlie some of the core symptoms observed in humans prenatally exposed to valproic acid.

A core feature of autism spectrum disorders is the impairment in social interactions. Among other brain regions, a deficit in amygdala processing has been suggested to underlie this impairment, but whether the amygdala is processing fear abnormally in autism, is yet not clear. We used the valproic acid (VPA) rat model of autism to (a) screen for autism-like symptoms in rats, (b) test for alterations in amygdala-dependent fear processing, and (c) evaluate neuronal reactivity and synaptic plasticity in the lateral amygdala by means of in vitro single-cell electrophysiological recordings. VPA-treated animals displayed several symptoms common to autism, among them impaired social interactions and increased repetitive behaviors. Furthermore, VPA-treated rats were more anxious and exhibited abnormally high and longer lasting fear memories, which were overgeneralized and harder to extinguish. On the cellular level, the amygdala was hyperreactive to electrical stimulation and displayed boosted synaptic plasticity as well as a deficit in inhibition. We show for the first time enhanced, overgeneralized and resistant conditioned fear memories in an animal model of autism. Such hyperfear could be caused by the hyperreactivity and hyperplasticity found in the lateral amygdala, which may in turn be due to a deficit in the inhibitory system of the amygdala. We hypothesize an 'aversive world' syndrome that could, even if not a primary cause of the disorder itself, underlie some core symptoms in autism, such as impairments in social interactions and resistance to rehabilitation.

We present a novel framework for automatically constraining parameters of compartmental models of neurons, given a large set of experimentally measured responses of these neurons. In experiments, intrinsic noise gives rise to a large variability (e.g., in firing pattern) in the voltage responses to repetitions of the exact same input. Thus, the common approach of fitting models by attempting to perfectly replicate, point by point, a single chosen trace out of the spectrum of variable responses does not seem to do justice to the data. In addition, finding a single error function that faithfully characterizes the distance between two spiking traces is not a trivial pursuit. To address these issues, one can adopt a multiple objective optimization approach that allows the use of several error functions jointly. When more than one error function is available, the comparison between experimental voltage traces and model response can be performed on the basis of individual features of interest (e.g., spike rate, spike width). Each feature can be compared between model and experimental mean, in units of its experimental variability, thereby incorporating into the fitting this variability. We demonstrate the success of this approach, when used in conjunction with genetic algorithm optimization, in generating an excellent fit between model behavior and the firing pattern of two distinct electrical classes of cortical interneurons, accommodating and fast-spiking. We argue that the multiple, diverse models generated by this method could serve as the building blocks for the realistic simulation of large neuronal networks.

Autism is a devastating neurodevelopmental disorder with a polygenetic predisposition that seems to be triggered by multiple environmental factors during embryonic and/or early postnatal life. While significant advances have been made in identifying the neuronal structures and cells affected, a unifying theory that could explain the manifold autistic symptoms has still not emerged. Based on recent synaptic, cellular, molecular, microcircuit, and behavioral results obtained with the valproic acid (VPA) rat model of autism, we propose here a unifying hypothesis where the core pathology of the autistic brain is hyper-reactivity and hyper-plasticity of local neuronal circuits. Such excessive neuronal processing in circumscribed circuits is suggested to lead to hyper-perception, hyper-attention, and hyper-memory, which may lie at the heart of most autistic symptoms. In this view, the autistic spectrum are disorders of hyper-functionality, which turns debilitating, as opposed to disorders of hypo-functionality, as is often assumed. We discuss how excessive neuronal processing may render the world painfully intense when the neocortex is affected and even aversive when the amygdala is affected, leading to social and environmental withdrawal. Excessive neuronal learning is also hypothesized to rapidly lock down the individual into a small repertoire of secure behavioral routines that are obsessively repeated. We further discuss the key autistic neuropathologies and several of the main theories of autism and re-interpret them in the light of the hypothesized Intense World Syndrome.

