[DATALAD RUNCMD] Get latest validation reports with a summary

=== Do not change lines below ===
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 "exit": 0,
 "extra_inputs": [],
 "inputs": [],
 "outputs": [],
 "pwd": "."
}
^^^ Do not change lines above ^^^

.duct/logs/2024.11.11T06.54.34-3527_info.json

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.duct/logs/2024.11.11T06.54.34-3527_usage.json

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dandi-reports/001174/draft/metadata.json

@@ -52,8 +52,8 @@
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dandi-reports/001174/draft/metadata.yaml

@@ -14,8 +14,8 @@ assetsSummary:
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   - name: surgical technique
     schemaKey: MeasurementTechniqueType
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dandi-reports/001253/draft/linkml_validation_errs.json

@@ -0,0 +1,25 @@
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dandi-reports/001253/draft/linkml_validation_errs.yaml

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+    validator: type
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+  type: jsonschema validation

dandi-reports/001253/draft/metadata.json

@@ -0,0 +1,42 @@
+{
+  "id": "DANDI:001253/draft",
+  "url": "https://dandiarchive.org/dandiset/001253/draft",
+  "name": "An opponent striatal circuit for distributional reinforcement learning",
+  "access": [
+    {
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+  "license": [
+    "spdx:CC-BY-4.0"
+  ],
+  "version": "draft",
+  "citation": "Uchida, Naoshige (2024) An opponent striatal circuit for distributional reinforcement learning (Version draft) [Data set]. DANDI archive. https://dandiarchive.org/dandiset/001253/draft",
+  "schemaKey": "Dandiset",
+  "identifier": "DANDI:001253",
+  "repository": "https://dandiarchive.org",
+  "contributor": [
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+  "dateCreated": "2024-11-10T15:51:35.745964+00:00",
+  "description": "Machine learning research has achieved large performance gains on a wide range of tasks by expanding the learning target from mean rewards to entire probability distributions of rewards \u2014 an approach known as distributional reinforcement learning (RL). The mesolimbic dopamine system is thought to underlie RL in the mammalian brain by updating a representation of mean value in the striatum, but little is known about whether, where, and how neurons in this circuit encode information about higher-order moments of reward distributions. To fill this gap, we used high-density probes (Neuropixels) to record striatal activity from mice performing a classical conditioning task in which reward mean, reward variance, and stimulus identity were independently manipulated. In contrast to traditional RL accounts, we found robust evidence for abstract encoding of variance in the striatum. Remarkably, chronic ablation of dopamine inputs disorganized these distributional representations in the striatum without interfering with mean value coding. Two-photon calcium imaging and optogenetics revealed that the two major classes of striatal medium spiny neurons \u2014 D1 and D2 MSNs \u2014 contributed to this code by preferentially encoding the right and left tails of the reward distribution, respectively. We synthesize these findings into a new model of the striatum and mesolimbic dopamine that harnesses the opponency between D1 and D2 MSNs to reap the computational benefits of distributional RL.  ",
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dandi-reports/001253/draft/metadata.yaml

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+  probability distributions of rewards \u2014 an approach known as distributional
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+  RL in the mammalian brain by updating a representation of mean value in the striatum,
+  but little is known about whether, where, and how neurons in this circuit encode
+  information about higher-order moments of reward distributions. To fill this gap,
+  we used high-density probes (Neuropixels) to record striatal activity from mice
+  performing a classical conditioning task in which reward mean, reward variance,
+  and stimulus identity were independently manipulated. In contrast to traditional
+  RL accounts, we found robust evidence for abstract encoding of variance in the striatum.
+  Remarkably, chronic ablation of dopamine inputs disorganized these distributional
+  representations in the striatum without interfering with mean value coding. Two-photon
+  calcium imaging and optogenetics revealed that the two major classes of striatal
+  medium spiny neurons \u2014 D1 and D2 MSNs \u2014 contributed to this code by preferentially
+  encoding the right and left tails of the reward distribution, respectively. We synthesize
+  these findings into a new model of the striatum and mesolimbic dopamine that harnesses
+  the opponency between D1 and D2 MSNs to reap the computational benefits of distributional
+  RL.  "
+id: DANDI:001253/draft
+identifier: DANDI:001253
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dandi-reports/001253/draft/pydantic_validation_errs.json

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dandi-reports/001253/draft/pydantic_validation_errs.yaml

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