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An essential feature of goal-directed behavior is the ability to selectively respond to the diverse stimuli in one's environment. However, the neural mechanisms that enable us to respond to target stimuli while ignoring distractor stimuli are poorly understood. To study this sensory selection process, we trained male and female mice in a selective detection task in which mice learn to respond to rapid stimuli in the target whisker field and ignore identical stimuli in the opposite, distractor whisker field. In expert mice, we used widefield Ca 2+ imaging to analyze target-related and distractor-related neural responses throughout dorsal cortex. For target stimuli, we observed strong signal activation in primary somatosensory cortex (S1) and frontal cortices, including both the whisker representation of primary motor cortex (wMC) and anterior lateral motor cortex (ALM). For distractor stimuli, we observe strong signal activation in S1, with minimal propagation to frontal cortex. Our data support only modest subcortical filtering, with robust, step-like attenuation in distractor processing between mono-synaptically coupled regions of S1 and wMC. This study establishes a highly robust model system for studying the neural mechanisms of sensory selection and places important constraints on its implementation. SummaryResponding to task-relevant stimuli while ignoring task-irrelevant stimuli is critical for goaldirected behavior. Yet, the neural mechanisms involved in this selection process are poorly understood. We trained mice in a detection task with both target and distractor stimuli. During expert performance, we measured neural activity throughout cortex using widefield imaging. We observed responses to target stimuli in multiple sensory and motor cortical regions. In contrast, responses to distractor stimuli were abruptly suppressed beyond sensory cortex. Our findings localize the sites of attenuation when successfully ignoring a distractor stimulus, and provide essential foundations for further revealing the neural mechanism of sensory selection and distractor suppression.

Responding to a stimulus requires transforming an internal sensory representation into an internal motor representation. Where and how this sensory-motor transformation occurs is a matter of vigorous debate. Here, we trained male and female mice in a whisker detection go/no-go task in which they learned to respond (lick) following a transient whisker deflection. Using single unit recordings, we quantified sensory-related, motor-related, and choice-related activities in whisker primary somatosensory cortex (S1), whisker region of primary motor cortex (wMC), and anterior lateral motor cortex (ALM), three regions that have been proposed to be critical for the sensory-motor transformation in whisker detection. We observed strong sensory encoding in S1 and wMC, with enhanced encoding in wMC, and a lack of sensory encoding in ALM. We observed strong motor encoding in all three regions, yet largest in wMC and ALM. We observed the earliest choice probability in wMC, despite earliest sensory responses in S1. Based on the criteria of having both strong sensory and motor representations and early choice probability, we identify whisker motor cortex as the cortical region most directly related to the sensory-motor transformation. Our data support a model of sensory encoding originating in S1, sensory amplification and sensory-motor transformation occurring within wMC, and motor signals emerging in ALM after the sensory-motor transformation.

Attention selectively routes the most behaviorally relevant information from the stream of sensory inputs through the hierarchy of cortical areas. previous studies have shown that visual attention depends on the phase of oscillatory brain activities. these studies mainly focused on the stimulus presentation period, rather than the pre-stimulus period. Here, we hypothesize that selective attention controls the phase of oscillatory neural activities to efficiently process relevant information. We document an attentional modulation of pre-stimulus inter-trial phase coherence (a measure of deviation between instantaneous phases of trials) of low frequency local field potentials (LFP) in visual area Mt of macaque monkeys. our data reveal that phase coherence increases following a spatial cue deploying attention towards the receptive field of the recorded neural population. We further show that the attentional enhancement of phase coherence is positively correlated with the modulation of the stimulus-induced firing rate, and importantly, a higher phase coherence is associated with a faster behavioral response. these results suggest a functional utilization of intrinsic neural oscillatory activities for an enhanced processing of upcoming stimuli.One of the most important cognitive functions of the mammalian brain is selective attention. Attention selectively routes the most behaviorally relevant information from the stream of sensory inputs through the hierarchy of cortical areas. This allows the brain to make the most efficient use of its limited neural resources and to create appropriate behavioral responses quickly 1 . Attentional influences on neural responses in sensory cortex have been extensively documented; effects which reflect a multitude of aspects of cortical information processing 1-4 . Covertly directing attention towards the receptive field of a neuron in visual cortex enhances the neural responses even in the absence of visual stimulation 5,6 , alters the shape and profile of receptive fields 7-9 , modulates the variability and temporal structure of the neuron's firing patterns 10,11 , modulates inter-neuronal correlations to increase neural discriminability 12,13 and synchronizes neighboring neurons, presumably to better propagate information to downstream areas [14][15][16] .Attention has been suggested to exploit oscillatory neural activities, as well as oscillatory components of local field potentials (LFP), to enhance the efficacy of cortical processing 17-23 . LFPs represent synaptic activities of local cortical neuronal populations 24 . Their oscillations are tightly linked to attention in both low and high frequencies 18,[25][26][27][28][29] . Previous studies have shown that synchronization in the gamma as well as high gamma band

AbstractResponding to a stimulus requires transforming an internal sensory representation into an internal motor representation. Where and how this sensory-motor transformation occurs is a matter of vigorous debate. Here, we trained mice in a whisker detection go/no-go task in which they learned to respond (lick) following a transient whisker deflection. Using single unit recordings, we quantified sensory-, motor- and choice-related activities in whisker primary somatosensory cortex (S1), whisker primary motor cortex (wMC) and anterior lateral motor cortex (ALM). Based on the criteria of having both strong sensory and motor representations and early choice probability, we identify whisker motor cortex as the cortical region most directly related to the sensory-motor transformation. Our data support a model of sensory amplification occurring between S1 and wMC, sensory-motor transformation occurring within wMC, and propagation of a motor command occurring between wMC and ALM.

