Attention Configures Synchronization Within Local Neuronal Networks for Processing of the Behaviorally Relevant Stimulus

Abstract

The need for fast and dynamic processing of relevant information imposes high demands onto the flexibility and efficiency of the nervous system. A good example for such flexibility is the attention-dependent selection of relevant sensory information. Studies investigating attentional modulations of neuronal responses to simultaneously arriving input showed that neurons respond, as if only the attended stimulus would be present within their receptive fields (RF). However, attention also improves neuronal representation and behavioral performance, when only one stimulus is present. Thus, attention serves for selecting relevant input and changes the neuronal processing of signals representing selected stimuli, ultimately leading to a more efficient behavioral performance. Here, we tested the hypothesis that attention configures the strength of functional coupling between a local neuronal network's neurons specifically for effective processing of signals representing attended stimuli. This coupling is measured as the strength of γ-synchronization between these neurons. The hypothesis predicts that the pattern of synchronization in local networks should depend on which stimulus is attended. Furthermore, we expect this pattern to be similar for the attended stimulus presented alone or together with irrelevant stimuli in the RF. To test these predictions, we recorded spiking-activity and local field potentials (LFP) with closely spaced electrodes in area V4 of monkeys performing a demanding attention task. Our results show that the γ-band phase coherence (γ-PhC) between spiking-activity and the LFP, as well as the spiking-activity of two groups of neurons, strongly depended on which of the two stimuli in the RF was attended. The γ-PhC was almost identical for the attended stimulus presented either alone or together with a distractor. The functional relevance of dynamic γ-band synchronization is further supported by the observation of strongly degraded γ-PhC before behavioral errors, while firing rates were barely affected. These qualitatively different results point toward a failure of attention-dependent top-down mechanisms to correctly synchronize the local neuronal network in V4, even though this network receives the correctly selected input. These findings support the idea of a flexible, demand-dependent dynamic configuration of local neuronal networks, for performing different functions, even on the same sensory input.

Optimizing the Yield of Multi-Unit Activity by Including the Entire Spiking Activity

“…Full-wave rectification and subsequent low-pass filtering of multi-unit spiking activity was introduced by Eckhorn and colleagues (Eckhorn, 1991, 1992; Eckhorn and Obermüller, 1993; Brosch et al, 1997) to improve spectral frequency analysis of correlated activity, and has been used by several groups as an alternative measure for multi-unit activity (Self et al, 2016; Dougherty et al, 2017; Drebitz et al, 2018). Because this method does not involve setting a threshold for cutting spike amplitudes, full information about the neuronal response is preserved.…”

“…The fifth animal (monkey T) was implanted with a recording chamber located above areas V4 and V1, allowing for bidirectional movement of up to four electrodes. Details on anesthesia, analgesia, and surgical procedures are reported elsewhere (Wegener et al, 2004; Galashan et al, 2011; Schledde et al, 2017; Drebitz et al, 2018). All procedures were in accordance with the Regulations for the Welfare of Experimental Animals issued by the Federal Government of Germany and with the guidelines of the European Union (2010/63/EU) for care and use of laboratory animals, and were approved by the local authorities (Senator für Gesundheit, Bremen, Germany).…”

“…Since its introduction, ESA has been used as an alternative measure for multi-unit activity by several groups (Self et al, 2016; Dougherty et al, 2017; Drebitz et al, 2018), but many of its important properties are still awaiting quantitative description. The purpose of the present study is to analyze the potential of ESA for increasing the yield of multi-unit recordings at different SNRs, and to quantitatively compare evoked responses based on ESA and thresholded MUA.…”

Differential Contributions of Inhibitory Subnetwork to Visual Cortical Modulations Identified via Computational Model of Working Memory

“…The goal of this study is to provide insight into the neural mechanisms giving rise to this non-linear interaction between sensory stimulus and top-down signal: to explain how extrastriate responses to sensory stimuli are modulated, without changes in baseline firing, to account for WM- and attention-induced changes in spiking behavior. Both attention and WM also induce oscillations in local field potentials (LFPs) (Fries et al, 2008 ; Siegel et al, 2008 ; Gregoriou et al, 2009 ; Liebe et al, 2012 ; Daume et al, 2017 ) and the timing of spikes relative to these oscillations (Lee et al, 2005 ; Vinck et al, 2013 ; Bahmani et al, 2018 ; Drebitz et al, 2018 ; Fiebelkorn and Kastner, 2020 ). These oscillations are believed to contribute to enhanced sensory processing (Bahmani et al, 2018 ).…”

Inferring entire spiking activity from local field potentials with deep learning

“…taking the absolute value) followed by low-pass filtering 14 . The term ESA is relatively new 17 but its underlying principle has existed and been used for several decades 15,18 . Despite being less popular and less frequently used signal than its counterparts, ESA offers appealing advantages.…”