#populationactivity — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #populationactivity, aggregated by home.social.
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Easy false alarms still looked neurally like "correct rejections", while difficult false alarms shifted toward "hit-like" #PopulationActivity, suggesting #PMC encodes what the animal believes it heard rather than simply whether it licked.
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Easy false alarms still looked neurally like "correct rejections", while difficult false alarms shifted toward "hit-like" #PopulationActivity, suggesting #PMC encodes what the animal believes it heard rather than simply whether it licked.
🧵2/2
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Easy false alarms still looked neurally like "correct rejections", while difficult false alarms shifted toward "hit-like" #PopulationActivity, suggesting #PMC encodes what the animal believes it heard rather than simply whether it licked.
🧵2/2
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Easy false alarms still looked neurally like "correct rejections", while difficult false alarms shifted toward "hit-like" #PopulationActivity, suggesting #PMC encodes what the animal believes it heard rather than simply whether it licked.
🧵2/2
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Easy false alarms still looked neurally like "correct rejections", while difficult false alarms shifted toward "hit-like" #PopulationActivity, suggesting #PMC encodes what the animal believes it heard rather than simply whether it licked.
🧵2/2
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🧠 New paper by Clark et al. (2025) shows that the #dimensionality of #PopulationActivity in #RNN can be explained by just two #connectivity parameters: effective #CouplingStrength and effective #rank. Uses networks with rapidly decaying singular value spectra and structured overlaps between left and right singular vectors. Could be useful for interpreting large scale population recordings and connectome data I guess:
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🧠 New paper by Clark et al. (2025) shows that the #dimensionality of #PopulationActivity in #RNN can be explained by just two #connectivity parameters: effective #CouplingStrength and effective #rank. Uses networks with rapidly decaying singular value spectra and structured overlaps between left and right singular vectors. Could be useful for interpreting large scale population recordings and connectome data I guess:
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🧠 New paper by Clark et al. (2025) shows that the #dimensionality of #PopulationActivity in #RNN can be explained by just two #connectivity parameters: effective #CouplingStrength and effective #rank. Uses networks with rapidly decaying singular value spectra and structured overlaps between left and right singular vectors. Could be useful for interpreting large scale population recordings and connectome data I guess:
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🧠 New paper by Clark et al. (2025) shows that the #dimensionality of #PopulationActivity in #RNN can be explained by just two #connectivity parameters: effective #CouplingStrength and effective #rank. Uses networks with rapidly decaying singular value spectra and structured overlaps between left and right singular vectors. Could be useful for interpreting large scale population recordings and connectome data I guess:
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🧠 New paper by Clark et al. (2025) shows that the #dimensionality of #PopulationActivity in #RNN can be explained by just two #connectivity parameters: effective #CouplingStrength and effective #rank. Uses networks with rapidly decaying singular value spectra and structured overlaps between left and right singular vectors. Could be useful for interpreting large scale population recordings and connectome data I guess: