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Vasoactive intestinal peptide-expressing (VIP) interneurons in the cortex regulate feedback inhibition of pyramidal neurons through suppression of somatostatin-expressing (SST) interneurons and, reciprocally, SST neurons inhibit VIP neurons. Although VIP neuron activity in the primary visual cortex (V1) of mouse is highly correlated with locomotion, the relevance of locomotion-related VIP neuron activity to visual coding is not known. Here we show that VIP neurons in mouse V1 respond strongly to low contrast front-to-back motion that is congruent with self-motion during locomotion but are suppressed by other directions and contrasts. VIP and SST neurons have complementary contrast tuning. Layer 2/3 contains a substantially larger population of low contrast preferring pyramidal neurons than deeper layers, and layer 2/3 (but not deeper layer) pyramidal neurons show bias for front-to-back motion specifically at low contrast. Network modeling indicates that VIP-SST mutual antagonism regulates the gain of the cortex to achieve sensitivity to specific weak stimuli without compromising network stability.

The apical dendrites of pyramidal neurons in sensory cortex receive primarily top-down signals from associative and motor regions, while cell bodies and nearby dendrites are heavily targeted by locally recurrent or bottom-up inputs from the sensory periphery. Based on these differences, a number of theories in computational neuroscience postulate a unique role for apical dendrites in learning. However, due to technical challenges in data collection, little data is available for comparing the responses of apical dendrites to cell bodies over multiple days. Here we present a dataset collected through the Allen Institute Mindscope’s OpenScope program that addresses this need. This dataset comprises high-quality two-photon calcium imaging from the apical dendrites and the cell bodies of visual cortical pyramidal neurons, acquired over multiple days in awake, behaving mice that were presented with visual stimuli. Many of the cell bodies and dendrite segments were tracked over days, enabling analyses of how their responses change over time. This dataset allows neuroscientists to explore the differences between apical and somatic processing and plasticity.

To 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 activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes the cortical activity of nearly 60,000 neurons from six visual areas, four layers, and 12 transgenic mouse lines in a total of 243 adult mice, in response to a systematic set of visual stimuli. We classify neurons on the basis of joint reliabilities to multiple stimuli and validate this functional classification with models of visual responses. While most classes are characterized by responses to specific subsets of the stimuli, the largest class is not reliably responsive to any of the stimuli and becomes progressively larger in higher visual areas. These classes reveal a functional organization wherein putative dorsal areas show specialization for visual motion signals.

Identification of structural connections between neurons is a prerequisite to understanding brain function. Here we developed a pipeline to systematically map brain-wide monosynaptic input connections to genetically defined neuronal populations using an optimized rabies tracing system. We used mouse visual cortex as the exemplar system and revealed quantitative target-specific, layer-specific and cell-class-specific differences in its presynaptic connectomes. The retrograde connectivity indicates the presence of ventral and dorsal visual streams and further reveals topographically organized and continuously varying subnetworks mediated by different higher visual areas. The visual cortex hierarchy can be derived from intracortical feedforward and feedback pathways mediated by upper-layer and lower-layer input neurons. We also identify a new role for layer 6 neurons in mediating reciprocal interhemispheric connections. This study expands our knowledge of the visual system connectomes and demonstrates that the pipeline can be scaled up to dissect connectivity of different cell populations across the mouse brain. The authors developed an optimized rabies tracing system for generating brain-wide monosynaptic input connectomes, and applied it in mouse visual cortex to reveal topographically organized subnetworks co-defined by visual areas, layers and cell classes.

We present a dual-plane mesoscopic imaging system capable of simultaneous image acquisition from two independent focal planes. The system was designed as an add-on to a recently introduced large field-of-view two-photon microscopy system, developed by Sofroniew, et al., eLife, 5, e14472, 2016. In this work, we merge two advanced multiphoton imaging technologies, i.e., temporal-division multiplexing and remote focusing, to maintain diffraction-limited resolution at both imaging planes, and achieve a more than 2-fold increase in the system's overall imaging throughput. We introduce a novel solution to decode temporally interleaved analog signals at nanosecond timescales to achieve high-speed time-multiplexed imaging. Detailed characterization and comparison of the modified and the original two-photon microscopy system was performed.