Neocortical pyramidal cells (PCs) project to various cortical and subcortical targets. In layer V, the population of thick tufted PCs (TTCs) projects to subcortical targets such as the tectum, brainstem, and spinal cord. Another population of layer V PCs projects via the corpus callosum to the contralateral neocortical hemisphere mediating information transfer between the hemispheres. This subpopulation (corticocallosally projecting cells [CCPs]) has been previously described in terms of their morphological properties, but less is known about their electrophysiological properties, and their synaptic connectivity is unknown. We studied the morphological, electrophysiological, and synaptic properties of CCPs by retrograde labeling with fluorescent microbeads in P13-P16 Wistar rats. CCPs were characterized by shorter, untufted apical dendrites, which reached only up to layers II/III, confirming previous reports. Synaptic connections between CCPs were different from those observed between TTCs, both in probability of occurrence and dynamic properties. We found that the CCP network is about 4 times less interconnected than the TTC network and the probability of release is 24% smaller, resulting in a more linear synaptic transmission. The study shows that layer V pyramidal neurons projecting to different targets form subnetworks with specialized connectivity profiles, in addition to the specialized morphological and electrophysiological intrinsic properties.

Valproic acid (VPA) is a powerful teratogen causing birth defects in humans, including autism spectrum disorder (ASD), if exposure occurs during the first trimester of embryogenesis. Learning and memory alterations are common symptoms of ASD, but underlying molecular and synaptic alterations remain unknown. We therefore studied plasticity-related mechanisms in the neocortex of 2-week-old rats prenatally exposed to VPA and tested for changes in glutamate-mediated transmission and plasticity in the neocortex. We found a selective overexpression of NR2A and NR2B subunits of NMDA receptors, as well as the commonly linked kinase calcium/calmodulin-dependent protein kinase II. Synaptic plasticity experiments between pairs of pyramidal neurons revealed an augmented postsynaptic form of long-term potentiation. These results indicate that VPA significantly enhances NMDA receptor-mediated transmission and causes increased plasticity in the neocortex. Enhanced plasticity introduces a surprising perspective to the potential molecular and synaptic mechanisms involved in children prenatally exposed to VPA.

Reliable activation of inhibitory pathways is essential for maintaining the balance between excitation and inhibition during cortical activity. Little is known, however, about the activation of these pathways at the level of the local neocortical microcircuit. We report a disynaptic inhibitory pathway among neocortical pyramidal cells (PCs). Inhibitory responses were evoked in layer 5 PCs following stimulation of individual neighboring PCs with trains of action potentials. The probability for inhibition between PCs was more than twice that of direct excitation, and inhibitory responses increased as a function of rate and duration of presynaptic discharge. Simultaneous somatic and dendritic recordings indicated that inhibition originated from PC apical and tuft dendrites. Multineuron whole-cell recordings from PCs and interneurons combined with morphological reconstructions revealed the mediating interneurons as Martinotti cells. Martinotti cells received facilitating synapses from PCs and formed reliable inhibitory synapses onto dendrites of neighboring PCs. We describe this feedback pathway and propose it as a central mechanism for regulation of cortical activity.

The patch-clamp technique has allowed detailed studies on the electrical properties of neurons. Dye loading through patch pipettes has allowed characterizing the morphological properties of the neurons. In addition, the patch-clamp technique also allows harvesting mRNA from single cells to study gene expression at the single-cell level (known as single-cell reverse transcription-polymerase chain reaction [RT-PCR] [1-3]). The combination of these three approaches allows determination of the Gene expression, Electrophysiology and Morphology (GEM) profile of neurons (gene expression, electrophysiology, and morphology) using a single patch pipette and patch-clamp recording. This combination provides a powerful technique to study and correlate the neuron's gene expression with its phenotype (electrical behavior and morphology) ( 4 - 7 ). The harvesting and amplification of single-cell mRNA for gene expression studies is a challenging task, especially for researchers with sparse or no training in molecular biology (see Notes 1 and 2). Here, we describe in detail the GEM profiling approach with special attention to the gene expression profiling.

The NEURON simulation environment has been extended to support parallel network simulations. Each processor integrates the equations for its subnet over an interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead is often less than the benefit of an increased fraction of the entire problem fitting into high speed cache. On the EPFL IBM Blue Gene, almost linear speedup was obtained up to 100 processors. Increasing one model from 500 to 40,000 realistic cells exhibited almost linear speedup on 2,000 processors, with an integration time of 9.8 seconds and communication time of 1.3 seconds. The potential for speed-ups of several orders of magnitude makes practical the running of large network simulations that could otherwise not be explored.

