Single-Trial Decoding from Local Field Potential Using Bag of Word Representation

Selective Modulation of Hippocampal Theta Oscillations in Response to Morphine versus Natural Reward

Despite the overlapping neural circuits underlying natural and drug rewards, several studies have suggested different behavioral and neurochemical mechanisms in response to drug vs. natural rewards. The strong link between hippocampal theta oscillations (4-12 Hz) and reward-associated learning and memory has raised the hypothesis that this rhythm in hippocampal CA1 might be differently modulated by drug- and natural-conditioned place preference (CPP). Time-frequency analysis of recorded local field potentials (LFPs) from the CA1 of freely moving male rats previously exposed to a natural (in this case, food), drug (in this case, morphine), or saline (control) reward cue in the CPP paradigm showed that the hippocampal CA1 theta activity represents a different pattern for entrance to the rewarded compared to unrewarded compartment during the post-test session of morphine- and natural-CPP. Comparing LFP activity in the CA1 between the saline and morphine/natural groups showed that the maximum theta power occurred before entering the unrewarded compartment and after the entrance to the rewarded compartment in morphine and natural groups, respectively. In conclusion, our findings suggest that drug and natural rewards could differently affect the theta dynamic in the hippocampal CA1 region during reward-associated learning and contextual cueing in the CPP paradigm.

Assessment of a single trial impact on the amplitude of the averaged event related potentials

Widely used in neuroscience the averaging of event related potentials is based on the assumption that small responses to the investigated events are present in every trial but can be hidden under the random noise. This situation often takes place, especially in experiments performed at hierarchically lower levels of sensory systems. However, in the studies of higher order complex neuronal networks evoked responses might appear only under particular conditions and be absent otherwise. We encountered this problem studying a propagation of interoceptive information to the cortical areas in the sleep-wake cycle. Cortical responses to various visceral events were present during some periods of sleep, then disappeared for a while and restored again after a period of absence. Further investigation of the viscero-cortical communication required a method that would allow labeling the trials contributing to the averaged event related responses-"efficient trials," and separating them from the trials without any response. Here we describe a heuristic approach to solving this problem in the context of viscero-cortical interactions occurring during sleep. However, we think that the proposed technique can be applicable to any situation where neuronal processing of the same events is expected to be variable due to internal or external factors modulating neuronal activity. The method was first implemented as a script for Spike 2 program version 6.16 (CED). However, at present a functionally equivalent version of this algorithm is also available as Matlab code at https://github.com/george-fedorov/erp-correlations.

A dual-channel language decoding from brain activity with progressive transfer training

When we view a scene, the visual cortex extracts and processes visual information in the scene through various kinds of neural activities. Previous studies have decoded the neural activity into single/multiple semantic category tags which can caption the scene to some extent. However, these tags are isolated words with no grammatical structure, insufficiently conveying what the scene contains. It is well‐known that textual language (sentences/phrases) is superior to single word in disclosing the meaning of images as well as reflecting people's real understanding of the images. Here, based on artificial intelligence technologies, we attempted to build a dual‐channel language decoding model (DC‐LDM) to decode the neural activities evoked by images into language (phrases or short sentences). The DC‐LDM consisted of five modules, namely, Image‐Extractor, Image‐Encoder, Nerve‐Extractor, Nerve‐Encoder, and Language‐Decoder. In addition, we employed a strategy of progressive transfer to train the DC‐LDM for improving the performance of language decoding. The results showed that the texts decoded by DC‐LDM could describe natural image stimuli accurately and vividly. We adopted six indexes to quantitatively evaluate the difference between the decoded texts and the annotated texts of corresponding visual images, and found that Word2vec‐Cosine similarity (WCS) was the best indicator to reflect the similarity between the decoded and the annotated texts. In addition, among different visual cortices, we found that the text decoded by the higher visual cortex was more consistent with the description of the natural image than the lower one. Our decoding model may provide enlightenment in language‐based brain‐computer interface explorations.

A neural correlate of visual feature binding in primate lateral prefrontal cortex

We effortlessly perceive visual objects as unified entities, despite the preferential encoding of their various visual features in separate cortical areas. A 'binding' process is assumed to be required for creating this unified percept, but the underlying neural mechanism and specific brain areas are poorly understood. We investigated 'feature-binding' across two feature dimensions, using a novel stimulus configuration, designed to disambiguate whether a given combination of color and motion direction is perceived as bound or unbound. In the "bound" condition, two behaviorally relevant features (color and motion) belong to the same object, while in the "unbound" condition they belong to different objects. We recorded local field potentials from the lateral prefrontal cortex (lPFC) in macaque monkeys that actively monitored the different stimulus configurations. Our data show a neural representation of visual feature binding especially in the 4-12 Hz frequency band and a transmission of binding information between different lPFC neural subpopulations. This information is linked to the animal's reaction time, suggesting a behavioral relevance of the binding information. Together, our results document the involvement of the prefrontal cortex, targeted by the dorsal and ventral visual streams, in binding visual features from different dimensions, in a process that includes a dynamic modulation of low frequency inter-regional communication.