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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Contrast versus luminance in retina and visual cortex
Two interleaved stimulus sets were identical except for the background. In one, the flow stimuli background was the mid-gray of the interstimulus
interval (equal background, eqbg), leading to a change of 9-10% in the space-average luminance. In the other, the space-average
luminance of the entire stimulus field was adjusted to a constant (equal luminance, eqlum) within 0.5%; i.e., the background was slightly
lightened when the dots in the flow were dark, and darkened when the dots were bright.
Most cortical cells appeared to respond similarly to the two stimulus sets, as if stimulus structure mattered but not the background change, while
the responses of most retinal ganglion cells appeared to differ between the two conditions. Machine learning algorithms confirmed this
quantitatively. A manifold embedding of neurons to the two stimulus sets was constructed using diffusion maps. In this manifold, the responses
of the same cell to eqlum and eqbg stimuli were significantly closer to one another for V1 rather than for the retina. Geometrically, the median
ratio of the distance between the responses of each cell to the two stimulus sets as compared to the distance to the closest cell on the manifold
was 3.5 for V1 compared to 12.7 for retina. Topologically, the fraction of cells for which the responses of the same cell to the two stimulus sets
were connected in the diffusion map datagraph was 53% for V1 but only 9% for retina; when retina and cortex were co-embedded in the
manifold, these fractions were 44% and 6%.
While retina and cortex differ on average, it will be intriguing to determine whether particular classes of retinal cells behave more like V1
neurons, and vice versa.
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- Award ID(s):
- 1822650
- NSF-PAR ID:
- 10290875
- Date Published:
- Journal Name:
- Abstracts Society for Neuroscience
- ISSN:
- 0190-5295
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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