The local microcircuitry of the neocortex is structurally a tabula rasa, with the axon of each pyramidal neuron having numerous submicrometer appositions with the dendrites of all neighboring pyramidal neurons, but is functionally highly selective, with synapses formed onto only a small proportion of these targets. This design leaves a vast potential for the microcircuit to rewire without extensive axonal or dendritic growth. To examine whether rewiring does take place, we used multineuron patch-clamp recordings on 12- to 14-day-old rat neocortical slices and studied long-term changes in synaptic connectivity within clusters of neurons. We found pyramidal neurons spontaneously connecting and disconnecting from each other and that exciting the slice with glutamate greatly increases the number of new connections established. Evoked emergence of new synaptic connections requires action potential activity and activation of metabotropic glutamate receptor 5, but not NMDA receptor or group II or group III metabotropic glutamate receptor activation. We also found that it is the weaker connections that are selectively eliminated. These results provide direct evidence for spontaneous and evoked rewiring of the neocortical microcircuitry involving entire functional multisynaptic connections. We speculate that this form of microcircuit plasticity enables an evolution of the microcircuit connectivity by natural selection as a function of experience.

The prefrontal cortex is specially adapted to generate persistent activity that outlasts stimuli and is resistant to distractors, presumed to be the basis of working memory. The pyramidal network that supports this activity is unknown. Multineuron patch-clamp recordings in the ferret medial prefrontal cortex showed a heterogeneity of synapses interconnecting distinct subnetworks of different pyramidal cells. One subnetwork was similar to the pyramidal network commonly found in primary sensory areas, consisting of accommodating pyramidal cells interconnected with depressing synapses. The other subnetwork contained complex pyramidal cells with dual apical dendrites displaying nonaccommodating discharge patterns; these cells were hyper-reciprocally connected with facilitating synapses displaying pronounced synaptic augmentation and post-tetanic potentiation. These cellular, synaptic and network properties could amplify recurrent interactions between pyramidal neurons and support persistent activity in the prefrontal cortex.

IBM's Blue Gene supercomputer allows a quantum leap in the level of detail at which the brain can be modelled. I argue that the time is right to begin assimilating the wealth of data that has been accumulated over the past century and start building biologically accurate models of the brain from first principles to aid our understanding of brain function and dysfunction.

To understand the interface between global brain function and molecular neuroscience--that is, the microcircuit level--a major challenge. Such understanding is prerequisite if we are to account for neural function in cellular terms. Very few vertebrate microcircuits are yet understood because their analysis is demanding technically. In this review of the TINS Microcircuits Special Feature, we attempt to shed light on the problem by comparing the operation of four types of microcircuit, to identify common molecular and cellular components. Central pattern generator (CPG) networks underlying rhythmic movements and hippocampal microcircuits that generate gamma and theta rhythms are compared with the neocortical microcircuits used in cognitive tasks and a cerebellar network. The long-term goal is to identify the components of a molecular and synaptic tool kit for the design of different microcircuits.

The functions performed by different neural microcircuits depend on the anatomical and physiological properties of the various synaptic pathways connecting neurons. Neural microcircuits across various species and brain regions are similar in terms of their repertoire of neurotransmitters, their synaptic kinetics, their short-term and long-term plasticity, and the target-specificity of their synaptic connections. However, microcircuits can be fundamentally different in terms of the precise recurrent design used to achieve a specific functionality. In this review, which is part of the TINS Microcircuits Special Feature, we compare the connectivity designs in spinal, hippocampal, neocortical and cerebellar microcircuits, and discuss the different computational challenges that each microcircuit faces.

