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
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