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1. MEG correlates of speech planning in simple vs. interactive picture naming in children and adults

   https://doi.org/10.1371/journal.pone.0292316  

   Goldman, Ebony; Bou-Dargham, Sherine; Lai, Marco; Guda, Anvita; Fallon, Jacqui; Hauptman, Miriam; Reinoso, Alejandra; Phillips, Sarah; Abrams, Ellie; Parrish, Alicia; et al (October 2023, PLOS ONE)

   Molinaro, Nicola (Ed.)

   The picture naming task is common both as a clinical task and as a method to study the neural bases of speech production in the healthy brain. However, this task is not reflective of most naturally occurring productions, which tend to happen within a context, typically in dialogue in response to someone else’s production. How the brain basis of the classic “confrontation picture naming” task compares to the planning of utterances in dialogue is not known. Here we used magnetoencephalography (MEG) to measure neural activity associated with language production using the classic picture naming task as well as a minimal variant of the task, intended as more interactive or dialogue-like. We assessed how neural activity is affected by the interactive context in children, teenagers, and adults. The general pattern was that in adults, the interactive task elicited a robust sustained increase of activity in frontal and temporal cortices bilaterally, as compared to simple picture naming. This increase was present only in the left hemisphere in teenagers and was absent in children, who, in fact, showed the reverse effect. Thus our findings suggest a robustly bilateral neural basis for the coordination of interaction and a very slow developmental timeline for this network. 

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2. Dynamical latent state computation in the male macaque posterior parietal cortex

   https://doi.org/10.1038/s41467-023-37400-4  

   Lakshminarasimhan, Kaushik J.; Avila, Eric; Pitkow, Xaq; Angelaki, Dora E. (April 2023, Nature Communications)

   Abstract Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. We hypothesized that neural populations estimate these states by processing sensory history through recurrent interactions which reflect the internal model of the world. To test this, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state - monkey’s displacement from the goal - was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that task demands shape the neural interactions in PPC, leading them to embody a world model that consolidates information and tracks task-relevant hidden states. 

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3. Neural Correlations Across Mice During Spontaneous and Task-Related Behaviors

   https://doi.org/10.1109/NER52421.2023.10123873  

   Yi, Daiyao; Saxena, Shreya (April 2023, IEEE)

   While the neural commonalities as subjects perform similar task-related behaviors has been previously examined, it is very difficult to ascertain the neural commonalities for spontaneous, task-unrelated behaviors such as grooming. As our ability to record high-dimensional naturalistic behavioral and corresponding neural data increases, we can now try to understand the relationship between different subjects performing spontaneous behaviors that occur rarely in time. Here, we first apply novel machine learning techniques to behavioral video data from four head-fixed mice as they perform a self-initiated decision-making task while their neural activity is recorded using widefield calcium imaging. Across mice, we automatically identify spontaneous behaviors such as grooming and task-related behaviors such as lever pulls. Next, we explore the commonalities between the neural activity of different mice as they perform these tasks by transforming the neural activity into a common subspace, using Multidimensional Canonical Correlation Analysis (MCCA). Finally, we compare the commonalities across different trials in the same subject to those across subjects for different types of behaviors, and find that many recorded brain regions display high levels of correlation for spontaneous behaviors such as grooming. The combined behavioral and neural analysis methods in this paper provide an understanding of how similarly different animals perform innate behaviors. 

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4. Adaptation and learning as strategies to maximize reward in neurofeedback tasks

   https://doi.org/10.3389/fnhum.2024.1368115  

   Osuna-Orozco, Rodrigo; Zhao, Yi; Stealey, Hannah Marie; Lu, Hung-Yun; Contreras-Hernandez, Enrique; Santacruz, Samantha Rose (March 2024, Frontiers in Human Neuroscience)

   Adaptation and learning have been observed to contribute to the acquisition of new motor skills and are used as strategies to cope with changing environments. However, it is hard to determine the relative contribution of each when executing goal directed motor tasks. This study explores the dynamics of neural activity during a center-out reaching task with continuous visual feedback under the influence of rotational perturbations Results for a brain-computer interface (BCI) task performed by two non-human primate (NHP) subjects are compared to simulations from a reinforcement learning agent performing an analogous task. We characterized baseline activity and compared it to the activity after rotational perturbations of different magnitudes were introduced. We employed principal component analysis (PCA) to analyze the spiking activity driving the cursor in the NHP BCI task as well as the activation of the neural network of the reinforcement learning agent. Results and discussionOur analyses reveal that both for the NHPs and the reinforcement learning agent, the task-relevant neural manifold is isomorphic with the task. However, for the NHPs the manifold is largely preserved for all rotational perturbations explored and adaptation of neural activity occurs within this manifold as rotations are compensated by reassignment of regions of the neural space in an angular pattern that cancels said rotations. In contrast, retraining the reinforcement learning agent to reach the targets after rotation results in substantial modifications of the underlying neural manifold. Our findings demonstrate that NHPs adapt their existing neural dynamic repertoire in a quantitatively precise manner to account for perturbations of different magnitudes and they do so in a way that obviates the need for extensive learning. 

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5. Language to Network: Conditional Parameter Adaptation with Natural Language Descriptions

   Jin, Tian; Liu, Zhun; Yan, Shengjia; Eichenberger, Alexandre; Morency, Louis-Philippe (January 2020, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics)

   Transfer learning using ImageNet pre-trained models has been the de facto approach in a wide range of computer vision tasks. However, fine-tuning still requires task-specific training data. In this paper, we propose N3 (Neural Networks from Natural Language) - a new paradigm of synthesizing task-specific neural networks from language descriptions and a generic pre-trained model. N3 leverages language descriptions to generate parameter adaptations as well as a new task-specific classification layer for a pre-trained neural network, effectively “fine-tuning” the network for a new task using only language descriptions as input. To the best of our knowledge, N3 is the first method to synthesize entire neural networks from natural language. Experimental results show that N3 can out-perform previous natural-language based zero-shot learning methods across 4 different zero-shot image classification benchmarks. We also demonstrate a simple method to help identify keywords in language descriptions leveraged by N3 when synthesizing model parameters. 

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