Neocortical neurones can be classified according to several independent criteria: morphological, physiological, and molecular expression (neuropeptides (NPs) and/or calcium-binding proteins (CaBPs)). While it has been suggested that particular NPs and CaBPs characterize certain anatomical subtypes of neurones, there is also considerable overlap in their expression, and little is known about simultaneous expression of multiple NPs and CaBPs in morphologically characterized neocortical neurones. Here we determined the gene expression profiles of calbindin (CB), parvalbumin (PV), calretinin (CR), neuropeptide Y (NPY), vasoactive intestinal peptide (VIP), somatostatin (SOM) and cholecystokinin (CCK) in 268 morphologically identified neurones located in layers 2-6 in the juvenile rat somatosensory neocortex. We used patch-clamp electrodes to label neurones with biocytin and harvest the cytoplasm to perform single-cell RT-multiplex PCR. Quality threshold clustering, an unsupervised algorithm that clustered neurones according to their entire profile of expressed genes, revealed seven distinct clusters. Surprisingly, each cluster preferentially contained one anatomical class. Artificial neural networks using softmax regression predicted anatomical types at nearly optimal statistical levels. Classification tree-splitting (CART), a simple binary neuropeptide decision tree algorithm, revealed the manner in which expression of the multiple mRNAs relates to different anatomical classes. Pruning the CART tree revealed the key predictors of anatomical class (in order of importance: SOM, PV, VIP, and NPY). We reveal here, for the first time, a strong relationship between specific combinations of NP and CaBP gene expressions and the anatomical class of neocortical neurones.

The synaptic drive from neuronal populations varies considerably over short time scales. Such changes in the pre-synaptic rate trigger many temporal processes absent under steady-state conditions. This paper examines the differential impact of pyramidal cell population bursts on post-synaptic pyramidal cells receiving depressing synapses, and on a class of interneuron that receives facilitating synapses. In experiment a significant shift of the order of one hundred milliseconds is seen between the response of these two cell classes to the same population burst. It is demonstrated here that such a temporal differentiation of the response can be explained by the synaptic and membrane properties without recourse to elaborate cortical wiring schemes. Experimental data is first used to construct models of the two types of dynamic synaptic response. A population-based approach is then followed to examine analytically the temporal synaptic filtering effects of the population burst for the two post-synaptic targets. The peak-to-peak delays seen in experiment can be captured by the model for experimentally realistic parameter ranges. It is further shown that the temporal separation of the response is communicated in the outgoing action potentials of the two post-synaptic cells: pyramidal cells fire at the beginning of the burst and the class of interneuron receiving facilitating synapses fires at the end of the burst. The functional role of such delays in the temporal organisation of activity in the cortical microcircuit is discussed.

The neocortex has a high capacity for plasticity. To understand the full scope of this capacity, it is essential to know how neurons choose particular partners to form synaptic connections. By using multineuron whole-cell recordings and confocal microscopy we found that axons of layer V neocortical pyramidal neurons do not preferentially project toward the dendrites of particular neighboring pyramidal neurons; instead, axons promiscuously touch all neighboring dendrites without any bias. Functional synaptic coupling of a small fraction of these neurons is, however, correlated with the existence of synaptic boutons at existing touch sites. These data provide the first direct experimental evidence for a tabula rasa-like structural matrix between neocortical pyramidal neurons and suggests that pre- and postsynaptic interactions shape the conversion between touches and synapses to form specific functional microcircuits. These data also indicate that the local neocortical microcircuit has the potential to be differently rewired without the need for remodeling axonal or dendritic arbors.

Mammals adapt to a rapidly changing world because of the sophisticated perceptual and cognitive function enabled by the neocortex. The neocortex, which has expanded to constitute nearly 80% of the human brain seems to have arisen from repeated duplication of a stereotypical template of neurons and synaptic circuits with subtle specializations in different brain regions and species. Determining the design and function of this microcircuitry is therefore of paramount importance to understanding normal and abnormal higher brain function. Recent advances in recording synaptically-coupled neurons has allowed rapid dissection of the neocortical microcircuitry thus yielding a massive amount of quantitative anatomical, electrical and gene expression data on the neurons and the synaptic circuits that connect the neurons. Due to the availability of the above mentioned data, it has now become imperative to database the neurons of the microcircuit and their synaptic connections. The NEOBASE project, aims to archive the neocortical microcircuit data in a manner that facilitates development of advanced data mining applications, statistical and bioinformatics analyses tools, custom microcircuit builders, and visualization and simulation applications. The database architecture is based on ROOT, a software environment that allows the construction of an object oriented database with numerous relational capabilities. The proposed architecture allows construction of a database that closely mimics the architecture of the real microcircuit, which facilitates the interface with virtually any application, allows for data format evolution, and aims for full interoperability with other databases. NEOBASE will provide an important resource and research tool for studying the microcircuit basis of normal and abnormal neocortical function. The database will be available to local as well as remote users using Grid based tools and technologies.

