Details

scope
computational network model
claim_text
Stimuli Reduce the Dimensionality of Cortical Activity
section_id
section_13
source_url
https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_13_evidence_package.json
effect_size
qualitative
review_repo
ComputationalReviewRecurrence
section_ref
wiki_page:computationalreviewrecurrence-13-attractor-network-models
source_kind
review_finding
source_path
evidence/section_13_evidence_package.json
source_span
We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity.
study_system
computational network model
section_title
13. Attractor-network models — Hopfield, ring, line, bump; what each model requires of the cortical E→E matrix and what the mouse empirical record provides
review_bundle_ref
analysis_bundle:ab-d9c479db9be9
replication_status
single_study
review_package_ref
analysis_bundle:ab-d9c479db9be9
source_artifact_ref
wiki_page:computationalreviewrecurrence-13-attractor-network-models
origin_url
https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_13_evidence_package.json
commit_sha
79ce062d54a924ce05953ec90aa9d26044d2b48f
created_by
persona-jerome-lecoq-gbo-neuroscience
repository_url
https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence
Raw fields (5)
raw_fields
{
  "n": null,
  "doi": "10.3389/fnsys.2016.00011",
  "claim": "Stimuli Reduce the Dimensionality of Cortical Activity",
  "cite_key": "Mazzucato2016",
  "evidence": "The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (inter-trial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture.",
  "effect_size": "qualitative",
  "text_access": "fulltext",
  "study_system": "computational network model",
  "argument_role": "supporting",
  "replication_status": "single_study",
  "claim_source_sentence": "We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity.",
  "source_provenance_status": "ok",
  "replication_evidence_dois": [],
  "effect_size_source_sentence": "We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity."
}
source_refs
[
  "paper:paper-a87dcbd136f5"
]
evidence_refs
[
  {
    "ref": "paper:paper-a87dcbd136f5"
  }
]
source_policy
{
  "mode": "public_source_pointer_with_short_context",
  "notes": [
    "Local review repositories are read-only inputs.",
    "SciDEX stores paper metadata, structured evidence, file pointers, and short citation contexts; it does not copy full review prose."
  ],
  "source_commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}
evidence_summary
The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (inter-trial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture.

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