Even simple sensory stimuli evoke neural responses that are dynamic and complex. Are the temporally patterned neural activities important for controlling the behavioral output? Here, we investigated this issue. Our results reveal that in the insect antennal lobe, due to circuit interactions, distinct neural ensembles are activated during and immediately following the termination of every odorant. Such non-overlapping response patterns are not observed even when the stimulus intensity or identities were changed. In addition, we find that ON and OFF ensemble neural activities differ in their ability to recruit recurrent inhibition, entrain field-potential oscillations and more importantly in their relevance to behaviour (initiate versus reset conditioned responses). Notably, we find that a strikingly similar strategy is also used for encoding sound onsets and offsets in the marmoset auditory cortex. In sum, our results suggest a general approach where recurrent inhibition is associated with stimulus ‘recognition' and ‘derecognition'.

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Sensory stimuli evoke spiking activities patterned across neurons and time that are hypothesized to encode information about their identity. Since the same stimulus can be encountered in a multitude of ways, how stable or flexible are these stimulus-evoked responses? Here, we examined this issue in the locust olfactory system. In the antennal lobe, we found that both spatial and temporal features of odor-evoked responses varied in a stimulus-history dependent manner. The response variations were not random, but allowed the antennal lobe circuit to enhance the uniqueness of the current stimulus. Nevertheless, information about the odorant identity became confounded due to this contrast-enhancement computation. Notably, a linear logical classifier (OR-of-ANDs) that can decode information distributed in flexible subsets of neurons generated predictions that matched results from our behavioral experiments. In sum, our results reveal a simple computational logic for achieving the stability vs. flexibility tradeoff in sensory coding. INTRODUCTIONThe key task of a sensory system is to transduce and represent information about environmental cues as electrical neural activities so that the organism may generate an appropriate behavioral response. The precise format in which neural activities represents stimulus specific information i.e. 'the neural code' has been a topic of great debate in neuroscience

Sensory stimuli evoke spiking activities patterned across neurons and time that are hypothesized to encode information about their identity. Since the same stimulus can be encountered in a multitude of ways, how stable or flexible are these stimulus-evoked responses? Here we examine this issue in the locust olfactory system. In the antennal lobe, we find that both spatial and temporal features of odor-evoked responses vary in a stimulus-history dependent manner. The response variations are not random, but allow the antennal lobe circuit to enhance the uniqueness of the current stimulus. Nevertheless, information about the odorant identity is conf ounded due to this contrast enhancement computation. Notably, predictions from a linear logical classifier (OR-of-ANDs) that can decode information distributed in flexible subsets of neurons match results from behavioral experiments. In sum, our results suggest that a trade-off between stability and flexibility in sensory coding can be achieved using a simple computational logic.

Structural changes in the choroid, a layer located between the retina and sclera, could indicate various vision impairments. Consequently, ophthalmologists inspect optical coherence tomography (OCT) scans of the posterior section of the eye towards making diagnosis. With a view to assist diagnosis, we propose an automated technique for segmentation of the choroid layer. Specifically, we detect the upper and lower boundaries of the choroid using structural similarity and adaptive Hessian analysis. Subsequently, we detect choroid vessels within those boundaries using a level set method. Experimental results are presented using spectral domain (SD) OCT images.