neurosock
@neurosock.bsky.social
#BrainChips monthly recap. I make #neuro papers easy to understand. To make #Neuralink possible. Neuro PhD. AI🤖ML👾Data Sci 📊 Monkeys🐵Future🚀Cyberpunk⚡🦾🌌
Thanks😊. I enjoy making this little silly drawings. Helps me understand the paper much more and remember better.
October 31, 2025 at 10:12 PM
Thanks😊. I enjoy making this little silly drawings. Helps me understand the paper much more and remember better.
Thanks. Love your handle BTW!
October 30, 2025 at 3:55 PM
Thanks. Love your handle BTW!
If you like this, and think I should keep doing these every week, hit a like on the first post!
It motivates me to spend 5-6 hours a week preparing each summary 😇.
It motivates me to spend 5-6 hours a week preparing each summary 😇.
October 30, 2025 at 11:36 AM
If you like this, and think I should keep doing these every week, hit a like on the first post!
It motivates me to spend 5-6 hours a week preparing each summary 😇.
It motivates me to spend 5-6 hours a week preparing each summary 😇.
Disclaimers:
1) This is a simplification from a very complex paper! Sorry if I omitted some details, it is all for clarity 😸
2) As any paper, this study has to be further REPLICATED to know if its true!
1) This is a simplification from a very complex paper! Sorry if I omitted some details, it is all for clarity 😸
2) As any paper, this study has to be further REPLICATED to know if its true!
October 30, 2025 at 11:36 AM
Disclaimers:
1) This is a simplification from a very complex paper! Sorry if I omitted some details, it is all for clarity 😸
2) As any paper, this study has to be further REPLICATED to know if its true!
1) This is a simplification from a very complex paper! Sorry if I omitted some details, it is all for clarity 😸
2) As any paper, this study has to be further REPLICATED to know if its true!
Limitations:
The study used head-fixed mice, which restricts their natural movement repertoire.
The findings must also be tested in more complex, non-Pavlovian tasks.
The study used head-fixed mice, which restricts their natural movement repertoire.
The findings must also be tested in more complex, non-Pavlovian tasks.
October 30, 2025 at 11:36 AM
Limitations:
The study used head-fixed mice, which restricts their natural movement repertoire.
The findings must also be tested in more complex, non-Pavlovian tasks.
The study used head-fixed mice, which restricts their natural movement repertoire.
The findings must also be tested in more complex, non-Pavlovian tasks.
Implications for neuro:
This challenges a foundational dogma about dopamine and learning.
It shifts focus from abstract cognitive states to the concrete physical actions and motor vigor that DA might directly encode.
This challenges a foundational dogma about dopamine and learning.
It shifts focus from abstract cognitive states to the concrete physical actions and motor vigor that DA might directly encode.
October 30, 2025 at 11:36 AM
Implications for neuro:
This challenges a foundational dogma about dopamine and learning.
It shifts focus from abstract cognitive states to the concrete physical actions and motor vigor that DA might directly encode.
This challenges a foundational dogma about dopamine and learning.
It shifts focus from abstract cognitive states to the concrete physical actions and motor vigor that DA might directly encode.
DA is an adaptive signal that switches its function in real-time.
Upon reward delivery, it stops predicting force and starts predicting the *rate of licking* (Fig 9h, 9j).
Upon reward delivery, it stops predicting force and starts predicting the *rate of licking* (Fig 9h, 9j).
October 30, 2025 at 11:36 AM
DA is an adaptive signal that switches its function in real-time.
Upon reward delivery, it stops predicting force and starts predicting the *rate of licking* (Fig 9h, 9j).
Upon reward delivery, it stops predicting force and starts predicting the *rate of licking* (Fig 9h, 9j).
Optogenetically stimulating DA neurons in place of reward was *not sufficient* for learning.
Inhibiting DA neurons during the task *did not impair* learning (Fig 10b, 10i).
Inhibiting DA neurons during the task *did not impair* learning (Fig 10b, 10i).
October 30, 2025 at 11:36 AM
Optogenetically stimulating DA neurons in place of reward was *not sufficient* for learning.
Inhibiting DA neurons during the task *did not impair* learning (Fig 10b, 10i).
Inhibiting DA neurons during the task *did not impair* learning (Fig 10b, 10i).
Changes in DA firing due to reward magnitude, probability, and omission were all explained by parallel changes in the *force* the mice exerted (Figs 4, 5).
October 30, 2025 at 11:36 AM
Changes in DA firing due to reward magnitude, probability, and omission were all explained by parallel changes in the *force* the mice exerted (Figs 4, 5).
This force-tuning was independent of reward, appearing during spontaneous movements.
