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#hippocampalreplay

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@LMPrida @perpl_lab @acnavasolive @saman @cogneurophys 🥳so happy this is out! I wasn’t sure how the ML models would perform, given the frequency shift in monkey (and human) SWRs, and the larger post-ripple wave compared to those in mice. But, a few models rose to the top straightaway and performed even better with modest “top up” training! 📈Now I’m curious how the #opensource toolbox will perform on human hippocampal data. ……..➡️👩‍🔬both senior authors and one lead author were #womenInSTEM working across two continents, and the original multi-model testing was formulated as a hackathon that included #diverserepresentation ! #neuroai #ann #cnn #neuroscience #hippocampus #HippocampalReplay #internationalwomensday

I have presented this poster on #HippocampalReplay at #SFN23 !

I'm also uploading the poster as an image here, and it's on the SFN website: cattendee.abstractsonline.com/

Little summary below:

Our goal was to investigate if replay of hippocampal #PlaceCells was indeed reflecting immediate #SpatialPlanning.

The existing literature is a little unclear: some studies find 'planning replay' and some do not. Most of the time, planning is not dissociated from reward consumption in these studies as they can generally be simplified to an alternation between two rewarded locations. In our case, we separate the location of planning from the location of reward and focus on replay happening at the planning time.

At the location of planning, we find that:

1) there is actually almost no replay at the time of planning
2) the rare 'start replay' events do NOT over-represent the future trajectory, or even the goal
3) the rare 'start replay' events do not differ for successful vs unsuccessful planning.

At the goal location, we find that:

4) Many replays occur at the goal, even before the reward delivery!
5) 'goal replays' strongly over-represent the goal (either before or after reward delivery)
6) 'goal replays' do not over-represent the current trajectory compared to the alternative trajectory, or even the optimal path if the less-optimal path was taken
7) 'goal replays' were strongly affected by errors and were quasi-absent during error trials (choosing an unrewarded end box)

In conclusion:

Reward (received or expected), but not planning, drives hippocampal replay!

Let me know if you have any questions after looking at the poster :D

Edit: updated because this is in the past now (but hoping to publish it soon!)

I’ll be presenting a poster about #HippocampalReplay at #SFN2023!

The session: abstractsonline.com/pp8/#!/108

The poster: Hippocampal replay does not reflect trajectory planning in a spatial planning task

The date: November 15, 2023, 8:00 AM - 12:00 PM

The location: Walter E. Washington Convention center, Washington D.C., WCC Halls A-C

Will you be there?!? There is also an online version!

Teaser video ⬇️
#Hippocampus #PlaceCells #FlexibleNavigation #NeuroRat #Neuroscience
1/2

#Introduction of my #Neuroscience-oriented account! 🧠

I am a postdoctoral researcher investigating #NeuroRats 🐀​ doing flexible #Navigation 🗺️​ and how their #Hippocampus might support this fascinating ability.
Is it #PlaceCells?
Is it #HippocampalReplay?
​Is it #SplitterCells?
Is it everything, everywhere (... yes, yes, all at once)?

This account will post, boost and follow specifically neuroscience content, don't hesitate to tag it when relevant.

Followers, please tell us about yourself under my pinned tweet!

New computational model of #HippocampalReplay:
A recurrent network model of planning explains hippocampal replay and human behavior

By Jensen et al.
It looks super interesting! But let me tell you: this is not what hippocampal replay does in rodents… replay is not for planning.🤷‍♂️

bioRxivA recurrent network model of planning explains hippocampal replay and human behaviorWhen interacting with complex environments, humans can rapidly adapt their behavior to changes in task or context. To facilitate this adaptation, we often spend substantial periods of time contemplating possible futures before acting. For such planning to be rational, the benefits of planning to future behavior must at least compensate for the time spent thinking. Here we capture these features of human behavior by developing a neural network model where not only actions, but also planning, are controlled by prefrontal cortex. This model consists of a meta-reinforcement learning agent augmented with the ability to plan by sampling imagined action sequences drawn from its own policy, which we refer to as ‘rollouts’. Our results demonstrate that this agent learns to plan when planning is beneficial, explaining the empirical variability in human thinking times. Additionally, the patterns of policy rollouts employed by the artificial agent closely resemble patterns of rodent hippocampal replays recently recorded in a spatial navigation task, in terms of both their spatial statistics and their relationship to subsequent behavior. Our work provides a new theory of how the brain could implement planning through prefrontal-hippocampal interactions, where hippocampal replays are triggered by – and in turn adaptively affect – prefrontal dynamics. ### Competing Interest Statement The authors have declared no competing interest.