The computational power of the neocortex arises from interactions of multiple neurons, which display a wide range of electrical properties. The gene expression profiles underlying this phenotypic diversity are unknown. To explore this relationship, we combined whole-cell electrical recordings with single-cell multiplex RT-PCR of rat (p13-16) neocortical neurons to obtain cDNA libraries of 26 ion channels (including voltage activated potassium channels, Kv1.1/2/4/6, Kvbeta1/2, Kv2.1/2, Kv3.1/2/3/4, Kv4.2/3; sodium/potassium permeable hyperpolarization activated channels, HCN1/2/3/4; the calcium activated potassium channel, SK2; voltage activated calcium channels, Caalpha1A/B/G/I, Cabeta1/3/4), three calcium binding proteins (calbindin, parvalbumin and calretinin) and GAPDH. We found a previously unreported clustering of ion channel genes around the three calcium-binding proteins. We further determined that cells similar in their expression patterns were also similar in their electrical properties. Subsequent regression modeling with statistical resampling yielded a set of coefficients that reliably predicted electrical properties from the expression profile of individual neurons. This is the first report of a consistent relationship between the co-expression of a large profile of ion channel and calcium binding protein genes and the electrical phenotype of individual neocortical neurons.

Whole-cell patch-clamp recordings followed by histochemical staining and single-cell RT-PCR were obtained from 180 Martinotti interneurones located in layers II to VI of the somatosensory cortex of Wistar rats (P13-P16) in order to examine their anatomical, electrophysiological and molecular properties. Martinotti cells (MCs) mostly displayed ovoid-shaped somata, bitufted dendritic morphologies, and axons with characteristic spiny boutons projecting to layer I and spreading horizontally across neighbouring columns more than 1 mm. Electron microscopic examination of MC boutons revealed that all synapses were symmetrical and most synapses (71%) were formed onto dendritic shafts. MCs were found to contact tuft, apical and basal dendrites in multiple neocortical layers: layer II/III MCs targeted mostly layer I and to a lesser degree layer II/III; layer IV MCs targeted mostly layer IV and to a lesser degree layer I; layer V and VI MCs targeted mostly layer IV and layer I and to a lesser degree the layer in which their somata was located. MCs typically displayed spike train accommodation (90%; n = 127) in response to depolarizing somatic current injections, but some displayed non-accommodating (8%) and a few displayed irregular spiking responses (2%). Some accommodating and irregular spiking MCs also responded initially with bursts (17%). Accommodating responses were found in all layers, non-accommodating mostly in upper layers and bursting mostly in layer V. Single-cell multiplex RT-PCR performed on 63 MCs located throughout layers II-VI, revealed that all MCs were somatostatin (SOM) positive, and negative for parvalbumin (PV) as well as vasoactive intestinal peptide (VIP). Calbindin (CB), calretinin (CR), neuropeptide Y (NPY) and cholecystokinin (CCK) were co- expressed with SOM in some MCs. Some layer-specific trends seem to exist. Finally, 24 accommodating MCs were examined for the expression of 26 ion channel genes. The ion channels with the highest expression in these MCs were (from highest to lowest); Cabeta1, Kv3.3, HCN4, Cabeta4, Kv3.2, Kv3.1, Kv2.1, HCN3, Caalpha1G, Kv3.4, Kv4.2, Kv1.1 and HCN2. In summary, this study provides the first detailed analysis of the anatomical, electrophysiological and molecular properties of Martinotti cells located in different neocortical layers. It is proposed that MCs are crucial interneurones for feedback inhibition in and between neocortical layers and columns.

Mammals adapt to a rapidly changing world because of the sophisticated cognitive functions that are supported by the neocortex. The neocortex, which forms almost 80% of the human brain, seems to have arisen from repeated duplication of a stereotypical microcircuit template with subtle specializations for different brain regions and species. The quest to unravel the blueprint of this template started more than a century ago and has revealed an immensely intricate design. The largest obstacle is the daunting variety of inhibitory interneurons that are found in the circuit. This review focuses on the organizing principles that govern the diversity of inhibitory interneurons and their circuits.