It even held true during an aversive air puff, proving it's not about "reward" (Fig 3e, 3h).
It even held true during an aversive air puff, proving it's not about "reward" (Fig 3e, 3h).
October 30, 2025 at 11:36 AM
This force-tuning was independent of reward, appearing during spontaneous movements.
It even held true during an aversive air puff, proving it's not about "reward" (Fig 3e, 3h).
It even held true during an aversive air puff, proving it's not about "reward" (Fig 3e, 3h).
This is how we learned that from their results:
They identified two distinct DA neuron types: "Forward" and "Backward" populations.
These cells fire to drive movement in a specific direction (Fig 1e, 1h, 1k).
They identified two distinct DA neuron types: "Forward" and "Backward" populations.
These cells fire to drive movement in a specific direction (Fig 1e, 1h, 1k).
October 30, 2025 at 11:36 AM
This is how we learned that from their results:
They identified two distinct DA neuron types: "Forward" and "Backward" populations.
These cells fire to drive movement in a specific direction (Fig 1e, 1h, 1k).
They identified two distinct DA neuron types: "Forward" and "Backward" populations.
These cells fire to drive movement in a specific direction (Fig 1e, 1h, 1k).
But how?
The breakthrough was using highly sensitive force sensors on head-fixed mice.
This allowed them to measure subtle, continuous movements, not just discrete licks or port entries.
The breakthrough was using highly sensitive force sensors on head-fixed mice.
This allowed them to measure subtle, continuous movements, not just discrete licks or port entries.
October 30, 2025 at 11:36 AM
But how?
The breakthrough was using highly sensitive force sensors on head-fixed mice.
This allowed them to measure subtle, continuous movements, not just discrete licks or port entries.
The breakthrough was using highly sensitive force sensors on head-fixed mice.
This allowed them to measure subtle, continuous movements, not just discrete licks or port entries.
But why?
The RPE hypothesis has been a cornerstone of neuroscience, explaining associative learning.
But it couldn't explain why DA activity also correlates with movement kinematics or aversive events.
The RPE hypothesis has been a cornerstone of neuroscience, explaining associative learning.
But it couldn't explain why DA activity also correlates with movement kinematics or aversive events.
October 30, 2025 at 11:36 AM
But why?
The RPE hypothesis has been a cornerstone of neuroscience, explaining associative learning.
But it couldn't explain why DA activity also correlates with movement kinematics or aversive events.
The RPE hypothesis has been a cornerstone of neuroscience, explaining associative learning.
But it couldn't explain why DA activity also correlates with movement kinematics or aversive events.
Here is another way to put it: We thought DA was the GPS, telling the brain when to change direction when we did a wrong turn.
This paper suggests DA is the *accelerator*, just making the car go.
This paper suggests DA is the *accelerator*, just making the car go.
October 30, 2025 at 11:36 AM
Here is another way to put it: We thought DA was the GPS, telling the brain when to change direction when we did a wrong turn.
This paper suggests DA is the *accelerator*, just making the car go.
This paper suggests DA is the *accelerator*, just making the car go.
How does reward probability change the DA signal?
RPE view: DA activity scales with reward probability, encoding the *strength* of the prediction.
New view: Probability changes the animal's *effort*, and the DA signal simply tracks that performance.
RPE view: DA activity scales with reward probability, encoding the *strength* of the prediction.
New view: Probability changes the animal's *effort*, and the DA signal simply tracks that performance.
October 30, 2025 at 11:36 AM
How does reward probability change the DA signal?
RPE view: DA activity scales with reward probability, encoding the *strength* of the prediction.
New view: Probability changes the animal's *effort*, and the DA signal simply tracks that performance.
RPE view: DA activity scales with reward probability, encoding the *strength* of the prediction.
New view: Probability changes the animal's *effort*, and the DA signal simply tracks that performance.
Why does a bigger reward cause a bigger DA spike?
RPE view: A larger reward causes a larger DA spike because it's a bigger "positive error."
New view: A larger reward makes the mouse push *harder*, and the DA spike just tracks that *vigor*.
RPE view: A larger reward causes a larger DA spike because it's a bigger "positive error."
New view: A larger reward makes the mouse push *harder*, and the DA spike just tracks that *vigor*.
October 30, 2025 at 11:36 AM
Why does a bigger reward cause a bigger DA spike?
RPE view: A larger reward causes a larger DA spike because it's a bigger "positive error."
New view: A larger reward makes the mouse push *harder*, and the DA spike just tracks that *vigor*.
RPE view: A larger reward causes a larger DA spike because it's a bigger "positive error."
New view: A larger reward makes the mouse push *harder*, and the DA spike just tracks that *vigor*.
