Manipulating synthetic optogenetic odors reveals the coding logic of olfactory perception.

How does neural activity generate perception? Finding the combinations of spatial or temporal activity features (such as neuron identity or latency) that are consequential for perception remains challenging. We trained mice to recognize synthetic odors constructed from parametrically defined patterns of optogenetic activation, then measured perceptual changes during extensive and controlled perturbations across spatiotemporal dimensions. We modeled recognition as the matching of patterns to learned templates. The templates that best predicted recognition were sequences of spatially identified units, ordered by latencies relative to each other (with minimal effects of sniff). Within templates, individual units contributed additively, with larger contributions from earlier-activated units. Our synthetic approach reveals the fundamental logic of the olfactory code and provides a general framework for testing links between sensory activity and perception.

Behavioral readout of spatio-temporal codes in olfaction.

Neural recordings performed at an increasing scale and resolution have revealed complex, spatio-temporally precise patterns of activity in the olfactory system. Multiple models may explain the functional consequences of the spatio-temporal olfactory code, but the link to behavior remains unclear. Recent evidence in the field suggests a behavioral sensitivity to both fine spatial and temporal features in the code. How these features and combinations of features give rise to olfactory behavior is the subject of active research in the field. Modern genetic and optogenetic methods show great promise in testing the link between olfactory codes and behavior.

Improvement of learning domains of nursing students with the use of authentic assessment pedagogy in clinical practice.

With evolving healthcare demands, nursing educators need to constantly review their teaching methodologies in order to enhance learners' knowledge and competency of skills in the clinical settings. Learning is an active process in which meaning is accomplished on the basis of experience and that authentic assessment pedagogy will enable nursing students to play an active part in their learning. The study was conducted with an aim to examine nursing students' learning domains through the introduction of the authentic assessment pedagogy during their clinical practice. A quasi-experimental study (n = 54) was conducted over a period of 10 weeks at a local tertiary hospital. The experimental group was exposed to the authentic assessment pedagogy and were taught to use the assessment rubrics as an instrument to help enhance their learning. Students were assessed and scored according to the assessment rubrics, which were categorized into four domains; cognitive, psychomotor, affective and critical thinking abilities. The findings indicated that an overall score for the four domains between the experimental and control groups were significant, with p value of <0.05. Critical thinking scores were indicative of consistent improvement within the experimental group. The findings confirmed that learning outcomes of the nursing students were enhanced through the early introduction of the authentic assessment pedagogy in the clinical setting.

Reconstructing representations of dynamic visual objects in early visual cortex.

As raw sensory data are partial, our visual system extensively fills in missing details, creating enriched percepts based on incomplete bottom-up information. Despite evidence for internally generated representations at early stages of cortical processing, it is not known whether these representations include missing information of dynamically transforming objects. Long-range apparent motion (AM) provides a unique test case because objects in AM can undergo changes both in position and in features. Using fMRI and encoding methods, we found that the "intermediate" orientation of an apparently rotating grating, never presented in the retinal input but interpolated during AM, is reconstructed in population-level, feature-selective tuning responses in the region of early visual cortex (V1) that corresponds to the retinotopic location of the AM path. This neural representation is absent when AM inducers are presented simultaneously and when AM is visually imagined. Our results demonstrate dynamic filling-in in V1 for object features that are interpolated during kinetic transformations.

Color updating on the apparent motion path.

When a static stimulus appears successively at two distant locations, we perceive illusory motion of the stimulus across them-long-range apparent motion (AM). Previous studies have shown that when the apparent motion stimuli differ in shape, interpolation between the two shapes is perceived across the AM path. In contrast, the perceived color during AM has been shown to abruptly change from the color of the first stimulus into that of the second, suggesting interpolation does not occur for color during AM. Here, we report the first evidence to our knowledge, that an interpolated color, distinct from the colors of either apparent motion stimulus, is represented as the intermediate percept on the path of apparent motion. Using carefully chosen target colors-cyan, pink, and lime-that are perceptually and neurally intermediate between blue and green, orange and magenta, and green and orange respectively, we show that detection of a target presented on the apparent motion path was impaired when the color of the target was "in-between" the initial and terminal stimulus colors. Furthermore, we show that this feature-specific masking effect for the intermediate color cannot be accounted for by color similarity between the intermediate color and the color of the terminal inducer. Our findings demonstrate that intermediate colors can be interpolated over the apparent motion trajectory as in the case of shape, possibly involving similar interpolation processes for shape and color during apparent motion.

Effects of synaptic connectivity on liquid state machine performance.

The Liquid State Machine (LSM) is a biologically plausible computational neural network model for real-time computing on time-varying inputs, whose structure and function were inspired by the properties of neocortical columns in the central nervous system of mammals. The LSM uses spiking neurons connected by dynamic synapses to project inputs into a high dimensional feature space, allowing classification of inputs by linear separation, similar to the approach used in support vector machines (SVMs). The performance of a LSM neural network model on pattern recognition tasks mainly depends on its parameter settings. Two parameters are of particular interest: the distribution of synaptic strengths and synaptic connectivity. To design an efficient liquid filter that performs desired kernel functions, these parameters need to be optimized. We have studied performance as a function of these parameters for several models of synaptic connectivity. The results show that in order to achieve good performance, large synaptic weights are required to compensate for a small number of synapses in the liquid filter, and vice versa. In addition, a larger variance of the synaptic weights results in better performance for LSM benchmark problems. We also propose a genetic algorithm-based approach to evolve the liquid filter from a minimum structure with no connections, to an optimized kernel with a minimal number of synapses and high classification accuracy. This approach facilitates the design of an optimal LSM with reduced computational complexity. Results obtained using this genetic programming approach show that the synaptic weight distribution after evolution is similar in shape to that found in cortical circuitry.