It is quite difficult to construct circuits of spiking neurons that can carry out complex computational tasks. On the other hand even randomly connected circuits of spiking neurons can in principle be used for complex computational tasks such as time-warp invariant speech recognition. This is possible because such circuits have an inherent tendency to integrate incoming information in such a way that simple linear readouts can be trained to transform the current circuit activity into the target output for a very large number of computational tasks. Consequently we propose to analyze circuits of spiking neurons in terms of their roles as analog fading memory and non-linear kernels, rather than as implementations of specific computational operations and algorithms. This article is a sequel to [W. Maass, T. Natschläger, H. Markram, Real-time computing without stable states: a new framework for neural computation based on perturbations, Neural Comput. 14 (11) (2002) 2531-2560, Online available as #130 from: ], and contains new results about the performance of generic neural microcircuit models for the recognition of speech that is subject to linear and non-linear time-warps, as well as for computations on time-varying firing rates. These computations rely, apart from general properties of generic neural microcircuit models, just on capabilities of simple linear readouts trained by linear regression. This article also provides detailed data on the fading memory property of generic neural microcircuit models, and a quick review of other new results on the computational power of such circuits of spiking neurons.

It is well established that sensory stimulation results in the activity of multiple functional columns in the neocortex. The manner in which neurones within each column are active in relation to each other is, however, not known. Multiple whole-cell recordings in activated neocortical slices from rat revealed diverse correlation profiles of excitatory synaptic input to different types of neurones. The specific correlation profile between any two neurones could be predicted by the settings of synaptic depression and facilitation at the input synapses. Simulations further showed that patterned activity is essential for synaptic dynamics to impose the temporal dispersion of excitatory input. We propose that synaptic dynamics choreograph neuronal activity within the neocortical microcircuit in a context-dependent manner.

Structural and functional diversity of GABAergic interneurons has become increasingly central in our understanding of the elemental steps of information processing in the brain. The use of different molecular, electrophysiological and anatomical techniques has provided a wealth of new information regarding GABAergic interneurons over the past decade but it has also led to confusion regarding the number of subtypes of GABAergic interneurons. Combinatorial approaches that also consider multiple parameters seem now to offer renewed hope for finally clarifying the structural diversity of GABAergic interneurons. New molecular techniques have become a powerful tool for exposing the functional diversity of GABAergic neurons at the cellular, microcircuit and systems levels. This article reviews literature regarding molecular tools that have been used, or that appear promising for future attempts, to classify GABAergic interneurons. Some important limitations will also be indicated.

A model is presented that allows prediction of the probability for the formation of appositions between the axons and dendrites of any two neurons based only on their morphological statistics and relative separation. Statistics of axonal and dendritic morphologies of single neurons are obtained from 3D reconstructions of biocytin-filled cells, and a statistical representation of the same cell type is obtained by averaging across neurons according to the model. A simple mathematical formulation is applied to the axonal and dendritic statistical representations to yield the probability for close appositions. The model is validated by a mathematical proof and by comparison of predicted appositions made by layer 5 pyramidal neurons in the rat somatosensory cortex with real anatomical data. The model could be useful for studying microcircuit connectivity and for designing artificial neural networks.

Temporal integration of information and prediction of future sensory inputs are assumed to be important computational tasks of generic cortical microcircuits. It has remained open how cortical microcircuits could possibly achieve this, especially since they consist--in contrast to most neural network models--of neurons and synapses with heterogeneous dynamic responses. It turns out, however, that the diversity of computational units increases the capability of microcircuit models for temporal integration. Furthermore the prediction of future input may be rather easy for such circuits since it suffices to train the readouts from such microcircuits. In this article we show that very simple readouts from a generic recurrently connected circuit of integrate-and-fire neurons with diverse dynamic synapses can be trained in an unsupervised manner to predict movements of different objects, that move within an unlimited number of combinations of speed, angle, and offset over a simulated sensory field. The autonomously trained microcircuit model is also able to compute the direction of motion, which is a computationally difficult problem ('aperture problem') since it requires disambiguation of local sensory readings through the context of other sensory readings at the current and preceding moments. Furthermore the same circuit can be trained simultaneously in a supervised manner to also report the shape and velocity of the moving object. Finally it is shown that the trained neural circuit supports novelty detection and the generation of 'imagined movements'. Altogether the results of this article suggest that it is not necessary to construct specific and biologically unrealistic neural circuit models for specific sensory processing tasks, since 'found' generic cortical microcircuit models in combination with very simple perceptron-like readouts can easily be trained to solve such computational tasks.