Why does DA firing "dip" when a reward is omitted?
RPE view: The famous "dip" in DA when a reward is omitted is a negative prediction error.
New view: The dip simply reflects the animal *abruptly stopping* its forward movement.
RPE view: The famous "dip" in DA when a reward is omitted is a negative prediction error.
New view: The dip simply reflects the animal *abruptly stopping* its forward movement.
October 30, 2025 at 11:36 AM
Why does DA firing "dip" when a reward is omitted?
RPE view: The famous "dip" in DA when a reward is omitted is a negative prediction error.
New view: The dip simply reflects the animal *abruptly stopping* its forward movement.
RPE view: The famous "dip" in DA when a reward is omitted is a negative prediction error.
New view: The dip simply reflects the animal *abruptly stopping* its forward movement.
In RPE, the DA signal shifts from reward to cue 🔔 over time.
Why does the DA signal "move" to the cue?
RPE view: This helps an animal learn what things are important.
New view: the signal shifts because the animal's *action* (pushing forward) shifts to the cue.
Why does the DA signal "move" to the cue?
RPE view: This helps an animal learn what things are important.
New view: the signal shifts because the animal's *action* (pushing forward) shifts to the cue.
October 30, 2025 at 11:36 AM
In RPE, the DA signal shifts from reward to cue 🔔 over time.
Why does the DA signal "move" to the cue?
RPE view: This helps an animal learn what things are important.
New view: the signal shifts because the animal's *action* (pushing forward) shifts to the cue.
Why does the DA signal "move" to the cue?
RPE view: This helps an animal learn what things are important.
New view: the signal shifts because the animal's *action* (pushing forward) shifts to the cue.
Let's unpack this:
The classical RPE model says DA neurons encode the *difference* between expected and actual rewards.
A surprise spikes DA activity, while disappointment causes a dip.
This helps a mouse 🐭 learn what to pay attention to.
The classical RPE model says DA neurons encode the *difference* between expected and actual rewards.
A surprise spikes DA activity, while disappointment causes a dip.
This helps a mouse 🐭 learn what to pay attention to.
October 30, 2025 at 11:36 AM
Let's unpack this:
The classical RPE model says DA neurons encode the *difference* between expected and actual rewards.
A surprise spikes DA activity, while disappointment causes a dip.
This helps a mouse 🐭 learn what to pay attention to.
The classical RPE model says DA neurons encode the *difference* between expected and actual rewards.
A surprise spikes DA activity, while disappointment causes a dip.
This helps a mouse 🐭 learn what to pay attention to.
This paper says that all those observations we thought were related to the RL inspired Reward Prediction Error (RPE), are telling us something else.
Although mathematically beautiful, this theory might have nothing to do with the brain reality.
Or at least not with DA.
Although mathematically beautiful, this theory might have nothing to do with the brain reality.
Or at least not with DA.
October 30, 2025 at 11:36 AM
This paper says that all those observations we thought were related to the RL inspired Reward Prediction Error (RPE), are telling us something else.
Although mathematically beautiful, this theory might have nothing to do with the brain reality.
Or at least not with DA.
Although mathematically beautiful, this theory might have nothing to do with the brain reality.
Or at least not with DA.
For years, neuroscientists have scratched their heads about dopamine because it seems to do so much.
This paper might finally explain what have we been doing wrong all the time in experiments.
It also matches alternative evidence that has been accumulating over the years.
This paper might finally explain what have we been doing wrong all the time in experiments.
It also matches alternative evidence that has been accumulating over the years.
October 30, 2025 at 11:36 AM
For years, neuroscientists have scratched their heads about dopamine because it seems to do so much.
This paper might finally explain what have we been doing wrong all the time in experiments.
It also matches alternative evidence that has been accumulating over the years.
This paper might finally explain what have we been doing wrong all the time in experiments.
It also matches alternative evidence that has been accumulating over the years.
The canonical view is DA signals RPE, a "teaching signal" for learning.
This paper argues we were wrong all the time: DA is a "performance signal" that controls the vigor and direction of movement and it is only indirectly related to learning by controlling effort.
This paper argues we were wrong all the time: DA is a "performance signal" that controls the vigor and direction of movement and it is only indirectly related to learning by controlling effort.
October 30, 2025 at 11:36 AM
The canonical view is DA signals RPE, a "teaching signal" for learning.
This paper argues we were wrong all the time: DA is a "performance signal" that controls the vigor and direction of movement and it is only indirectly related to learning by controlling effort.
This paper argues we were wrong all the time: DA is a "performance signal" that controls the vigor and direction of movement and it is only indirectly related to learning by controlling effort.