An approximate line attractor in the hypothalamus that encodes an aggressive internal state

Nair, Aditya; Karigo, Tomomi; Yang, Baohua; Linderman, Scott W.; Anderson, David J.; Kennedy, Ann

doi:10.1101/2022.04.19.488776

Cited by 5 publications

(3 citation statements)

References 67 publications

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“…Our modeling approach has the advantage of decomposing the dynamics of a complex system into simpler linear parts that are amenable to analysis and interpretation, similar to switching LDS models 42 . A previous application of such models has led to the discovery of line attractor dynamics in the hypothalamus of mice during decisions underlying aggression 43 . In combination with methods from control theory, LDS can also be used to infer inputs that are optimal for a given task, like bringing brain activity into healthy regimes in biomedical applications 44 or optimally configuring cortical dynamics during movement preparation [37][38][39] .…”

Section: Discussionmentioning

confidence: 99%

Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics

Magraner

Mante

Sahani

2023

Preprint

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The complex activity of neural populations in the Prefrontal Cortex (PFC) is a hallmark of high-order cognitive processes. How these rich cortical dynamics emerge and give rise to neural computations is largely unknown. Here, we infer models of neural population dynamics that explain how PFC circuits of monkeys may select and integrate relevant sensory inputs during context-dependent perceptual decisions. A class of models implementing linear dynamics accurately captured the rich features of the recorded PFC responses.These models fitted the neural activity nearly as well as a factorization of population responses that had the flexibility to capture non-linear temporal patterns, suggesting that linear dynamics is sufficient to recapitulate the complex PFC responses in each context. Two distinct mechanisms of input selection and integration were consistent with the PFC data. One mechanism implemented recurrent dynamics that differed between contexts, the other a subtle modulation of the inputs across contexts. The two mechanisms made different predictions about the contribution of non-normal recurrent dynamics in transiently amplifying and selectively integrating the inputs. In both mechanisms the inputs were inferred directly from the data and spanned multi-dimensional input subspaces. Input integration likewise consistently involved high dimensional dynamics that unfolded in two distinct phases, corresponding to integration on fast and slow time-scales. Our study offers a principled framework to link the activity of neural populations to computation and to find mechanistic descriptions of neural processes that are consistent with the rich dynamics implemented by neural circuits.

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Section: Discussionmentioning

confidence: 99%

Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics

Magraner

Mante

Sahani

2023

Preprint

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“…During attack, primarily male-tuned neurons are active 9,19 . Optogenetic experiments 9,11,13 and computational modeling 41 suggest that the activity of VMHvl Esr1 neurons must reach a certain threshold in order for males to transition from sniff to attack. In the absence of BNSTpr Esr1 activity female-tuned neurons become dominant in VMHvl, so the number and activity of male-selective neurons are reduced (Fig.…”

Section: Discussionmentioning

confidence: 99%

Transformations of neural representations in a social behaviour network

Yang

Karigo

Anderson

2022

Nature

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Mating and aggression are innate social behaviors that are controlled by subcortical circuits in the extended amygdala and hypothalamus [1][2][3][4] . The bed nucleus of the stria terminalis (BNSTpr) is a node that receives input encoding sex-specific olfactory cues from the medial amygdala (MeApd) 5,6 , and which in turn projects to hypothalamic nuclei that control mating 7-9 (MPOA) and aggression 9-14 (VMHvl), respectively 15 . Previous studies have demonstrated that male aromatase + BNSTpr (AB) neurons are required for mounting and attack, and may identify conspecific sex according to their overall level of activity 16 . However neural representations in BNSTpr, their function and their transformations in the hypothalamus have not been characterized. Here we have performed calcium imaging 17,18 of male BNSTpr Esr1 neurons during social behaviors. We identify distinct populations of female-vs. male-tuned neurons in BNSTpr, with the former outnumbering the latter by ~2:1, similar to MeApd and MPOA but opposite to VMHvl, where male-tuned neurons predominate 6,9,19 . Chemogenetic silencing of BNSTpr Esr1 neurons whilst imaging MPOA Esr1 or VMHvl Esr1 neurons in behaving animals revealed, unexpectedly, that the male-dominant sex-tuning bias in VMHvl was inverted to female-dominant, while a switch from sniff-to mount-selective neurons during mating was attenuated in MPOA. Our data also indicate that BNSTpr Esr1 neurons are not essential for conspecific sex identification. Rather, they control the transition from appetitive to consummatory phases of male social behaviors by shaping sexand behavior-specific neural representations in the hypothalamus.

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“…These tools abstract behavior trajectories from video recordings, and facilitate the modeling of behavioral dynamics. Most pipelines for analyzing animal behavior consist of three key steps [39]: (1) pose estimation [61,69,18,50,43], (2) spatial-temporal feature extraction [38,13], and (3) quantification and phenotyping of behavior [46,42,68]. In our work, we consider the analysis of behavior after pose estimation or keypoint extraction is performed.…”

Section: Animal Behavior Analysismentioning

confidence: 99%

Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis

Azabou¹,

Mendelson²,

Ahad³

et al. 2023

Preprint

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Natural behavior consists of dynamics that are complex and unpredictable, especially when trying to predict many steps into the future. While some success has been found in building representations of behavior under constrained or simplified task-based conditions, many of these models cannot be applied to free and naturalistic settings where behavior becomes increasingly hard to model. In this work, we develop a multi-task representation learning model for behavior that combines two novel components: (i) an action-prediction objective that aims to predict the distribution of actions over future timesteps, and (ii) a multi-scale architecture that builds separate latent spaces to accommodate short-and long-term dynamics. After demonstrating the ability of the method to build representations of both local and global dynamics in realistic robots in varying environments and terrains, we apply our method to the MABe 2022 Multi-agent behavior challenge, where our model ranks first overall (top rank over all 13 tasks), first on all sequence-level tasks, and 1st or 2nd on 7 out of 9 frame-level tasks. In all of these cases, we show that our model can build representations that capture the many different factors that drive behavior and solve a wide range of downstream tasks.Preprint. Under review.

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An approximate line attractor in the hypothalamus that encodes an aggressive internal state

Cited by 5 publications

References 67 publications

Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics

Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics

Transformations of neural representations in a social behaviour network

Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis

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