Lateral prefrontal and posterior parietal cortical areas exhibit task-dependent activation during working memory tasks in humans and monkeys. Neurons in these regions become synchronized during attention demanding tasks, but the contribution of these interactions to working memory is largely unknown. Using simultaneous recordings of neural activity from multiple areas in both regions, we find widespread, task-dependent and content specific synchronization of activity across the fronto-parietal network during visual working memory. The patterns of synchronization are prevalent among stimulus selective neurons and are governed by influences arising in parietal cortex. These results indicate that short-term memories are represented by large-scale patterns of synchronized activity across the fronto-parietal network.

Studies on processing in primary visual areas often use artificial stimuli such as bars or gratings. As a result, little is known about the properties of activity patterns for the natural stimuli processed by the visual system on a daily basis. Furthermore, in the cat, a well-studied model system for visual processing, most results are obtained from anesthetized subjects and little is known about neuronal activations in the alert animal. Addressing these issues, we measure local field potentials (lfp) and multiunit spikes in the primary visual cortex of awake cats. We compare changes in the lfp power spectra and multiunit firing rates for natural movies, movies with modified spatio-temporal correlations as well as gratings. The activity patterns elicited by drifting gratings are qualitatively and quantitatively different from those elicited by natural stimuli and this difference arises from both spatial as well as temporal properties of the stimuli. Furthermore, both local field potentials and multiunit firing rates are most sensitive to the second-order statistics of the stimuli and not to their higher-order properties. Finally, responses to natural movies show a large variability over time because of activity fluctuations induced by rapid stimulus motion. We show that these fluctuations are not dependent on the detailed spatial properties of the stimuli but depend on their temporal jitter. These fluctuations are important characteristics of visual activity under natural conditions and impose limitations on the readout of possible differences in mean activity levels.

Working memory requires large-scale cooperation among widespread cortical and subcortical brain regions. Importantly, these processes must achieve an appropriate balance between functional integration and segregation, which are thought to be mediated by task-dependent spatiotemporal patterns of correlated activity. Here, we used cross-correlation analysis to estimate the incidence, magnitude, and relative phase angle of temporally correlated activity from simultaneous local field potential recordings in a network of prefrontal and posterior parietal cortical areas in monkeys performing an oculomotor, delayed match-to-sample task. We found longrange intraparietal and frontoparietal correlations that display a bimodal distribution of relative phase values, centered near 0°and 180°, suggesting a possible basis for functional segregation among distributed networks. Both short-and long-range correlations display striking task-dependent transitions in strength and relative phase, indicating that cognitive events are accompanied by robust changes in the pattern of temporal coordination across the frontoparietal network.

In various mental disorders, dysfunction of the prefrontal cortex contributes to cognitive deficits. Here we studied how the claustrum (CLA), a nucleus sharing reciprocal connections with the cortex, may participate in these cognitive impairments. We show that specific ensembles of CLA and of medial prefrontal cortex (mPFC) neurons are activated during a task requiring cognitive control such as attentional set-shifting, i.e. the ability to shift attention towards newly relevant stimulus-reward associations while disengaging from irrelevant ones. CLA neurons exert a direct excitatory input on mPFC pyramidal cells, and chemogenetic inhibition of CLA neurons suppresses the formation of specific mPFC assemblies during attentional set-shifting. Furthermore, impairing the recruitment of specific CLA assemblies through opto/chemogenetic manipulations prevents attentional set-shifting. In conclusion, we propose that the CLA controls the reorganization of mPFC ensembles to enable attentional set-shifting, emphasizing a potential role of the CLA-mPFC network in attentional dysfunctions.

Schizophrenia is a severely debilitating neurodevelopmental disorder. Establishing a causal link between circuit dysfunction and particular behavioral traits that are relevant to schizophrenia is crucial to shed new light on the mechanisms underlying the pathology. We studied an animal model of the human 22q11 deletion syndrome, the mutation that represents the highest genetic risk of developing schizophrenia. We observed a desynchronization of hippocampal neuronal assemblies that resulted from parvalbumin interneuron hypoexcitability. Rescuing parvalbumin interneuron excitability with pharmacological or chemogenetic approaches was sufficient to restore wild-type-like CA1 network dynamics and hippocampal-dependent behavior during adulthood. In conclusion, our data provide insights into the network dysfunction underlying schizophrenia and highlight the use of reverse engineering to restore physiological and behavioral phenotypes in an animal model of neurodevelopmental disorder.

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It was often reported and suggested that the synchronization of spikes can occur without changes in the firing rate. However, few theoretical studies have tested its mechanistic validity. In the present study, we investigate whether changes in synaptic weights can induce an independent modulation of synchrony while the firing rate remains constant. We study this question at the level of both single neurons and neuronal populations using network simulations of conductance based integrate-and-fire neurons. The network consists of a single layer that includes local excitatory and inhibitory recurrent connections, as well as long-range excitatory projections targeting both classes of neurons. Each neuron in the network receives external input consisting of uncorrelated Poisson spike trains. We find that increasing this external input leads to a linear increase of activity in the network, as well as an increase in the peak frequency of oscillation. In contrast, balanced changes of the synaptic weight of excitatory longrange projections for both classes of postsynaptic neurons modulate the degree of synchronization without altering the firing rate. These results demonstrate that, in a simple network, synchronization and firing rate can be modulated independently, and thus, may be used as independent coding dimensions.

Cognitive processes play out on massive brain-wide networks, which produce widely distributed patterns of activity. Capturing these activity patterns requires tools that are able to simultaneously measure activity from many distributed sites with high spatiotemporal resolution. Unfortunately, current techniques with adequate coverage do not provide the requisite spatiotemporal resolution. Large-scale microelectrode recording devices, with dozens to hundreds of microelectrodes capable of simultaneously recording from nearly as many cortical and subcortical areas, provide a potential way to minimize these tradeoffs. However, placing hundreds of microelectrodes into a behaving animal is a highly risky and technically challenging endeavor that has only been pursued by a few groups. Recording activity from multiple electrodes simultaneously also introduces several statistical and conceptual dilemmas, such as the multiple comparisons problem and the uncontrolled stimulus response problem. In this perspective article, we discuss some of the techniques that we, and others, have developed for collecting and analyzing large-scale data sets, and address the future of this emerging field.