A lightweight data-driven spiking neuronal network model of Drosophila olfactory nervous system with dedicated hardware support.

Data-driven spiking neuronal network (SNN) models enable in-silico analysis of the nervous system at the cellular and synaptic level. Therefore, they are a key tool for elucidating the information processing principles of the brain. While extensive research has focused on developing data-driven SNN models for mammalian brains, their complexity poses challenges in achieving precision. Network topology often relies on statistical inference, and the functions of specific brain regions and supporting neuronal activities remain unclear. Additionally, these models demand huge computing facilities and their simulation speed is considerably slower than real-time. Here, we propose a lightweight data-driven SNN model that strikes a balance between simplicity and reproducibility. The model is built using a qualitative modeling approach that can reproduce key dynamics of neuronal activity. We target the Drosophila olfactory nervous system, extracting its network topology from connectome data. The model was successfully implemented on a small entry-level field-programmable gate array and simulated the activity of a network in real-time. In addition, the model reproduced olfactory associative learning, the primary function of the olfactory system, and characteristic spiking activities of different neuron types. In sum, this paper propose a method for building data-driven SNN models from biological data. Our approach reproduces the function and neuronal activities of the nervous system and is lightweight, acceleratable with dedicated hardware, making it scalable to large-scale networks. Therefore, our approach is expected to play an important role in elucidating the brain's information processing at the cellular and synaptic level through an analysis-by-construction approach. In addition, it may be applicable to edge artificial intelligence systems in the future.

Isolation and reconstruction of cardiac mitochondria from SBEM images using a deep learning-based method.

Mitochondrial morphological defects are a common feature of diseased cardiac myocytes. However, quantitative assessment of mitochondrial morphology is limited by the time-consuming manual segmentation of electron micrograph (EM) images. To advance understanding of the relation between morphological defects and dysfunction, an efficient morphological reconstruction method is desired to enable isolation and reconstruction of mitochondria from EM images. We propose a new method for isolating and reconstructing single mitochondria from serial block-face scanning EM (SBEM) images. CDeep3M, a cloud-based deep learning network for EM images, was used to segment mitochondrial interior volumes and boundaries. Post-processing was performed using both the predicted interior volume and exterior boundary to isolate and reconstruct individual mitochondria. Series of SBEM images from two separate cardiac myocytes were processed. The highest F1-score was 95% using 50 training datasets, greater than that for previously reported automated methods and comparable to manual segmentations. Accuracy of separation of individual mitochondria was 80% on a pixel basis. A total of 2315 mitochondria in the two series of SBEM images were evaluated with a mean volume of 0.78 µm3. The volume distribution was very broad and skewed; the most frequent mitochondria were 0.04-0.06 µm3, but mitochondria larger than 2.0 µm3 accounted for more than 10% of the total number. The average short-axis length was 0.47 µm. Primarily longitudinal mitochondria (0-30 degrees) were dominant (54%). This new automated segmentation and separation method can help quantitate mitochondrial morphology and improve understanding of myocyte structure-function relationships.

Multisensory enhancement of burst activity in an insect auditory neuron.

Detecting predators is crucial for survival. In insects, a few sensory interneurons receiving sensory input from a distinct receptive organ extract specific features informing the animal about approaching predators and mediate avoidance behaviors. Although integration of multiple sensory cues relevant to the predator enhances sensitivity and precision, it has not been established whether the sensory interneurons that act as predator detectors integrate multiple modalities of sensory inputs elicited by predators. Using intracellular recording techniques, we found that the cricket auditory neuron AN2, which is sensitive to the ultrasound-like echolocation calls of bats, responds to airflow stimuli transduced by the cercal organ, a mechanoreceptor in the abdomen. AN2 enhanced spike outputs in response to cross-modal stimuli combining sound with airflow, and the linearity of the summation of multisensory integration depended on the magnitude of the evoked response. The enhanced AN2 activity contained bursts, triggering avoidance behavior. Moreover, cross-modal stimuli elicited larger and longer lasting excitatory postsynaptic potentials (EPSP) than unimodal stimuli, which would result from a sublinear summation of EPSPs evoked respectively by sound or airflow. The persistence of EPSPs was correlated with the occurrence and structure of burst activity. Our findings indicate that AN2 integrates bimodal signals and that multisensory integration rather than unimodal stimulation alone more reliably generates bursting activity. NEW & NOTEWORTHY Crickets detect ultrasound with their tympanum and airflow with their cercal organ and process them as alert signals of predators. These sensory signals are integrated by auditory neuron AN2 in the early stages of sensory processing. Multisensory inputs from different sensory channels enhanced excitatory postsynaptic potentials to facilitate burst firing, which could trigger avoidance steering in flying crickets. Our results highlight the cellular basis of multisensory integration in AN2 and possible effects on escape behavior.

Auditory modulation of wind-elicited walking behavior in the cricket Gryllus bimaculatus.

Animals flexibly change their locomotion triggered by an identical stimulus depending on the environmental context and behavioral state. This indicates that additional sensory inputs in different modality from the stimulus triggering the escape response affect the neuronal circuit governing that behavior. However, how the spatio-temporal relationships between these two stimuli effect a behavioral change remains unknown. We studied this question, using crickets, which respond to a short air-puff by oriented walking activity mediated by the cercal sensory system. In addition, an acoustic stimulus, such as conspecific 'song' received by the tympanal organ, elicits a distinct oriented locomotion termed phonotaxis. In this study, we examined the cross-modal effects on wind-elicited walking when an acoustic stimulus was preceded by an air-puff and tested whether the auditory modulation depends on the coincidence of the direction of both stimuli. A preceding 10 kHz pure tone biased the wind-elicited walking in a backward direction and elevated a threshold of the wind-elicited response, whereas other movement parameters, including turn angle, reaction time, walking speed and distance were unaffected. The auditory modulations, however, did not depend on the coincidence of the stimulus directions. A preceding sound consistently altered the wind-elicited walking direction and response probability throughout the experimental sessions, meaning that the auditory modulation did not result from previous experience or associative learning. These results suggest that the cricket nervous system is able to integrate auditory and air-puff stimuli, and modulate the wind-elicited escape behavior depending on the acoustic context.