Spontaneous neuronal activity strongly impacts stimulus encoding and behavioral responses. We sought to determine the effects of neocortical prestimulus activity on stimulus detection. We trained mice in a selective whisker detection task, in which they learned to respond (lick) to target stimuli in one whisker field and ignore distractor stimuli in the contralateral whisker field. During expert task performance, we used widefield Ca2+ imaging to assess prestimulus and post-stimulus neuronal activity broadly across frontal and parietal cortices. We found that lower prestimulus activity correlated with enhanced stimulus detection: lower prestimulus activity predicted response versus no response outcomes and faster reaction times. The activity predictive of trial outcome was distributed through dorsal neocortex, rather than being restricted to whisker or licking regions. Using principal component analysis, we demonstrate that response trials are associated with a distinct and less variable prestimulus neuronal subspace. For single units, prestimulus choice probability was weak yet distributed broadly, with lower than chance choice probability correlating with stronger sensory and motor encoding. These findings support low amplitude and low variability as an optimal prestimulus cortical state for stimulus detection that presents globally and predicts response outcomes for both target and distractor stimuli.

A learned sensory-motor behavior engages multiple brain regions, including the neocortex and the basal ganglia. How a target stimulus is detected by these regions and converted to a motor response remains poorly understood. Here, we performed electrophysiological recordings and pharmacological inactivations of whisker motor cortex and dorsolateral striatum to determine the representations within, and functions of, each region during performance in a selective whisker detection task in male and female mice. From the recording experiments, we observed robust, lateralized sensory responses in both structures. We also observed bilateral choice probability and pre-response activity in both structures, with these features emerging earlier in whisker motor cortex than dorsolateral striatum. These findings establish both whisker motor cortex and dorsolateral striatum as potential contributors to the sensory-to-motor (sensorimotor) transformation. We performed pharmacological inactivation studies to determine the necessity of these brain regions for this task. We found that suppressing the dorsolateral striatum severely disrupts responding to task-relevant stimuli, without disrupting the ability to respond, whereas suppressing whisker motor cortex resulted in more subtle changes in sensory detection and response criterion. Together these data support the dorsolateral striatum as an essential node in the sensorimotor transformation of this whisker detection task.Significance StatementSelecting an item in a grocery store, hailing a cab – these daily practices require us to transform sensory stimuli into motor responses. Many decades of previous research have studied goal-directed sensory-to-motor transformations within various brain structures, including the neocortex and the basal ganglia. Yet, our understanding of how these regions coordinate to perform sensory-to-motor transformations is limited because these brain structures are often studied by different researchers and through different behavioral tasks. Here, we record and perturb specific regions of the neocortex and the basal ganglia and compare their contributions during performance of a goal-directed somatosensory detection task. We find notable differences in the activities and functions of these regions, which suggests specific contributions to the sensory-to-motor transformation process.

24Attention selectively routes the most behaviorally relevant information among the vast 25 pool of sensory inputs through cortical regions. Previous studies have shown that visual 26 attention samples the surrounding stimuli periodically. However, the neural mechanism 27 underlying this sampling in the sensory cortex, and whether the brain actively uses these 28 rhythms, has remained elusive. Here, we hypothesize that selective attention controls the 29 phase of oscillatory synaptic activities to efficiently process the relevant information in 30 the brain. We document an attentional modulation of pre-stimulus inter-trial phase 31 coherence (a measure of deviation between instantaneous phases of trials) at low 32 frequencies in macaque visual area MT. Our data reveal that phase coherence increases 33 when attention is deployed towards the receptive field of the recorded neural population. 34 We further show that the attentional enhancement of phase coherence is positively 35 correlated with the attentional modulation of stimulus induced firing rate, and 36 importantly, a higher phase coherence leads to a faster behavioral response. Our results 37 suggest a functional utilization of intrinsic neural oscillatory activities for better 38 processing upcoming environmental stimuli, generating the optimal behavior. 39 attention | local field potentials | phase coherence | reaction time | visual cortex 40 41 72 (Yamagishi et al., 2003). Although there is prominent evidence on attentional modulation of 73 low frequency amplitude, the role of low frequency phase in attentional processing is yet 74 controversial. 75 The phase of low frequency oscillations modulates local neural activities represented by 76 gamma band activity, which presumably enables distant brain regions to interact (Demiralp et 77 al. , 2007). Some studies have shown that the phase of ongoing neural oscillations is responsible 78 for periodic sampling by visual attention (Busch and VanRullen, 2010; VanRullen et al., 2011). 79Furthermore, the phase of low frequency oscillations facilitates information transfer and neural 80 coding in the brain (Voloh and Womelsdorf, 2016). Therefore, low frequency phase can enable 81 the neural system to prepare for processing upcoming sensory stimuli. 82Pre-stimulus neural activity has been shown to be a determinant of retrieving episodic memory, 83 perception of environmental information and attention-related variability in response speed 84 (Addante et al., 2011; Hanslmayr et al., 2013 Hanslmayr et al., , 2007 Shibata et al., 2008). Interestingly, it has 85 been shown that pre-stimulus brain activity causally determines visual perception (Dugué et 86 al., 2011). In addition, it has been shown that the phase of low frequency oscillations is 87 responsible for this causal relationship (Hanslmayr et al., 2013). Furthermore, attention has 88 been reported to determine the phase of low frequency neural oscillations in order to influence 89 neuronal responses and behavioral responses to external events (Lakatos et al.,...