A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.

Spike-frequency adaptation in neocortical pyramidal neurons was examined using the whole cell patch-clamp technique and a phenomenological model of neuronal activity. Noisy current was injected to reproduce the irregular firing typically observed under in vivo conditions. The response was quantified by computing the poststimulus histogram (PSTH). To simulate the spiking activity of a pyramidal neuron, we considered an integrate-and-fire model to which an adaptation current was added. A simplified model for the mean firing rate of an adapting neuron under noisy conditions is also presented. The mean firing rate model provides a good fit to both experimental and simulation PSTHs and may therefore be used to study the response characteristics of adapting neurons to various input currents. The models enable identification of the relevant parameters of adaptation that determine the shape of the PSTH and allow the computation of the response to any change in injected current. The results suggest that spike frequency adaptation determines a preferred frequency of stimulation for which the phase delay of a neuron's activity relative to an oscillatory input is zero. Simulations show that the preferred frequency of single neurons dictates the frequency of emergent population rhythms in large networks of adapting neurons. Adaptation could therefore be one of the crucial factors in setting the frequency of population rhythms in the neocortex.

A central debate regarding neocortical function concerns the degree to which the underlying microcircuitry is stereotypically organized. Stereotypy reflects invariance in structure and function, as a result of common genetic templates and environmental conditions, whereas uniqueness can be caused by genetic variations, differences in environmental conditions as well as random processes. Stereotypy is an appealing concept because it provides strong support for determinism in the formation of neuronal microcircuits and in the relationship between their specific structure and function.

Anatomical, electrophysiological and molecular diversity of basket cell-like interneurons in layers II-IV of rat somatosensory cortex were studied using patch-clamp electrodes filled with biocytin. This multiparametric study shows that neocortical basket cells (BCs) are composed of three distinct subclasses: classical large (LBC) and small (SBC) basket cells and a third subclass, the nest basket cell (NBC). Anatomically, NBCs were distinct from LBCs and SBCs in that they formed simpler dendritic arbors and an axonal plexus of inter-mediate density, composed of a few long, smooth axonal branches. Electrophysiologically, NBCs exhibited diverse discharge responses to depolarizing current injections including accommodation, non-accommodation and stuttering. Single-cell multiplex RT-PCR revealed distinct mRNA expression patterns for the calcium binding proteins parvalbumin (PV), calbindin (CB) and calretinin (CR), and the neuropeptides somatostatin (SOM), vasoactive intestinal peptide (VIP), cholecystokinin (CCK) and neuropeptide Y (NPY) for each BC-subclass. SBCs lacked NPY expression but invariably expressed VIP, whereas neither VIP, CR nor SOM expression was detected in LBCs, and VIP and CR expression was absent in NBCs. Electro-physiologically distinct types of NBCs formed GABAergic synapses with specific dynamics onto pyramidal cells (PCs) and received either strongly facilitating or depressing synaptic inputs from PCs. Finally, NBCs were found to be the most common basket cell in layers II/III, while LBCs were the most common in layer IV. These data provide multiparametric distinguishing features of three major subclasses of basket cells and indicate that NBCs are powerful interneurons that provide most of the (peri-)somatic inhibition in the supragranular layers.

This article throws new light on the possible role of synapses in information transmission through theoretical analysis and computer simulations. We show that the internal dynamic state of a synapse may serve as a transient memory buffer that stores information about the most recent segment of the spike train that was previously sent to this synapse. This information is transmitted to the postsynaptic neuron through the amplitudes of the postsynaptic response for the next few spikes. In fact, we show that most of this information about the preceding spike train is already contained in the postsynaptic response for just two additional spikes. It is demonstrated that the postsynaptic neuron receives simultaneously information about the specific type of synapse which has transmitted these pulses. In view of recent findings by Gupta et al. [Science, 287 (2000) 273] that different types of synapses are characteristic for specific types of presynaptic neurons, the postsynaptic neuron receives in this way partial knowledge about the identity of the presynaptic neuron from which it has received information. Our simulations are based on recent data about the dynamics of GABAergic synapses. We show that the relatively large number of synaptic release sites that make up a GABAergic synaptic connection makes these connections suitable for such complex information transmission processes.

