Abstract:Optimal behavior and survival result from integration of information across sensory systems. Modulation of network activity at the level of primary sensory cortices has been identified as a mechanism of cross-modal integration, yet its cellular substrate is still poorly understood. Here, we uncover the mechanisms by which individual neurons in primary somatosensory (S1) and visual (V1) cortices encode visual-tactile stimuli. For this, simultaneous extracellular recordings were performed from all layers of the … Show more
“…The field has a growing body of evidence showing that the canonical model needs to be enhanced to support more sophisticated visual computation. 5,31 For instance, neurons in mouse V1 show complex visual responses previously associated with higher cortical areas, including pattern selectivity for plaid stimuli 33 Furthermore, the emergence of the rodent as a prominent model of the visual system in recent years has revealed evidence of non-visual computation, including behavioral responses such as reward timing and sequence learning 34 , as well as modulation by multimodal sensory stimuli 35,36 and motor signals. 23,24,37–39…”
SummaryTo understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.
“…The field has a growing body of evidence showing that the canonical model needs to be enhanced to support more sophisticated visual computation. 5,31 For instance, neurons in mouse V1 show complex visual responses previously associated with higher cortical areas, including pattern selectivity for plaid stimuli 33 Furthermore, the emergence of the rodent as a prominent model of the visual system in recent years has revealed evidence of non-visual computation, including behavioral responses such as reward timing and sequence learning 34 , as well as modulation by multimodal sensory stimuli 35,36 and motor signals. 23,24,37–39…”
SummaryTo understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.
“…If we had limited our analysis to the synchronization of spike-times irrespective of their iFR, we would not have observed any differences between PV and SST interneurons. This result provides further evidence that, in vivo, spike-timing and firing-rate cannot be considered in isolation [7][8][9] .…”
Section: Discussionmentioning
confidence: 55%
“…Since spike-time and firing rate co-exist and are not completely dissociable in the spiketiming sequences [7][8][9] , it is important to examine how the synchronization of spike-times depend on the iFR of ISI. Thus, we directly compared the spike-times of synchronized L5 and L6 neurons with that of L4 neurons as a function of the L4 neurons' iFRs.…”
Section: Resultsmentioning
confidence: 99%
“…Precisely timed spikes that are spatially coordinated or synchronized across multiple neurons with millisecond temporal precision have been shown to encode sensory information about stimuli [1][2][3][4][5][6] . Information is contained in both the spike times 2,5 as well as the instantaneous firing-rate (iFR) of precisely timed spike sequences 1,3 , emphasizing the coexistence of temporal and rate codes during sensory information processing [7][8][9] . Yet, the neural circuit mechanisms supporting the generation of highly synchronized spike sequences across cortical layers remain unknown.…”
Section: Introductionmentioning
confidence: 99%
“…Theoretical as well as experimental studies have suggested that inhibition can modulate spatial correlation/synchronization of spike-times between nearby neurons [10][11][12] and in neurons across multiple neuronal layers 13,14 . In fact, the latency between excitation and inhibition (E/I latency) has been shown to modulate timing and rate of spike sequences in tandem in vivo [7][8][9] . Thus, E/I latency may have critical role in spatio-temporal synchronization of spike-times.…”
Synchronization of precise spike-times across multiple neurons carries information about sensory stimuli. Inhibitory interneurons are suggested to promote this synchronization, but it is unclear whether distinct interneuron subtypes provide different contributions. To test this, we examined single-unit recordings from barrel cortex in vivo and used optogenetics to determine the contribution of two classes of inhibitory interneurons: parvalbumin (PV)-and somatostatin (SST)-positive interneurons to spike-timing synchronization across cortical layers. We found that PV interneurons preferentially promote the synchronization of spiketimes when instantaneous firing-rates are low (<12 Hz), whereas SST interneurons preferentially promote the synchronization of spike-times when instantaneous firing-rates are high (>12 Hz). Furthermore, using a computational model, we demonstrate that these effects can be explained by PV and SST interneurons having preferential contribution to feedforward and feedback inhibition, respectively. Our findings demonstrate that distinct subtypes of inhibitory interneurons have frequency-selective roles in spatio-temporal synchronization of precise spike-times.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.