Biology subjects

Chandak, R.

Publications and source records attributed to Chandak, R..

4 recordsLinked to original sources

bioRxiv2021

Neural manifolds for odor-driven innate and acquired appetitive preferences

Sensory stimuli evoke spiking neural responses that innately or after learning drive suitable behavioral outputs. How are these spiking activities intrinsically patterned to encode for innate preferences, and could the neural response organization impose constraints on learning? We examined this issue in the locust olfactory system. Using a diverse odor panel, we found that ensemble activities both during ( ON response) and after stimulus presentations ( OFF response) could be linearly mapped onto overall appetitive preference indices. Although diverse, ON and OFF response patterns generated by innately appetitive odorants were still limited to a low-dimensional subspace (a neural manifold). Similarly, innately non-appetitive odorants evoked responses that were separable yet confined to another neural manifold. Notably, only odorants that evoked neural response excursions in the appetitive manifold were conducive for learning. In sum, these results provide insights on how encoding for innate preferences can also set limits on associative learning.

neuroscience↗

bioRxiv2020

Invariant Odor Recognition with ON-OFF Neural Ensembles

Invariant recognition of a stimulus is a challenging pattern-recognition problem that must be dealt with by all sensory systems. Since neural responses evoked by a stimulus could be perturbed in a multitude of ways, could a single scheme be devised to achieve this computational capability? We examined this issue in locust olfactory system. We found that odor-evoked responses in individual projection neurons in the locust antennal lobe varied unpredictably with repetition, stimulus dynamics, stimulus history, presence of background odorants, and changes in ambient conditions. Yet, a highly-constrained Bayesian logistic regression approach with ternary weights could provide robust odor recognition. We found that this approach could be further simplified: sum firing rates of ON neurons and subtract total activity in OFF neurons ( ON minus OFF classifier). Notably, we found that this approach could be generalized to develop a Boolean neural network that can perform well in a non-olfactory pattern recognition task.

neuroscience↗

bioRxiv2020

Explosive sensing with insect-based biorobots

Stand-off chemical sensing is an important capability with applications in several domains including homeland security. Engineered devices for this task, popularly referred to as electronic noses, have limited capacity compared to the broad-spectrum abilities of the biological olfactory system. Therefore, we propose a hybrid bio-electronic solution that directly takes advantage of the rich repertoire of olfactory sensors and sophisticated neural computational framework available in an insect olfactory system. We show that select subsets of neurons in the locust (Schistocerca americana) brain were activated upon exposure to various explosive chemical species (such as DNT and TNT). Responses from an ensemble of neurons provided a unique, multivariate fingerprint that allowed discrimination of explosive vapors from non-explosive chemical species and from each other. Notably, target chemical recognition could be achieved within a few hundred milliseconds of exposure. Finally, we developed a minimally-invasive surgical approach and mobile multi-unit electrophysiological recording system to tap into the neural signals in a locust brain and realize a biorobotic explosive sensing system. In sum, our study provides the first demonstration of how biological olfactory systems (sensors and computations) can be hijacked to develop a cyborg chemical sensing approach. SUMMARYWe demonstrate a bio-robotic chemical sensing approach where signals from an insect brain are directly utilized to detect and distinguish various explosive chemical vapors.

bioengineering↗

bioRxiv2017

Dynamic contrast enhancement and flexible odor codes

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.

neuroscience↗