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We have created encoding manifolds to reveal the overall responses of a brain area to a variety of stimuli. Encoding manifolds organize response properties globally: each point on an encoding manifold is a neuron, and nearby neurons respond similarly to the stimulus ensemble in time. We previously found, using a large stimulus ensemble including optic flows, that encoding manifolds for the retina were highly clustered, with each cluster corresponding to a different ganglion cell type. In contrast, the topology of the V1 manifold was continuous. Now, using responses of individual neurons from the Allen Institute Visual Coding-Neuropixels dataset in the mouse, we infer encoding manifolds for V1 and for five higher cortical visual areas (VISam, VISal, VISpm, VISlm, and VISrl). We show here that the encoding manifold topology computed only from responses to various grating stimuli is also continuous, not only for V1 but also for the higher visual areas, with smooth coordinates spanning it that include, among others, orientation selectivity and firing-rate magnitude. Surprisingly, the encoding manifold for gratings also provides information about natural scene responses. To investigate whether neurons respond more strongly to gratings or natural scenes, we plot the log ratio of natural scene responses to grating responses (mean firing rates) on the encoding manifold. This reveals a global coordinate axis organizing neurons' preferences between these two stimuli. This coordinate is orthogonal (i.e., uncorrelated) to that organizing firing rate magnitudes in VISp. Analyzing layer responses, a preference for gratings is concentrated in layer 6, whereas preference for natural scenes tends to be higher in layers 2/3 and 4. We also find that preference for natural scenes dominates the responses of neurons that prefer low (0.02 cpd) and high (0.32 cpd) spatial frequencies, rather than intermediate ones (0.04 to 0.16 cpd). Conclusion: while gratings seem limited and natural scenes unconstrained, machine learning algorithms can reveal subtle relationships between them beyond linear techniques.
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Toward a manifold encoding neural responses
Understanding circuit properties from physiological data presents two challenges: (i) recordings do not reveal connectivity, and (ii) stimuli only exercise circuits to a limited extent. We address these challenges for the mouse visual system with a novel neural manifold obtained using unsupervised algorithms. Each point in our manifold is a neuron; nearby neurons respond similarly in time to similar parts of a stimulus ensemble. This ensemble includes drifting gratings and flows, i.e., patterns resembling what a mouse would “see” running through fields. Regarding (i), our manifold differs from the standard practice in computational neuroscience: embedding trials in neural coordinates. Topology matters: we infer that, if the circuit consists of separate components, the manifold is discontinuous (illustrated with retinal data). If there is significant overlap between circuits, the manifold is nearly-continuous (cortical data). Regarding (ii), most of the cortical manifold is not activated with conventional gratings, despite their prominence in laboratory settings. Our manifold suggests organizing cortical circuitry by a few specialized circuits for specific members of the stimulus ensemble, together with circuits involving ‘multi-stimuli’-responding neurons. To approach real circuits, local neighborhoods in the manifold are identified with actual circuit components. For retinal data, we show these components correspond to distinct ganglion cell types by their mosaic-like receptive field organization, while for cortical data, neighborhoods organize neurons by type (excitatory/inhibitory) and anatomical layer. In summary: the topology of neural organization reflects well the underlying anatomy and physiology of the retina and the visual cortex.
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- Award ID(s):
- 1822650
- PAR ID:
- 10462537
- Date Published:
- Journal Name:
- MODVIS Workshop on Computational Models in Vision
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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