A5091137440- Neurobiology and Insect Physiology Research
- Neural dynamics and brain function
- Insect and Arachnid Ecology and Behavior
- Advanced Memory and Neural Computing
- Plant and animal studies
- CCD and CMOS Imaging Sensors
- Advanced Fluorescence Microscopy Techniques
- Plant and Biological Electrophysiology Studies
- Visual perception and processing mechanisms
- Neuroscience and Neural Engineering
- Photoreceptor and optogenetics research
- Neural Networks and Reservoir Computing
- Neuroscience and Neuropharmacology Research
- Blind Source Separation Techniques
- Retinal Development and Disorders
- Insect behavior and control techniques
- Olfactory and Sensory Function Studies
- Insect Pheromone Research and Control
- Advanced Vision and Imaging
- Functional Brain Connectivity Studies
- Speech and Audio Processing
- Cell Image Analysis Techniques
- Music and Audio Processing
- EEG and Brain-Computer Interfaces
- Digital Holography and Microscopy
Fordham University
2023-2024
Columbia University
2014-2023
South China University of Technology
2023
University of Tübingen
1994
This paper presents an ultra-low-power voice activity detector (VAD). It uses analog signal processing for acoustic feature extraction (AFE) directly on the microphone output, approximate event-driven analog-to-digital conversion (ED-ADC), and digital deep neural network (DNN) speech/non-speech classification. New circuits, including low-noise amplifier, bandpass filter, full-wave rectifier contribute to more than 9× normalized power/channel reduction in front-end compared best prior art....
Voice user interfaces (UIs) are highly compelling for wearable and mobile devices. They have the advantage of using compact ultra-low-power (ULP) input devices (e.g. passive microphones). Together with ULP signal acquisition processing, voice UIs can give energy-harvesting acoustic sensor nodes battery-operating sought-after capability natural interaction humans. activity detection (VAD), separating speech from background noise, is a key building block in such UIs, e.g. it enable power...
In recent years, a wealth of Drosophila neuroscience data have become available including cell type and connectome/synaptome datasets for both the larva adult fly. To facilitate integration across modalities to accelerate understanding functional logic fruit fly brain, we developed FlyBrainLab, unique open-source computing platform that integrates 3D exploration visualization diverse with interactive modeled executable brain circuits. FlyBrainLab’s User Interface, Utilities Libraries Circuit...
Abstract The fruit fly is a key model organism for studying the activity of interconnected brain circuits. A large scattered global research community neurobiologists and neurogeneticists, computational theoretical neuroscientists, computer scientists engineers has been developing vast trove experimental modeling data that yet to be distilled into new knowledge understanding functional logic brain. Developing open shared models, modelling tools repositories can accessed from anywhere in...
In this paper, we investigate neural circuit architectures encoding natural visual scenes with neuron models consisting of dendritic stimulus processors (DSPs) in cascade biophysical spike generators (BSGs). DSPs serve as functional processing stimuli up to and including the neuron's active tree. BSGs model generation at axon hillock level where neurons respond aggregated synaptic currents. The highly nonlinear behavior calls for novel methods input/output (I/O) analysis circuits decoding...
The fruit fly's natural visual environment is often characterized by light intensities ranging across several orders of magnitude and rapidly varying contrast space time. Fruit fly photoreceptors robustly transduce and, in conjunction with amacrine cells, process scenes provide the resulting signal to downstream targets. Here, we model first step processing photoreceptor-amacrine cell layer. We propose a novel divisive normalization processor (DNP) for modeling computation taking place DNP...
We consider a class of neural circuit models with internal noise sources arising in sensory systems. The basic neuron model these circuits consists dendritic stimulus processor (DSP) cascaded biophysical spike generator (BSG). is modeled as set nonlinear operators that are assumed to have Volterra series representation. Biophysical point models, such the Hodgkin-Huxley neuron, used generator. address question how intrinsic affect precision encoding and decoding stimuli functional...
Previous research demonstrated that global phase alone can be used to faithfully represent visual scenes. Here we provide a reconstruction algorithm by using only local information. We also demonstrate effectively detect motion. The phase-based motion detector is akin models employed in biological vision, for example, the Reichardt detector. detection introduced here consists of two building blocks. first block measures/evaluates temporal change phase. derivative shown exhibit structure...
