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Fabrizio Musacchio
@pixeltracker@sigmoid.social  ·  activity timestamp 2 weeks ago

🧠 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:

🌍 https://doi.org/10.1103/2jt7-c8cq

#CompNeuro #NeuralDynamics #Connectome

Connectivity Structure and Dynamics of Nonlinear Recurrent Neural Networks

Fig. 2.
Schematic of the random-mode model. Upper: couplings 
J
 are generated as a sum of outer products, 
ℓ
a
r
a
T
, with component strengths 
D
a
. Lower: the two-point function
C
⋆
ϕ
(
τ
)
 and four-point function 
Ψ
⋆
ϕ
(
τ
)
 are calculated in terms of the statistics of 
D
a
. The two-point function depends only on the effective gain 
g
eff
, while the four-point function depends on both 
g
eff
 and 
PR
D
, the effective dimension of the connectivity determined by the 
D
a
 distribution.
Fig. 2. Schematic of the random-mode model. Upper: couplings J are generated as a sum of outer products, ℓ a r a T , with component strengths D a . Lower: the two-point function C ⋆ ϕ ( τ ) and four-point function Ψ ⋆ ϕ ( τ ) are calculated in terms of the statistics of D a . The two-point function depends only on the effective gain g eff , while the four-point function depends on both g eff and PR D , the effective dimension of the connectivity determined by the D a distribution.
Fig. 2. Schematic of the random-mode model. Upper: couplings J are generated as a sum of outer products, ℓ a r a T , with component strengths D a . Lower: the two-point function C ⋆ ϕ ( τ ) and four-point function Ψ ⋆ ϕ ( τ ) are calculated in terms of the statistics of D a . The two-point function depends only on the effective gain g eff , while the four-point function depends on both g eff and PR D , the effective dimension of the connectivity determined by the D a distribution.
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Ross Gayler boosted
jnpn
@jnpn@mastodon.social  ·  activity timestamp 4 weeks ago

https://www.youtube.com/watch?v=PymMVna6T8A

Anne Churchland (CSHL) 1: How do brains decide?

#neurology #brain #connectome #neuroscience #decision

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jnpn
@jnpn@mastodon.social  ·  activity timestamp 4 weeks ago

https://www.youtube.com/watch?v=PymMVna6T8A

Anne Churchland (CSHL) 1: How do brains decide?

#neurology #brain #connectome #neuroscience #decision

jnpn
@jnpn@mastodon.social replied  ·  activity timestamp 4 weeks ago

https://www.youtube.com/watch?v=cFpGbmqXWOI

Anne Churchland (CSHL) 2: Connecting movement & ...

#neurology #neuroscience #brain #connectome

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jnpn
@jnpn@mastodon.social  ·  activity timestamp 4 weeks ago

https://www.youtube.com/watch?v=PymMVna6T8A

Anne Churchland (CSHL) 1: How do brains decide?

#neurology #brain #connectome #neuroscience #decision

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Björn Brembs
@brembs@mastodon.social  ·  activity timestamp last month

The #connectome may be important, but it does not seem to help understand neuronal activity patterns:

"Comprehensive neuronal identification enabled us to examine the relationship between whole-brain activity and the connectome but we found no strong correlations between them."

https://doi.org/10.1016/j.cell.2020.12.012

Replication: https://doi.org/10.1016/j.cub.2022.06.039

The structure of a nervous system is one thing, but its relation to NS function seems to be much more tenuous than we may have hoped for.

#neuroscience

A set of hub neurons and non-local connectivity features support global brain dynamics in C. elegans

NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in C. elegans

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Björn Brembs
@brembs@mastodon.social  ·  activity timestamp last month

How much can a #connectome tell us about the activity of neurons? It seems not all that much:

"neuronal perturbation often prompted responses from cells that had no anatomical connections to the target cell"

https://www.thetransmitter.org/connectome/worms-help-untangle-longstanding-brain-structure-function-mystery/

#neuroscience #biology

The Transmitter: Neuroscience News and Perspectives

Worms help untangle brain structure/function mystery

The synaptic connectome of most animals bears little resemblance to functional brain maps, but it can still predict neuronal activity, according to two preprints that tackle the puzzle in C. elegans.
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