Cazin, N. | bioRxiv Science

bioRxiv2019

Beyond the word and image: III. Neurodynamic properties of the semantic network

Understanding the neural process underlying the comprehension of visual images and sentences remains a major open challenge in cognitive neuroscience. We previously demonstrated with fMRI and DTI that comprehension of visual images and sentences describing human activities recruits a common semantic system. The current research tests the hypothesis that this common semantic system will display similar neural dynamics during processing in these two modalities. To investigate these neural dynamics we recorded EEG from naive subjects as they saw simple narratives made up of a first visual image depicting a human event, followed by a second that was either a sequentially coherent narrative follow-up, or not, of the first image. In separate blocks of trials the same protocol was presented using sentences. Analysis of the EEG signal revealed common neural dynamics for semantic processing across image and sentence modalities. Late positive ERPs were observed in response to sequential incoherence for sentences and images, consistent with previous studies that examined coherence in these two modalities separately. Analysis of oscillatory power revealed increased gamma-band activity for sequential coherence, again consistent with previous studies showing gamma increases for coherence and matching in sentence and image processing. Multivariate analysis demonstrated that training a classifier on data from one modality (images or sentences) allowed reliable decoding of the sequential coherence of data from trials in the untrained modality, providing further support for a common underlying semantic system for images and sentences. Processing sequential coherence of successive stimuli is associated with neural dynamics that are common to sentence and visual image modalities and that can be decoded across modalities. These results are discussed in the context of EEG signatures of narrative processing and meaning, and more general neural mechanisms for structure processing.

neuroscience↗

bioRxiv2018

Prefrontal cortex creates novel navigation sequences from hippocampal place-cell replay with spatial reward propagation

As rats learn to search for multiple sources of food or water in a complex environment, they generate increasingly efficient trajectories between reward sites, across multiple trials. This optimization capacity has been characterized in the Traveling Salesrat Problem (TSP) (de Jong et al (2011). Such spatial navigation capacity involves the replay of hippocampal place-cells during awake states, generating small sequences of spatially related place-cell activity that we call \"snippets\". These snippets occur primarily during sharp-wave-ripple (SWR) events. Here we focus on the role of replay during the awake state, as the animal is learning across multiple trials. We hypothesize that snippet replay generates synthetic data that can substantially expand and restructure the experience available to make PFC learning more optimal. We developed a model of snippet generation that is modulated by reward, propagated in the forward and reverse directions. This implements a form of spatial credit assignment for reinforcement learning. We use a biologically motivated computational framework known as reservoir computing to model PFC in sequence learning, in which large pools of prewired neural elements process information dynamically through reverberations. This PFC model is ideal to consolidate snippets into larger spatial sequences that may be later recalled by subsets of the original sequences. Our simulation experiments provide neurophysiological explanations for two pertinent observations related to navigation. Reward modulation allows the system to reject non-optimal segments of experienced trajectories, and reverse replay allows the system to \"learn\" trajectories that is has not physically experienced, both of which significantly contribute to the TSP behavior.\n\nAuthor SummaryAs rats search for multiple sources of food in a complex environment, they generate increasingly efficient trajectories between reward sites, across multiple trials, characterized in the Traveling Salesrat Problem (TSP). This likely involves the coordinated replay of place-cell \"snippets\" between successive trials. We hypothesize that \"snippets\" can be used by the prefrontal cortex (PFC) to implement a form of reward-modulated reinforcement learning. Our simulation experiments provide neurophysiological explanations for two pertinent observations related to navigation. Reward modulation allows the system to reject non-optimal segments of experienced trajectories, and reverse replay allows the system to \"learn\" trajectories that it has not physically experienced, both of which significantly contribute to the TSP behavior.

neuroscience↗