🌟 New Research Alert! 🌟
Excited to share our latest work (accepted to NeurIPS2024) on understanding working memory in multi-task RNN models using naturalistic stimuli!: with @takuito.bsky.social and @bashivan.bsky.social
#tweeprint below:

📢Another #tweeprint incoming 📢

We talked about how task-driven deep CNNs are a good model of monkey V1 - but these models have struggled to predict neural activity in visual cortex of the mouse.

What gives?

This is work in collab with...

📢 New #NeuroAI #tweeprint 📢
bionicvisionlab.org/publications/2023-05-bra...

What makes a good model of V1 activity?

Neuroscientists know it's all about center-surround antagonism, divisive normalization, and cortical magnification. However, top @brain_score models...

#BioImageAnalysis #tweeprint

Just published in @JOSS_TheOJ, FijiRelax, a #Fiji plugin for quantitative water imaging in living tissue with magnetic resonance relaxometry.
More in this mini-🧵 1/

TLDR: paper is there: https://joss.theoj.org/papers/10.21105/joss.04981

Tweeprint time!

Phenotrack3D, an automatic processing pipeline for high-throughput 3D+t reconstruction of #maize architecture! Just published in @PlantMethods by Benoît Daviet @UMR_LEPSE and @BioImage_Ro @AgapInstitut #PhenomenTeam

More in this #tweeprint thread! (1/9)

Tweeprint time!

Just published in @PlantMethods, RootSystemTracker, an automatic processing pipeline for high-throughput 2D+t reconstruction of root system architecture.

More in this #tweeprint thread! (1/13)

https://youtu.be/SWEqnnOhIOU

Time for my first #Bioimage #Tweeprint!

In time-lapse/multimodal imaging, objects move/deform. Images series must be aligned accurately to enable analysis. Hard in 2D, terrific in 3D!

This thread covers Fijiyama, a popular #Fiji plugin for image registration in 3D+t🧵
(1/8)

A large majority of awake hippocampal sharp-wave ripples feature spatial trajectories with momentum During periods of rest, hippocampal place cells feature bursts of activity called sharp-wave ripples (SWRs). Heuristic approaches to their analysis have revealed that a small fraction of SWRs appear to “simulate” trajectories through the environment—called awake hippocampal replay—while the functional role of a majority of these SWRs remains unclear. Applying a novel probabilistic approach to characterize the spatio-temporal dynamics embedded in SWRs, we instead show that almost all SWRs of foraging rodents simulate such trajectories through the environment. Furthermore, these trajectories feature momentum, that is, inertia in their velocities, that mirrors the animals’ natural movement. This stands in contrast to replay events during sleep which seem to follow Brownian motion without such momentum. Lastly, interpreting the replay trajectories in the context of navigational planning revealed that similar past analyses were biased by the heuristic SWR sub-selection. Overall, our approach provides a more complete characterization of the spatio-temporal dynamics within SWRs, highlights qualitative differences between sleep and awake replay, and ought to support future, more detailed, and less biased analysis of the role of awake replay in navigational planning. ### Competing Interest Statement The authors have declared no competing interest.

#tweeprint time! Check out the new work of Emma Krause and myself on showing that almost all awake hippocampal sharp-wave ripples (SWRs) appear to encode trajectories with momentum through the environment: www.biorxiv.org/content/10.1101/2021.05.... 1/

For a brief summary of the work check the #tweeprint: 2/

Adaptation Properties Allow Identification of Optimized Neural Codes The adaptation of neural codes to the statistics of their environment is well captured by efficient coding approaches. Here we solve an inverse problem: characterizing the objective and constraint functions that efficient codes appear to be optimal for, on the basis of how they adapt to different stimulus distributions. We formulate a general efficient coding problem, with flexible objective and constraint functions and minimal parametric assumptions. Solving special cases of this model, we provide solutions to broad classes of Fisher information-based efficient coding problems, generalizing a wide range of previous results. We show that different objective function types impose qualitatively different adaptation behaviors, while constraints enforce characteristic deviations from classic efficient coding signatures. Despite interaction between these effects, clear signatures emerge for both unconstrained optimization problems and information-maximizing objective functions. Asking for a fixed-point of the neural code adaptation, we find an objective-independent characterization of constraints on the neural code. We use this result to propose an experimental paradigm that can characterize both the objective and constraint functions that an observed code appears to be optimized for.

#tweeprint time! Luke Rast and I are looking at efficient coding and the adaptation of optimized neural codes to changing distributions of the encoded stimulus. Get the pre-print at https://arxiv.org/abs/2010.14699

Optimal policy for attention-modulated decisions explains human fixation behavior Traditional accumulation-to-bound decision-making models assume that all choice options are processed simultaneously with equal attention. In real life decisions, however, humans tend to alternate their visual fixation between individual items in order to efficiently gather relevant information [[46][1], [23][2], [21][3], [12][4], [15][5]]. These fixations also causally affect one’s choices, biasing them toward the longer-fixated item [[38][6], [2][7], [25][8]]. We derive a normative decision-making model in which fixating a choice item boosts information about that item. In contrast to previous models [[25][8], [39][9]], we assume that attention enhances the reliability of information rather than its magnitude, consistent with neurophysiological findings [[3][10], [13][11], [29][12], [45][13]]. Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation patterns and fixation-related choice biases seen in human decision-makers, and provides a Bayesian computational rationale for the fixation bias. This insight led to additional behavioral predictions that we confirmed in human behavioral data. Finally, we explore the consequences of changing the relative allocation of cognitive resources to the attended versus the unattended item, and show that decision performance is benefited by a more balanced spread of cognitive resources. ### Competing Interest Statement The authors have declared no competing interest. [1]: #ref-46 [2]: #ref-23 [3]: #ref-21 [4]: #ref-12 [5]: #ref-15 [6]: #ref-38 [7]: #ref-2 [8]: #ref-25 [9]: #ref-39 [10]: #ref-3 [11]: #ref-13 [12]: #ref-29 [13]: #ref-45

#tweeprint time! I'm excited to share the work of Anthony Jang, Ravi Sharma, and me on the active control of an attentional bottleneck in value-based and perceptual decision-making, now available at www.biorxiv.org/content/10.1101/2020.08.... 1/