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Record W1997854340 · doi:10.1186/1471-2202-12-s1-p33

Coding motion direction by action potential patterns

2011· article· en· W1997854340 on OpenAlexaff
Navid Khosravi, Eric S. Fortune, Maurice J. Chacron

Bibliographic record

VenueBMC Neuroscience · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicFish biology, ecology, and behavior
Canadian institutionsMcGill University
Fundersnot available
KeywordsCoding (social sciences)Action (physics)Computer scienceMotion (physics)Cognitive scienceNeuroscienceArtificial intelligencePsychologyMathematicsStatisticsPhysics

Abstract

fetched live from OpenAlex

Directional selectivity, in which neurons respond more strongly to an object moving in a given direction (‘preferred’) than to the same object moving in the opposite direction (‘null’), is an critical computation achieved in brain circuits as it is a correlate of movement perception [1]. Previous studies have almost exclusively characterized directional selectivity by computing the firing rate, which weights each action potential equally. However, neuronal spike trains frequently contain more or less stereotyped action potential patterns such a bursts (i.e. packets of action potentials followed by quiescence). A growing body of evidence suggests that these patterns, rather than being just a collection of action potentials, can be considered as a unitary event that encodes specific stimulus features (see [2] for review). However, as these studies used stationary stimuli, the putative role of action potential patterns in coding for movement direction remains largely a mystery to this day. To investigate this interesting problem, we performed in vivo recordings from direction selective midbrain electrosensory neurons in the weakly electric fish Apteronotus leptorhynchus in response to moving objects. We found that most neurons responded to these objects with a combination of bursts and isolated spikes. Segregating the spike train into bursts and isolated spikes revealed that bursts could carry specific information about movement direction. Indeed, in some neurons, we found that bursts were selectively elicited in the preferred direction while isolated spikes were elicited equally in both directions. However, in other neurons, we found that bursts were mostly elicited in one movement direction while isolated spikes were preferentially elicited in the opposite direction. However, when looking at the full spike train, these neurons displayed little or no directional selectivity. In order to explain these surprising results, we built a mathematical model incorporating the previously established mechanisms that generate directional selectivity in TS neurons, namely the generation of a directional bias by in which different time constants of Short Term Depression (STD) across the receptive field and nonlinear integration of these inputs by a subthreshold T-type calcium conductance [3,4]. We found STD time constant ratio values for which our model could reproduce both experimentally observed neuron types. Our results show that action potential patterns can be used to code for movement direction and raise the interesting possibility that bursts and isolated spikes can be used to distinguish movement direction simultaneously in individual neurons. Moreover, our results show that neurons that do not display directional selectivity when their full spike trains are considered can actually display directional selectivity when action potential patterns such as bursts and isolated spikes are considered instead.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.261
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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