The functional logic of odor information processing in the Drosophila antennal lobe

Lazar, Aurel A.; Liu, Tingkai; Yeh, Chung-Heng

doi:10.1371/journal.pcbi.1011043

Cited by 4 publications

(2 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This suggests that, despite its nonlinearity, the class of DNPs can be used as computational building blocks in early sensory processing (Lazar et al. 2023 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Divisive normalization processors in the early visual system of the Drosophila brain

Lazar,

Zhou

2023

Biol Cybern

Self Cite

View full text Add to dashboard Cite

Divisive normalization is a model of canonical computation of brain circuits. We demonstrate that two cascaded divisive normalization processors (DNPs), carrying out intensity/contrast gain control and elementary motion detection, respectively, can model the robust motion detection realized by the early visual system of the fruit fly. We first introduce a model of elementary motion detection and rewrite its underlying phase-based motion detection algorithm as a feedforward divisive normalization processor. We then cascade the DNP modeling the photoreceptor/amacrine cell layer with the motion detection DNP. We extensively evaluate the DNP for motion detection in dynamic environments where light intensity varies by orders of magnitude. The results are compared to other bio-inspired motion detectors as well as state-of-the-art optic flow algorithms under natural conditions. Our results demonstrate the potential of DNPs as canonical building blocks modeling the analog processing of early visual systems. The model highlights analog processing for accurately detecting visual motion, in both vertebrates and invertebrates. The results presented here shed new light on employing DNP-based algorithms in computer vision.

show abstract

“…This suggests that, despite its nonlinearity, the class of DNPs can be used as computational building blocks in early sensory processing (Lazar et al. 2023 ).…”

Section: Introductionmentioning

confidence: 99%

“…1B implementing intensity and contrast gain control and elementary motion detection, respectively, can model the robust motion detection realized by the early visual system of the fruit fly brain. This suggests that, despite its nonlinearity, the class of DNPs can be used as computational building blocks in early sensory processing (Lazar et al 2023).…”

mentioning

confidence: 99%

Divisive normalization processors in the early visual system of the Drosophila brain

Lazar,

Zhou

2023

Biol Cybern

Self Cite

View full text Add to dashboard Cite

show abstract

Neuron synchronization analyzed through spatial-temporal attention

Yang,

Pramod,

Chen

et al. 2024

Preprint

View full text Add to dashboard Cite

Across diverse organisms, the temporal dynamics of spiking responses between neurons, the neural synchrony, is crucial for encoding different stimuli. Neural synchrony is especially important in the insect antennal (olfactory) lobe (AL). Previous studies on synchronization, however, rely on pair-wise synchronization metrics including the cross-correlogram and cos-similarity between kernelized spikes train. These pair-wise analyses overlook an important aspect of synchronization which is the interaction at the population neuron level. There are also limited modeling techniques that incorporate the synchronization between neurons in modeling population spike trains. Inspired by recent advancements in machine learning, we leverage a modern attention mechanism to learn a generative normalizing flow that captures neuron population synchronization. Our method not only reveals the spiking mechanism of neurons in the AL region but also produces semi-interpretable attention weights that characterize neuron interactions over time. These automatically learned attention weights allow us to elucidate the known principles of neuron synchronization and further shed light on the functional roles of different cell types (the local interneurons (LNs), and projection neurons (PNs)) in the dynamic neural network in the AL. By varying the balance of excitation and inhibition in this neural circuit, our method further uncovers the pattern between the strength of synchronization and the ratio of an odorant in the mixture.Author SummaryThe olfactory system can accurately compute the mixture of volatile compounds emitted from distant sources, enabling the foraging species to exhibit fast and effective decisions. However, altering ratios of one of the compounds in the mixture could be perceived as a different odor. Leveraging the current understanding of neural synchronization on sensory neural regions of insects, we construct a spatial-temporal attention normalizing flow, which partially replicates the AL region’s functionality by learning the spiking mechanics of neurons. Beyond providing insights of the spiking mechanism of neurons in the AL region, our method also produces semi-interpretable attention weights that characterize neuron interaction over time. These automatically learned attention weights allow us to dissect out the principles of neuron synchronization and interaction mechanisms between projection neurons (PNs) and local neurons (LNs). Utilizing our accurate model of these AL functionality, we show evidence that the behavioral relevant compounds are closely clustered together while varying the intensities of one of the behavioral compounds in the mixture could attenuate the synchronization

show abstract

The functional logic of odor information processing in the Drosophila antennal lobe

2023

View full text Add to dashboard Cite

Recent advances in molecular transduction of odorants in the Olfactory Sensory Neurons (OSNs) of the Drosophila Antenna have shown that the odorant object identity is multiplicatively coupled with the odorant concentration waveform. The resulting combinatorial neural code is a confounding representation of odorant semantic information (identity) and syntactic information (concentration). To distill the functional logic of odor information processing in the Antennal Lobe (AL) a number of challenges need to be addressed including 1) how is the odorant semantic information decoupled from the syntactic information at the level of the AL, 2) how are these two information streams processed by the diverse AL Local Neurons (LNs) and 3) what is the end-to-end functional logic of the AL? By analyzing single-channel physiology recordings at the output of the AL, we found that the Projection Neuron responses can be decomposed into a concentration-invariant component, and two transient components boosting the positive/negative concentration contrast that indicate onset/offset timing information of the odorant object. We hypothesized that the concentration-invariant component, in the multi-channel context, is the recovered odorant identity vector presented between onset/offset timing events. We developed a model of LN pathways in the Antennal Lobe termed the differential Divisive Normalization Processors (DNPs), which robustly extract the semantics (the identity of the odorant object) and the ON/OFF semantic timing events indicating the presence/absence of an odorant object. For real-time processing with spiking PN models, we showed that the phase-space of the biological spike generator of the PN offers an intuit perspective for the representation of recovered odorant semantics and examined the dynamics induced by the odorant semantic timing events. Finally, we provided theoretical and computational evidence for the functional logic of the AL as a robust ON-OFF odorant object identity recovery processor across odorant identities, concentration amplitudes and waveform profiles.

show abstract

The functional logic of odor information processing in the Drosophila antennal lobe

Cited by 4 publications

References 62 publications

Divisive normalization processors in the early visual system of the Drosophila brain

Divisive normalization processors in the early visual system of the Drosophila brain

Neuron synchronization analyzed through spatial-temporal attention

The functional logic of odor information processing in the Drosophila antennal lobe

Contact Info

Product

Resources

About