More Like this

1. 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)

   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.

    

   more » « less
2. Computational models of adaptive behavior and prefrontal cortex

   https://doi.org/10.1038/s41386-021-01123-1  

   Soltani, Alireza ; Koechlin, Etienne ( August 2021 , Neuropsychopharmacology)

   The real world is uncertain, and while ever changing, it constantly presents itself in terms of new sets of behavioral options. To attain the flexibility required to tackle these challenges successfully, most mammalian brains are equipped with certain computational abilities that rely on the prefrontal cortex (PFC). By examining learning in terms of internal models associating stimuli, actions, and outcomes, we argue here that adaptive behavior relies on specific interactions between multiple systems including: (1) selective models learning stimulus–action associations through rewards; (2) predictive models learning stimulus- and/or action–outcome associations through statistical inferences anticipating behavioral outcomes; and (3) contextual models learning external cues associated with latent states of the environment. Critically, the PFC combines these internal models by forming task sets to drive behavior and, moreover, constantly evaluates the reliability of actor task sets in predicting external contingencies to switch between task sets or create new ones. We review different models of adaptive behavior to demonstrate how their components map onto this unifying framework and specific PFC regions. Finally, we discuss how our framework may help to better understand the neural computations and the cognitive architecture of PFC regions guiding adaptive behavior.

    

   more » « less
3. Delay-related activity in marmoset prefrontal cortex

   https://doi.org/10.1093/cercor/bhac289  

   Wong, Raymond K ; Selvanayagam, Janahan ; Johnston, Kevin D ; Everling, Stefan ( August 2022 , Cerebral Cortex)

   Abstract Persistent delay-period activity in prefrontal cortex (PFC) has long been regarded as a neural signature of working memory (WM). Electrophysiological investigations in macaque PFC have provided much insight into WM mechanisms; however, a barrier to understanding is the fact that a portion of PFC lies buried within the principal sulcus in this species and is inaccessible for laminar electrophysiology or optical imaging. The relatively lissencephalic cortex of the New World common marmoset (Callithrix jacchus) circumvents such limitations. It remains unknown, however, whether marmoset PFC neurons exhibit persistent activity. Here, we addressed this gap by conducting wireless electrophysiological recordings in PFC of marmosets performing a delayed-match-to-location task on a home cage-based touchscreen system. As in macaques, marmoset PFC neurons exhibited sample-, delay-, and response-related activity that was directionally tuned and linked to correct task performance. Models constructed from population activity consistently and accurately predicted stimulus location throughout the delay period, supporting a framework of delay activity in which mnemonic representations are relatively stable in time. Taken together, our findings support the existence of common neural mechanisms underlying WM performance in PFC of macaques and marmosets and thus validate the marmoset as a suitable model animal for investigating the microcircuitry underlying WM. 

   more » « less
4. Brain-in-the-Loop Learning Using fNIR and Simulated Virtual Reality Surgical Tasks: Hemodynamic and Behavioral Effects.

   https://doi.org/10.1007/978-3-319-20816-9_31  

   Shewokis, P.A. ( January 2015 , Lecture Notes in Computer Science)

   Functional near infrared spectroscopy (fNIR) is a noninvasive, portable optical imaging tool to monitor changes in hemodynamic responses (i.e., oxygenated hemoglobin (HbO)) within the prefrontal cortex (PFC) in response to sensory, motor or cognitive activation. We used fNIR for monitoring PFC activation during learning of simulated laparoscopic surgical tasks throughout 4 days of training and testing. Blocked (BLK) and random (RND) practice orders were used to test the practice schedule effect on behavioral, hemodynamic responses and relative neural efficiency (EFFrel-neural) measures during transfer. Left and right PFC for both tasks showed significant differences with RND using less HbO than BLK. Cognitive workload showed RND exhibiting high EFFrel-neural across the PFC for the coordination task while the more difficult cholecystectomy task showed EFFrel-neural differences only in the left PFC. Use of brain activation, behavioral and EFFrel-neural measures can provide a more accurate depiction of the generalization or transfer of learning. 

   more » « less
5. Characterization of Design Brain States Over Time When Using Morphological Analysis and TRIZ

   Milovanovic, J. ; Gero, J. ; Shealy, T. ; Hu, M. ( January 2023 , Design Computing and Cognition'22)

   Gero, John S. (Ed.)

   In this paper, we explored changes in brain states over time while designers were generating concepts. Participants either used morphological analysis or TRIZ to develop a design concept for two design tasks. While designing, participants’ brain activation in their prefrontal cortex (PFC) was monitored with a functional Near Infrared Spectroscopy machine. To identify variation in brain states, we analyzed changes in brain networks. Using k-mean clustering to classify brain networks for each task revealed four brain network patterns. While using morphological analysis, the occurrence of each pattern was similar along the design steps. For TRIZ, some brain states dominated depending on the design step. Drain states changes suggests that designers alternate engaging certain subregions of the PFC. This approach to studying brain behavior provides a more granular understanding of the evolution of design brain states over time. Findings add to the growing body of research exploring design neurocognition. 

   more » « less