The classic view that neural populations in sensory cortices preferentially encode responses to incoming stimuli has been strongly challenged by recent experimental studies. Despite the fact that a large fraction of variance of visual responses in rodents can be attributed to behavioral state and movements, trial-history, and salience, the effects of contextual modulations and expectations on sensory-evoked responses in visual and association areas remain elusive. Here, we present a comprehensive experimental and theoretical study showing that hierarchically connected visual and association areas differentially encode the temporal context and expectation of naturalistic visual stimuli, consistent with the theory of hierarchical predictive coding. We measured neural responses to expected and unexpected sequences of natural scenes in the primary visual cortex (V1), the posterior medial higher order visual area (PM), and retrosplenial cortex (RSP) using 2-photon imaging in behaving mice collected through the Allen Institute Mindscope's OpenScope program. We found that information about image identity in neural population activity depended on the temporal context of transitions preceding each scene, and decreased along the hierarchy. Furthermore, our analyses revealed that the conjunctive encoding of temporal context and image identity was modulated by expectations of sequential events. In V1 and PM, we found enhanced and specific responses to unexpected oddball images, signaling stimulus-specific expectation violation. In contrast, in RSP the population response to oddball presentation recapitulated the missing expected image rather than the oddball image. These differential responses along the hierarchy are consistent with classic theories of hierarchical predictive coding whereby higher areas encode predictions and lower areas encode deviations from expectation. We further found evidence for drift in visual responses on the timescale of minutes. Although activity drift was present in all areas, population responses in V1 and PM, but not in RSP, maintained stable encoding of visual information and representational geometry. Instead we found that RSP drift was independent of stimulus information, suggesting a role in generating an internal model of the environment in the temporal domain. Overall, our results establish temporal context and expectation as substantial encoding dimensions in the visual cortex subject to fast representational drift and suggest that hierarchically connected areas instantiate a predictive coding mechanism.The classic view that neural populations in sensory cortices preferentially encode responses to incoming stimuli has been strongly challenged by recent experimental studies. Despite the fact that a large fraction of variance of visual responses in rodents can be attributed to behavioral state and movements, trial-history, and salience, the effects of contextual modulations and expectations on sensory-evoked responses in visual and association areas remain elusive. Here, we present a comprehensive experimental and theoretical study showing that hierarchically connected visual and association areas differentially encode the temporal context and expectation of naturalistic visual stimuli, consistent with the theory of hierarchical predictive coding. We measured neural responses to expected and unexpected sequences of natural scenes in the primary visual cortex (V1), the posterior medial higher order visual area (PM), and retrosplenial cortex (RSP) using 2-photon imaging in behaving mice collected through the Allen Institute Mindscope's OpenScope program. We found that information about image identity in neural population activity depended on the temporal context of transitions preceding each scene, and decreased along the hierarchy. Furthermore, our analyses revealed that the conjunctive encoding of temporal context and image identity was modulated by expectations of sequential events. In V1 and PM, we found enhanced and specific responses to unexpected oddball images, signaling stimulus-specific expectation violation. In contrast, in RSP the population response to oddball presentation recapitulated the missing expected image rather than the oddball image. These differential responses along the hierarchy are consistent with classic theories of hierarchical predictive coding whereby higher areas encode predictions and lower areas encode deviations from expectation. We further found evidence for drift in visual responses on the timescale of minutes. Although activity drift was present in all areas, population responses in V1 and PM, but not in RSP, maintained stable encoding of visual information and representational geometry. Instead we found that RSP drift was independent of stimulus information, suggesting a role in generating an internal model of the environment in the temporal domain. Overall, our results establish temporal context and expectation as substantial encoding dimensions in the visual cortex subject to fast representational drift and suggest that hierarchically connected areas instantiate a predictive coding mechanism.

Detecting novel stimuli in the environment is critical for learning and survival, yet the neural basis of novelty processing is not understood. To characterize cell type-specific novelty processing, we surveyed the activity of ~15,000 excitatory and inhibitory neurons in mice performing a visual task with novel and familiar stimuli. Clustering revealed a dozen functional neuron types defined by experience-dependent encoding. Vasoactive-intestinal-peptide (Vip) expressing inhibitory neurons were diverse, encoding novel stimuli, omissions of familiar stimuli, or behavioral features. Distinct Somatostatin (Sst) expressing inhibitory neurons encoded either familiar or novel stimuli. Subsets of excitatory neurons co-clustered with specific Vip or Sst subpopulations, while Sst and Vip inhibitory clusters were non-overlapping. This study establishes that novelty processing is mediated by diverse functional neuron types in the visual cortex.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Clustering analysis is now performed on all cell types together instead of each independently; A new figure describing the full open access dataset has been added (Figure 1); Supplementary Text describing the dataset and details of analysis methods has been added; Figures and text have been revised for overall clarity and ease of interpretation* https://allensdk.readthedocs.io/en/latest/visual_behavior_optical_physiology.html* https://doi.org/10.48324/dandi.000711/0.231121.1730

