Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias

<div><p>The activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degrees of similarity between these neural activity patterns in response to different events are used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio, without losing the possibility of deriving an interpretable distance measure from the estimated similarity. The method is closely related to Pattern Component Model (PCM), but instead of modeling the estimated neural patterns as in PCM, BRSA models the imaging data directly and is suited for analyzing data in which the order of task conditions is not fully counterbalanced. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum <i>a posteriori</i> estimation of neural activity patterns, which can be further used for fMRI decoding. Our method therefore paves the way towards a more unified and principled analysis of neural representations underlying fMRI signals. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).</p></div

A Reflection Principle for the Control of Molecular Photodissociation in Solids: Model Simulation for F2 in Ar

Laser pulse induced photodissociation of molecules in rare gas solids is investigated by representative quantum wavepackets or classical trajectories which are directed towards, or away from cage exits, yielding dominant photodissociation into different neighbouring cages. The directionality is determined by a sequence of reflections inside the relief provided by the slopes of the potential energy surface of the excited system, which in turn depend on the initial preparation of the matrix isolated system, e.g. by laser pulses with different frequencies or by vibrational pre-excitation of the cage atoms. This reflection principle is demonstrated for a simple, two-dimensional model of F2 in Ar

Neural Signatures of Prediction Errors in a Decision-Making Task are Modulated by Action Execution Failures

Decisions must be implemented through actions, and actions are prone to error. As such, when an expected outcome is not obtained, an individual should be sensitive to not only whether the choice itself was suboptimal but also whether the action required to indicate that choice was executed successfully. The intelligent assignment of credit to action execution versus action selection has clear ecological utility for the learner. To explore this, we used a modified version of a classic reinforcement learning task in which feedback indicated whether negative prediction errors were, or were not, associated with execution errors. Using fMRI, we asked if prediction error computations in the human striatum, a key substrate in reinforcement learning and decision making, are modulated when a failure in action execution results in the negative outcome. Participants were more tolerant of non-rewarded outcomes when these resulted from execution errors versus when execution was successful, but reward was withheld. Consistent with this behavior, a model-driven analysis of neural activity revealed an attenuation of the signal associated with negative reward prediction errors in the striatum following execution failures. These results converge with other lines of evidence suggesting that prediction errors in the mesostriatal dopamine system integrate high-level information during the evaluation of instantaneous reward outcomes

Esophageal muscle physiology and morphogenesis require assembly of a collagen XIX–rich basement membrane zone

Collagen XIX is an extremely rare extracellular matrix component that localizes to basement membrane zones and is transiently expressed by differentiating muscle cells. Characterization of mice harboring null and structural mutations of the collagen XIX (Col19a1) gene has revealed the critical contribution of this matrix protein to muscle physiology and differentiation. The phenotype includes smooth muscle motor dysfunction and hypertensive sphincter resulting from impaired swallowing-induced, nitric oxide–dependent relaxation of the sphincteric muscle. Muscle dysfunction was correlated with a disorganized matrix and a normal complement of enteric neurons and interstitial cells of Cajal. Mice without collagen XIX exhibit an additional defect, namely impaired smooth-to-skeletal muscle cell conversion in the abdominal segment of the esophagus. This developmental abnormality was accounted for by failed activation of myogenic regulatory factors that normally drive esophageal muscle transdifferentiation. Therefore, these findings identify collagen XIX as the first structural determinant of sphincteric muscle function, and as the first extrinsic factor of skeletal myogenesis in the murine esophagus

Lowered sensitivity of bitter taste receptors to β-glucosides in bamboo lemurs: an instance of parallel and adaptive functional decline in TAS2R16?

竹食サル類の苦味感覚の進化を解明 --竹が先か苦味が先か--. 京都大学プレスリリース. 2021-04-16.Bitter taste facilitates the detection of potentially harmful substances and is perceived via bitter taste receptors (TAS2Rs) expressed on the tongue and oral cavity in vertebrates. In primates, TAS2R16 specifically recognizes β-glucosides, which are important in cyanogenic plants' use of cyanide as a feeding deterrent. In this study, we performed cell-based functional assays for investigating the sensitivity of TAS2R16 to β-glucosides in three species of bamboo lemurs (Prolemur simus, Hapalemur aureus and H. griseus), which primarily consume high-cyanide bamboo. TAS2R16 receptors from bamboo lemurs had lower sensitivity to β-glucosides, including cyanogenic glucosides, than that of the closely related ring-tailed lemur (Lemur catta). Ancestral reconstructions of TAS2R16 for the bamboo-lemur last common ancestor (LCA) and that of the Hapalemur LCA showed an intermediate sensitivity to β-glucosides between that of the ring-tailed lemurs and bamboo lemurs. Mutagenetic analyses revealed that P. simus and H. griseus had separate species-specific substitutions that led to reduced sensitivity. These results indicate that low sensitivity to β-glucosides at the cellular level-a potentially adaptive trait for feeding on cyanogenic bamboo-evolved independently after the Prolemur-Hapalemur split in each species

Prolonged dopamine signalling in striatum signals proximity and value of distant rewards

Predictions about future rewarding events have a powerful influence on behaviour. The phasic spike activity of dopamine-containing neurons, and corresponding dopamine transients in the striatum, are thought to underlie these predictions, encoding positive and negative reward prediction errors. However, many behaviours are directed towards distant goals, for which transient signals may fail to provide sustained drive. Here we report an extended mode of reward-predictive dopamine signalling in the striatum that emerged as rats moved towards distant goals. These dopamine signals, which were detected with fast-scan cyclic voltammetry (FSCV), gradually increased or—in rare instances—decreased as the animals navigated mazes to reach remote rewards, rather than having phasic or steady tonic profiles. These dopamine increases (ramps) scaled flexibly with both the distance and size of the rewards. During learning, these dopamine signals showed spatial preferences for goals in different locations and readily changed in magnitude to reflect changing values of the distant rewards. Such prolonged dopamine signalling could provide sustained motivational drive, a control mechanism that may be important for normal behaviour and that can be impaired in a range of neurologic and neuropsychiatric disorders.National Institutes of Health (U.S.) (Grant R01 MH060379)National Parkinson Foundation (U.S.)Cure Huntington’s Disease Initiative, Inc. (Grant A-5552)Stanley H. and Sheila G. Sydney Fun

Efficient concept formation in large state spaces

General autonomous agents must be able to operate in previously unseen worlds with large state spaces. To operate successfully in such worlds, the agents must maintain their own models of the environment, based on concept sets that are several orders of magnitude smaller. For adaptive agents, those concept sets cannot be fixed, but must adapt continuously to new situations. This, in turn, requires mechanisms for forming and preserving those concepts that are critical to successful decision-making, while removing others. In this paper we compare four general algorithms for learning and decision-making: (i) standard Q-learning, (ii) deep Q-learning, (iii) single-agent local Q-learning, and (iv) single-agent local Q-learning with improved concept formation rules. In an experiment with a state space larger than 232, it was found that a single-agent local Q-learning agent with improved concept formation rules performed substantially better than a similar agent with less sophisticated concept formation rules and slightly better than a deep Q-learning agent