Summary The Fruit Fly Brain Observatory (FFBO) is a collaborative effort between experimentalists, theorists and computational neuroscientists at Columbia University, National Tsing Hua University Sheffield with the goal to (i) create an open platform for emulation biological validation of fruit fly brain models in health disease, (ii) standardize tools methods graphical rendering, representation manipulation circuits, (iii) data its abstractions support natural language queries, (iv) focus...
Summary NeuroNLP, is a key application on the Fruit Fly Brain Observatory platform (FFBO, http://fruitflybrain.org ), that provides modern web-based portal for navigating fruit fly brain circuit data. Increases in availability and scale of connectome data, demand new, scalable accessible methods to facilitate investigation into functions latest complex circuits being uncovered. NeuroNLP enables in-depth exploration structure circuits, using intuitive natural language queries are capable...
The Lamina is a neuropil that resides in the optic lobe of early visual system fruit fly. Its neural circuit consists some 700 ∼ 800 cartridges. A cartridge an atomic abstraction whose I/O behavior can be studied isolation. anatomy neurons, neurotransmitter types and connectivity patterns neurons are briefly reviewed. circuit-level model suitable for implementation on GPU platform presented detail. Cartridge interconnects create circuits with different characteristics. number composition...
Functional identification is a key methodology for uncovering the logic of neuroinformation processing brain circuits. For neural circuits modeling sensory systems as dynamical systems, complexity algorithm largely depends on number stimuli used. Neurons in these consist dendritic stimulus processors signal dendritric tree and biological spike generators spiking mechanism at axon hillock. Here, we review multi-sensory spatio-temporal that arise encoding auditory scenes, color visual fields,...
The Drosophila brain has only a fraction of the number neurons higher organisms such as mice and humans. Yet sheer complexity its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling function fruit fly at this scale poses significant challenges. To address these challenges, we present here programmable ontology expands scope current anatomy ontologies to encompass functional logic brain. provides language not for circuit motifs but also...
Associative memory in the Mushroom Body of fruit fly brain depends on encoding and processing odorants first three stages Early Olfactory System: Antenna, Antennal Lobe Calyx. The Kenyon Cells (KCs) Calyx provide compartments identity pure odorant mixtures encoded as a train spikes. Characterizing code underlying KC spike trains is major challenge neuroscience. To address this we start by explicitly modeling space using constructs both semantic syntactic information. Odorant semantics...
We investigate the sparse functional identification of complex cells and decoding spatio-temporal visual stimuli encoded by an ensemble cells. The reconstruction algorithm is formulated as a rank minimization problem that significantly reduces number sampling measurements (spikes) required for decoding. also establish duality between provide algorithms low-rank dendritic stimulus processors. enables us to efficiently evaluate our reconstructing novel in input space. Finally, we demonstrate...
Summary Recently, multiple focused efforts have resulted in substantial increase the availability of connectome data fruit fly brain. Elucidating neural circuit function from such structural calls for a scalable computational modeling methodology. We propose methodology that includes i) brain emulation engine, with an architecture can tackle complexity whole modeling, ii) database supports tight integration biological and along support domain specific queries transformations, iii) graphical...
Video Time Decoding Machines faithfully reconstruct bandlimited stimuli encoded with Encoding Machines. The key step in recovery calls for the pseudo-inversion of a typically poorly conditioned large scale matrix. We investigate realization time decoders employing only neural components. show that can be realized recurrent networks, describe their architecture and evaluate performance. provide first demonstration natural synthetic video scenes spike domain performance using latter decoder is...
Abstract Divisive normalization is a model of canonical computation brain circuits. We demonstrate that two cascaded divisive processors (DNPs), carrying out intensity/contrast gain control and elementary motion detection, respectively, can the robust detection realized by early visual system fruit fly. first introduce rewrite its underlying phase-based algorithm as feedforward processor. then cascade DNP modeling photoreceptor/amacrine cell layer with DNP. extensively evaluate for in...