Abstract Scientists have long conjectured that the neocortex learns the structure of the environment in a predictive, hierarchical manner. To do so, expected, predictable features are differentiated from unexpected ones by comparing bottom-up and top-down streams of data. It is theorized that the neocortex then changes the representation of incoming stimuli, guided by differences in the responses to expected and unexpected events. Such differences in cortical responses have been observed; however, it remains unknown whether these unexpected event signals govern subsequent changes in the brain’s stimulus representations, and, thus, govern learning. Here, we show that unexpected event signals predict subsequent changes in responses to expected and unexpected stimuli in individual neurons and distal apical dendrites that are tracked over a period of days. These findings were obtained by observing layer 2/3 and layer 5 pyramidal neurons in primary visual cortex of awake, behaving mice using two-photon calcium imaging. We found that many neurons in both layers 2/3 and 5 showed large differences between their responses to expected and unexpected events. These unexpected event signals also determined how the responses evolved over subsequent days, in a manner that was different between the somata and distal apical dendrites. This difference between the somata and distal apical dendrites may be important for hierarchical computation, given that these two compartments tend to receive bottom-up and top-down information, respectively. Together, our results provide novel evidence that the neocortex indeed instantiates a predictive hierarchical model in which unexpected events drive learning. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://gui.dandiarchive.org/\\#/dandiset/000037 * https://github.com/colleenjg/OpenScope_CA_Analysis * 3 https://github.com/colleenjg/cred_assign_stimuli * ↵8 www.computeontario.ca and www.computecanada.ca * 9 Although the corrected p-value is rounded here to 0.010, the actual value is < 0.01.

Cortical columns interact through dynamic routing of neuronal activity. Monitoring these interactions in animals performing a behavioral task as close as possible to real time will advance our understanding of cortical computation. We developed the Multiplane Mesoscope which combines three established concepts in microscopy: spatio-temporal multiplexing, remote focusing, and random-access mesoscopy. With the Multiplane Mesoscope, we recorded excitatory and inhibitory neuronal subpopulations simultaneously across two cortical areas and multiple cortical layers in behaving mice. In the context of a visual detection of change task, we used this novel platform to study cortical areas interactions and quantified the cell-type specific distribution of neuronal correlations across a set of visual areas and layers. We found that distinct cortical subnetworks represent expected and unexpected visual events. Our findings demonstrate that expectation violations modify signal routing across cortical columns and establish the Allen Brain Observatory Multiplane Mesoscope as a unique platform to study signal routing across connected pairs of cortical areas. Competing Interest Statement The dual-beam add-on module (D.T., N.O., J.L and P.S.) intellectual property has been licensed to Thorlabs. Inc., by the Allen Institute. Footnotes * the manuscript was restructured to decreased technical details of the microscope design and include additional analysis and figures

Abstract Despite significant progress in understanding neural coding, it remains unclear how the coordinated activity of large populations of neurons relates to what an observer actually perceives. Since neurophysiological differences must underlie differences among percepts, differentiation analysis—quantifying distinct patterns of neurophysiological activity—is an “inside out” approach that addresses this question. We used two-photon calcium imaging in mice to systematically survey stimulus-evoked neurophysiological differentiation in excitatory populations across 3 cortical layers (L2/3, L4, and L5) in each of 5 visual cortical areas (primary, lateral, anterolateral, posteromedial, and anteromedial) in response to naturalistic and phase-scrambled movie stimuli. We find that unscrambled stimuli evoke greater neurophysiological differentiation than scrambled stimuli specifically in L2/3 of the anterolateral and anteromedial areas, and that this effect is modulated by arousal state and locomotion. Contrariwise, decoding performance was far above chance and did not vary substantially across areas and layers. Differentiation also differed within the unscrambled stimulus set, suggesting that differentiation analysis may be used to probe the ethological relevance of individual stimuli. Competing Interest Statement The authors have declared no competing interest.