Networks of GABAergic neurons are key elements in the generation of g oscillations in the brain. Computa-tional studies suggested that the emergence of co-herent oscillations requireshyperpolarizing inhibition. Here, we show that GABAA receptor-mediated inhibi-tion in mature interneurons of the hippocampal den-tate gyrus is shunting rather than hyperpolarizing. Un-expectedly, when shunting inhibition is incorporated into a structured interneuron network model with fast and strong synapses, coherent oscillations emerge. In comparison to hyperpolarizing inhibition, networks with shunting inhibition show several advantages. First, oscillations are generated with smaller tonic ex-citatory drive. Second, network frequencies are tuned to the g band. Finally, robustness against heterogene-ity in the excitatory drive is markedly improved. In sin-gle interneurons, shunting inhibition shortens the in-terspike interval for low levels of drive but prolongs it for high levels, leading to homogenization of neuro-nal firing rates. Thus, shunting inhibition may confer increased robustness to g oscillations in the brain.

Neural oscillations and their synchronization may represent a versatile signal to realize flexible communication within and between cortical areas. By now, there is extensive evidence to suggest that cognitive functions depending on coordination of distributed neural responses, such as perceptual grouping, attention-dependent stimulus selection, subsystem integration, working memory, and consciousness, are associated with synchronized oscillatory activity in the theta-, alpha-, beta-, and gamma-band, suggesting a functional mechanism of neural oscillations in cortical networks. In addition to their role in normal brain functioning, there is increasing evidence that altered oscillatory activity may be associated with certain neuropsychiatric disorders, such as schizophrenia, that involve dysfunctional cognition and behavior. In the following article,

Although spontaneous activity occurs throughout the neocortex, its relation to the activity produced by external or sensory inputs remains unclear. To address this, we used calcium imaging of mouse thalamocortical slices to reconstruct, with single-cell resolution, the spatiotemporal dynamics of activity of layer 4 in the presence or absence of thalamic stimulation. We found spontaneous neuronal coactivations corresponded to intracellular UP states. Thalamic stimulation of sufficient frequency (>10 Hz) triggered cortical activity, and UP states, indistinguishable from those arising spontaneously. Moreover, neurons were activated in identical and precise spatiotemporal patterns in thalamically triggered and spontaneous events. The similarities between cortical activations indicate that intracortical connectivity plays the dominant role in the cortical response to thalamic inputs. Our data demonstrate that precise spatiotemporal activity patterns can be triggered by thalamic inputs and indicate that the thalamus serves to release intrinsic cortical dynamics.

Spontaneous low-frequency fluctuations in the blood oxygen level–dependent (BOLD) functional magnetic res-onance imaging (MRI) signal have been shown to reflect neural synchrony between brain regions. A ‘‘default net-work’ ’ of spontaneous low-frequency fluctuations has been described in healthy volunteers during stimulus-independent thought. Negatively correlated with this network are regions activated during attention-demanding tasks. Both these networks involve brain regions and functions that have been linked with schizophrenia in previous research. The present study examined spontaneous slow fluctuations in the BOLD signal at rest, as measured by correlation with low-frequency oscillations in the posterior cingulate, in 17 schizophrenic patients, and 17 comparable healthy volunteers. Healthy volunteers demonstrated correlation

Large-scale neural networks are thought to be an essential substrate for the implementation of cognitive function by the brain. If so, then a thorough understanding of cognition is not possible without knowledge of how the large-scale neural networks of cognition (neurocognitive networks) operate. Of necessity, such understanding requires insight into structural, functional, and dynamical aspects of network operation, the intimate interweaving of which may be responsible for the intricacies of cognition. Knowledge of anatomical structure is basic to understanding how neurocognitive networks operate. Phylogenetically and ontogenetically determined patterns of synaptic connectivity form a structural network of brain areas, allowing communication between widely distributed collections of areas. The function of neurocognitive networks depends on selective activation of anatomically linked cortical and subcortical areas in a wide variety of configurations. Large-scale functional networks provide the cooperative processing which gives expression to cognitive function. The dynamics of neurocognitive network function relates to the evolving patterns of interacting brain areas that express cognitive function in real time. This article considers the proposition that a basic similarity of the structural, functional, and dynamical features of all neurocognitive networks in the brain causes them to function according to common operational principles. The formation of neural context through the coordinated mutual constraint of multiple interacting cortical areas, is considered as a guiding principle underlying all cognitive functions. Increasing knowledge of the operational principles of neurocognitive networks is likely to promote the advancement of cognitive theories, and to seed strategies for the enhancement of cognitive abilities.