Synaptic transmission in the neocortex is dynamic, such that the magnitude of the postsynaptic response changes with the history of the presynaptic activity. Therefore each response carries information about the temporal structure of the preceding presynaptic input spike train. We quantitatively analyze the information about previous interspike intervals, contained in single responses of dynamic synapses, using methods from information theory applied to experimentally based deterministic and probabilistic phenomenological models of depressing and facilitating synapses. We show that for any given dynamic synapse, there exists an optimal frequency of presynaptic spike firing for which the information content is maximal; simple relations between this optimal frequency and the synaptic parameters are derived. Depressing neocortical synapses are optimized for coding temporal information at low firing rates of 0.5-5 Hz, typical to the spontaneous activity of cortical neurons, and carry significant information about the timing of up to four preceding presynaptic spikes. Facilitating synapses, however, are optimized to code information at higher presynaptic rates of 9-70 Hz and can represent the timing of over eight presynaptic spikes.

The precise times of occurrence of individual pre- and postsynaptic action potentials are known to play a key role in the modification of synaptic efficacy. Based on stimulation protocols of two synaptically connected neurons, we infer an algorithm that reproduces the experimental data by modifying the probability of vesicle discharge as a function of the relative timing of spikes in the pre- and postsynaptic neurons. The primary feature of this algorithm is an asymmetry with respect to the direction of synaptic modification depending on whether the presynaptic spikes precede or follow the postsynaptic spike. Specifically, if the presynaptic spike occurs up to 50 ms before the postsynaptic spike, the probability of vesicle discharge is upregulated, while the probability of vesicle discharge is downregulated if the presynaptic spike occurs up to 50 ms after the postsynaptic spike. When neurons fire irregularly with Poisson spike trains at constant mean firing rates, the probability of vesicle discharge converges toward a characteristic value determined by the pre- and postsynaptic firing rates. On the other hand, if the mean rates of the Poisson spike trains slowly change with time, our algorithm predicts modifications in the probability of release that generalize Hebbian and Bienenstock-Cooper-Munro rules. We conclude that the proposed spike-based synaptic learning algorithm provides a general framework for regulating neurotransmitter release probability.

A puzzling feature of the neocortex is the rich array of inhibitory interneurons. Multiple neuron recordings revealed numerous electrophysiological-anatomical subclasses of neocortical gamma-aminobutyric acid-ergic (GABAergic) interneurons and three types of GABAergic synapses. The type of synapse used by each interneuron to influence its neighbors follows three functional organizing principles. These principles suggest that inhibitory synapses could shape the impact of different interneurons according to their specific spatiotemporal patterns of activity and that GABAergic interneuron and synapse diversity may enable combinatorial inhibitory effects in the neocortex.

Throughout the neocortex, groups of neurons have been found to fire synchronously on the time scale of several milliseconds. This near coincident firing of neurons could coordinate the multifaceted information of different features of a stimulus. The mechanisms of generating such synchrony are not clear. We simulated the activity of a population of excitatory and inhibitory neurons randomly interconnected into a recurrent network via synapses that display temporal dynamics in their transmission; surprisingly, we found a behavior of the network where action potential activity spontaneously self-organized to produce highly synchronous bursts involving virtually the entire network. These population bursts were also triggered by stimuli to the network in an all-or-none manner. We found that the particular intensities of the external stimulus to specific neurons were crucial to evoke population bursts. This topographic sensitivity therefore depends on the spectrum of basal discharge rates across the population and not on the anatomical individuality of the neurons, because this was random. These results suggest that networks in which neurons are even randomly interconnected via frequency-dependent synapses could exhibit a novel form of reflex response that is sensitive to the nature of the stimulus as well as the background spontaneous activity.