Robust anticipation of continuous steering actions from electroencephalographic data during simulated driving

Driving a car requires high cognitive demands, from sustained attention to perception and action planning. Recent research investigated the neural processes reflecting the planning of driving actions, aiming to better understand the factors leading to driving errors and to devise methodologies to anticipate and prevent such errors by monitoring the driver’s cognitive state and intention. While such anticipation was shown for discrete driving actions, such as emergency braking, there is no evidence for robust neural signatures of continuous action planning. This study aims to fill this gap by investigating continuous steering actions during a driving task in a car simulator with multimodal recordings of behavioural and electroencephalography (EEG) signals. System identification is used to assess whether robust neurophysiological signatures emerge before steering actions. Linear decoding models are then used to determine whether such cortical signals can predict continuous steering actions with progressively longer anticipation. Results point to significant EEG signatures of continuous action planning. Such neural signals show consistent dynamics across participants for anticipations up to 1 s, while individual-subject neural activity could reliably decode steering actions and predict future actions for anticipations up to 1.8 s. Finally, we use canonical correlation analysis to attempt disentangling brain and non-brain contributors to the EEG-based decoding. Our results suggest that low-frequency cortical dynamics are involved in the planning of steering actions and that EEG is sensitive to that neural activity. As a result, we propose a framework to investigate anticipatory neural activity in realistic continuous motor tasks.

Robust data storage in DNA by de Bruijn graph-based decoding

Data storage in DNA, which store information in polymers, is a potential technology with high density and long-term features. However, the indels, strand rearrangements, and strand breaks that emerged during synthesis, amplification, sequencing, and storage of DNA molecules need to be handled. Here, we report a de Bruijn graph-based, greedy path search algorithm (DBG-GPS), which can efficiently handle all these issues by efficient reconstruction of the DNA strands. DBG-GPS achieves accurate data recovery with low-quality, deep error-prone PCR products, and accelerated aged DNA samples (solution, 70℃ for two weeks). The robustness of DBG-GPS was verified with 100 times of multiple retrievals using PCR products with massive unspecific amplifications. Moreover, DBG-GPS shows linear decoding complexity and more than 100 times faster than the multiple alignment-based methods, indicating a suitable solution for large-scale data storage.

Super-robust data storage in DNA by de Bruijn graph-based decoding

High density and long-term features make DNA data storage a potential media. However, DNA data channel is a unique channel with unavoidable ‘data reputations’ in the forms of multiple error-rich strand copies. This multi-copy feature cannot be well harnessed by available codec systems optimized for single-copy media. Furthermore, lacking an effective mechanism to handle base shift issues, these systems perform poorly with indels. Here, we report the efficient reconstruction of DNA strands from multiple error-rich sequences directly, utilizing a De Bruijn Graph-based Greedy Path Search (DBG-GPS) algorithm. DBG-GPS can take advantage of the multi-copy feature for efficient correction of indels as well as substitutions. As high as 10% of errors can be accurately corrected with a high coding rate of 96.8%. Accurate data recovery with low quality, deep error-prone PCR products proved the high robustness of DBG-GPS (314Kb, 12K oligos). Furthermore, DBG-GPS shows 50 times faster than the clustering and multiple alignment-based methods reported. The revealed linear decoding complexity makes DBG-GPS a suitable solution for large-scale data storage. DBG-GPS’s capacity with large data was verified by large-scale simulations (300 MB). A Python implementation of DBG-GPS is available at https://switch-codes.coding.net/public/switch-codes/DNA-Fountain-De-Bruijn-Decoding/git/files.

Transformation of population code from dLGN to V1 facilitates linear decoding

SummaryHow neural populations represent sensory information, and how that representation is transformed from one brain area to another, are fundamental questions of neuroscience. The dorsolateral geniculate nucleus (dLGN) and primary visual cortex (V1) represent two distinct stages of early visual processing. Classic sparse coding theories propose that V1 neurons represent local features of images. More recent theories have argued that the visual pathway transforms visual representations to become increasingly linearly separable. To test these ideas, we simultaneously recorded the spiking activity of mouse dLGN and V1 in vivo. We find strong evidence for both sparse coding and linear separability theories. Surprisingly, the correlations between neurons in V1 (but not dLGN) were shaped as to be irrelevant for stimulus decoding, a feature which we show enables linear separability. Therefore, our results suggest that the dLGN-V1 transformation reshapes correlated variability in a manner that facilitates linear decoding while producing a sparse code.

A Sparse Probabilistic Code Underlies the Limits of Behavioral Discrimination

The cortical code that underlies perception must enable subjects to perceive the world at time scales relevant for behavior. We find that mice can integrate visual stimuli very quickly (<100 ms) to reach plateau performance in an orientation discrimination task. To define features of cortical activity that underlie performance at these time scales, we measured single-unit responses in the mouse visual cortex at time scales relevant to this task. In contrast to high-contrast stimuli of longer duration, which elicit reliable activity in individual neurons, stimuli at the threshold of perception elicit extremely sparse and unreliable responses in the primary visual cortex such that the activity of individual neurons does not reliably report orientation. Integrating information across neurons, however, quickly improves performance. Using a linear decoding model, we estimate that integrating information over 50–100 neurons is sufficient to account for behavioral performance. Thus, at the limits of visual perception, the visual system integrates information encoded in the probabilistic firing of unreliable single units to generate reliable behavior.

Solutions to All-Colors Problem on Graph Cellular Automata

The All-Ones Problem comes from the theory of σ+-automata, which is related to graph dynamical systems as well as the Odd Set Problem in linear decoding. In this paper, we further study and compute the solutions to the “All-Colors Problem,” a generalization of “All-Ones Problem,” on some interesting classes of graphs which can be divided into two subproblems: Strong-All-Colors Problem and Weak-All-Colors Problem, respectively. We also introduce a new kind of All-Colors Problem, k-Random Weak-All-Colors Problem, which is relevant to both combinatorial number theory and cellular automata theory.

Nonlinear mixed selectivity supports reliable neural computation

SummaryNeuronal activity in the brain is variable, yet both perception and behavior are generally reliable. How does the brain achieve this? Here, we show that the conjunctive coding of multiple stimulus features, commonly known as nonlinear mixed selectivity, may be used by the brain to support reliable information transmission using unreliable neurons. Nonlinear mixed selectivity (NMS) has been observed widely across the brain, from primary sensory to decision-making to motor areas. Representations of stimulus features are nearly always mixed together, rather than represented separately or with only additive (linear) mixing, as in pure selectivity. NMS has been previously shown to support flexible linear decoding for complex behavioral tasks. Here, we show that NMS has another important benefit: it requires as little as half the metabolic energy required by pure selectivity to achieve the same level of transmission reliability. This benefit holds for sensory, motor, and more abstract, cognitive representations. Further, we show experimental evidence that NMS exists in the brain even when it does not enable behaviorally useful linear decoding. This suggests that NMS may be a general coding scheme exploited by the brain for reliable and efficient neural computation.