Brain oscillations are important in controlling the timing of neuronalfiring.Thisprocesshasbeenextensivelyanalyzedin connection with gamma frequency oscillations and more recently with respect to theta frequency oscillations. Here we review evidence that theta and gamma oscillations work together to form a neural code. This coding scheme provides a way for multiple neural ensembles to represent an ordered sequence of items. In the hippocampus, this coding scheme is utilized during the phase precession, a phenomenon that can beinterpretedastherecallofsequencesofitems(places)from long-term memory. The same coding scheme may be used in certain cortical regions to encode multi-item short-term memory. The possibility that abnormalities in theta/gamma could underlie symptoms of schizophrenia is discussed. Key words: phase precession/synchronization/ hippocampus/memory Although brain oscillations have been known for a long time, it is only recently that attempts have been made to understand how brain rhythms support cognitive operations. 1 There is now considerable interest in the role of gamma oscillations (40–100 Hz) in cognitive processes and the possibility that abnormalities in these oscillations might underlie symptoms of schizophrenia (see reviews in this issue 2,3). However, prominent oscillations at lower frequency, notably in the theta range (4–10 Hz), also occur during cognitive processes. In this brief review, we will describe studies indicating that theta and gamma oscillations work together to create a neural code, the function of which is to allow representation of multiple items in a defined order. The possibility that this coding scheme is also utilized in other brain regions will be discussed.

In recent years, numerous studies have tested the relevance of neural oscillations in neuropsychiatric conditions, highlighting the potential role of changes in temporal coordination as a pathophysiological mechanism in brain disorders. In the current review, we provide an update on this hypothesis because of the growing evidence that temporal coordination is essential for the context and goal-dependent, dynamic formation of large-scale cortical networks. We shall focus on issues that we consider as particularly promising for a translational research program aimed at furthering our understanding of the origins of neuropsychiatric disorders and the development of effective therapies. We will focus on schizophrenia and Autism Spectrum Disorders (ASDs) to highlight important issues and challenges for the implementation of such an approach. Specifically, we will argue that deficits in temporal coordination lead to a disruption of functional large-scale networks which in turn can account for several specific dysfunctions associated with these disorders. Neuropsychiatric Disorders: the Grand Challenge for Brain Sciences The understanding of the origins of neuropsychiatric disorders, such as schizophrenia,

Neural systems adapt to changes in stimulus statistics. However, it is not known how stimuli with complex temporal dynamics drive the dynamics of adaptation and the resulting firing rate. For single neurons, it has often been assumed that adaptation has a single time scale. Here, we show that single rat neocortical pyramidal neurons adapt with a time scale that depends on the time scale of changes in stimulus statistics. This multiple time scale adaptation is consistent with fractional order differentiation, such that the neuron’s firing rate is a fractional derivative of slowly varying stimulus parameters. Biophysically, even though neuronal fractional differentiation effectively yields adaptation with many time scales, we find that its implementation requires only a few, properly balanced known adaptive mechanisms. Fractional differentiation provides single neurons with a fundamental and general computation that can contribute to efficient information processing, stimulus anticipation, and frequency independent phase shifts of oscillatory neuronal firing.

Simple perceptual decisions are ideally suited for studying the sensorimotor transformations underlying flexible behavior [1, 2]. During perceptual detection, a noisy sensory signal is converted into a behavioral report of the presence or absence of a perceptual experience [3]. Here, we used magnetoencephalography (MEG) to link the dynamics of neural population activity in human motor cortex to perceptual choices in a ‘‘yes/no’ ’ visual motion detection task. We found that (1) motor response-selective MEG activity in the ‘‘gamma’ ’ (64–100 Hz) and ‘‘beta’ ’ (12–36 Hz) frequency ranges predicted subjects ’ choices several seconds before their overt manual response; (2) this choice-predictive activity built up gradually during stimulus viewing toward both ‘‘yes’ ’ and ‘‘no’ ’ choices; and (3) the choice-predictive activity in motor cortex reflected the temporal integral of

■ Converging evidence from humans and nonhuman pri-mates is obliging us to abandon conventional models in favor of a radically different, distributed-network paradigm of corti-cal memory. Central to the new paradigm is the concept of memory network or cognit—that is, a memory or an item of knowledge defined by a pattern of connections between neu-ron populations associated by experience. Cognits are hi-erarchically organized in terms of semantic abstraction and complexity. Complex cognits link neurons in noncontiguous cortical areas of prefrontal and posterior association cortex. Cognits overlap and interconnect profusely, even across hier-archical levels (heterarchically), whereby a neuron can be part of many memory networks and thus many memories or items of knowledge. ■