scholarly journals A spiking neural program for sensory-motor control during foraging in flying insects

2020 ◽  
Author(s):  
Hannes Rapp ◽  
Martin Paul Nawrot
Keyword(s):  
Machine Learning ◽  
Motor Control ◽  
Neural Circuit ◽  
Systems Approach ◽  
Behavioral Control ◽  
Behavioral Strategies ◽  
Dynamic Memory ◽  
Flying Insects ◽  
Odor Plumes ◽  
Living Organisms

Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying neural circuits and computational principles we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning and motor control. We focus on cast & surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a spike-based plasticity rule the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast & surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning.

scholarly journals A spiking neural program for sensorimotor control during foraging in flying insects

2020 ◽  
Vol 117 (45) ◽  
pp. 28412-28421 ◽  
Author(s):  
Hannes Rapp ◽  
Martin Paul Nawrot
Keyword(s):  
Machine Learning ◽  
Neural Circuit ◽  
Systems Approach ◽  
Behavioral Control ◽  
Behavioral Strategies ◽  
Dynamic Memory ◽  
Flying Insects ◽  
Odor Plumes ◽  
Living Organisms ◽  
Short Time

Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying neural circuits and computational principles, we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning, and motor control. We focus on cast and surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a spike-based plasticity rule, the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast-and-surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning.

scholarly journals Neural computation underlying rapid learning and dynamic memory recall for sensori-motor control in insects

2020 ◽  
Author(s):  
Hannes Rapp ◽  
Martin Paul Nawrot
Keyword(s):  
Motor Control ◽  
Neural Circuit ◽  
Systems Approach ◽  
Behavioral Control ◽  
Memory Recall ◽  
Behavioral Strategies ◽  
Dynamic Memory ◽  
Flying Insects ◽  
Odor Plumes ◽  
Living Organisms

AbstractForaging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying nervous systems and abstract computational principles we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning and motor control. We focus on cast & surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a synaptic plasticity rule the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. Without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short timescales generates cast & surge motor commands. Our systems approach is generic and predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control.

scholarly journals Advances in automatic identification of flying insects using optical sensors and machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Carsten Kirkeby ◽  
Klas Rydhmer ◽  
Samantha M. Cook ◽  
Alfred Strand ◽  
Martin T. Torrance ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Sensors ◽  
Precision Agriculture ◽  
Insect Pests ◽  
Crop Damage ◽  
Automatic Identification ◽  
Flying Insects ◽  
Remote Sensor ◽  
Promising Solution ◽  
Environmentally Sensitive

AbstractWorldwide, farmers use insecticides to prevent crop damage caused by insect pests, while they also rely on insect pollinators to enhance crop yield and other insect as natural enemies of pests. In order to target pesticides to pests only, farmers must know exactly where and when pests and beneficial insects are present in the field. A promising solution to this problem could be optical sensors combined with machine learning. We obtained around 10,000 records of flying insects found in oilseed rape (Brassica napus) crops, using an optical remote sensor and evaluated three different classification methods for the obtained signals, reaching over 80% accuracy. We demonstrate that it is possible to classify insects in flight, making it possible to optimize the application of insecticides in space and time. This will enable a technological leap in precision agriculture, where focus on prudent and environmentally-sensitive use of pesticides is a top priority.

scholarly journals Spatial odor discrimination in hawkmoth, Manduca sexta (L.)

2020 ◽  
Author(s):  
P. Kalyanasundaram ◽  
M. A. Willis
Keyword(s):  
Spatial Distribution ◽  
Manduca Sexta ◽  
Flight Control ◽  
Spatial Information ◽  
Egg Laying ◽  
Proboscis Extension Reflex ◽  
Flying Insects ◽  
Odor Learning ◽  
Odor Plumes ◽  
Proboscis Extension

AbstractFlying insects track turbulent odor plumes to find mates, food and egg-laying sites. To maintain contact with the plume, insects are thought to adapt their flight control according to the distribution of odor in the plume using the timing of odor onsets and intervals between odor encounters. Although timing cues are important, few studies have addressed whether insects are capable of deriving spatial information about odor distribution from bilateral comparisons between their antennae in flight. The proboscis extension reflex (PER) associative learning protocol, originally developed to study odor learning in honeybees, was modified to show hawkmoths, Manduca sexta, can discriminate between odor stimuli arriving on either antenna. We show moths discriminated the odor arrival side with an accuracy of >70%. The information about spatial distribution of odor stimuli is thus available to moths searching for odor sources, opening the possibility that they use both spatial and temporal odor information.

scholarly journals FireProtDB: database of manually curated protein stability data

2020 ◽  
Vol 49 (D1) ◽  
pp. D319-D324
Author(s):  
Jan Stourac ◽  
Juraj Dubrava ◽  
Milos Musil ◽  
Jana Horackova ◽  
Jiri Damborsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
Elevated Temperatures ◽  
Recent Literature ◽  
Single Point ◽  
Biotechnological Applications ◽  
Computational Tools ◽  
Mild Conditions ◽  
Naturally Occurring ◽  
Stability Data ◽  
Living Organisms

Abstract The majority of naturally occurring proteins have evolved to function under mild conditions inside the living organisms. One of the critical obstacles for the use of proteins in biotechnological applications is their insufficient stability at elevated temperatures or in the presence of salts. Since experimental screening for stabilizing mutations is typically laborious and expensive, in silico predictors are often used for narrowing down the mutational landscape. The recent advances in machine learning and artificial intelligence further facilitate the development of such computational tools. However, the accuracy of these predictors strongly depends on the quality and amount of data used for training and testing, which have often been reported as the current bottleneck of the approach. To address this problem, we present a novel database of experimental thermostability data for single-point mutants FireProtDB. The database combines the published datasets, data extracted manually from the recent literature, and the data collected in our laboratory. Its user interface is designed to facilitate both types of the expected use: (i) the interactive explorations of individual entries on the level of a protein or mutation and (ii) the construction of highly customized and machine learning-friendly datasets using advanced searching and filtering. The database is freely available at https://loschmidt.chemi.muni.cz/fireprotdb.

scholarly journals A network underlying human higher-order motor control: Insights from machine learning-based lesion-behaviour mapping in apraxia of pantomime

Cortex ◽  
2019 ◽  
Vol 121 ◽  
pp. 308-321 ◽  
Author(s):  
Christoph Sperber ◽  
Daniel Wiesen ◽  
Georg Goldenberg ◽  
Hans-Otto Karnath
Keyword(s):  
Machine Learning ◽  
Motor Control ◽  
Higher Order

A Dynamic Body Model of the NematodeC. eleganswith Neural Oscillators

2005 ◽  
Vol 17 (3) ◽  
pp. 318-326 ◽  
Author(s):  
Michiyo Suzuki ◽  
◽  
Takeshi Goto ◽  
Toshio Tsuji ◽  
Hisao Ohtake ◽  
...  
Keyword(s):  
Computer Simulation ◽  
Motor Control ◽  
Caenorhabditis Elegans ◽  
Neural Circuit ◽  
Rhythmic Movement ◽  
Circuit Model ◽  
Neural Oscillators ◽  
C Elegans ◽  
Body Model ◽  
Multicellular Organisms

The nematode <I>Caenorhabditis elegans (C. elegans)</I>, a relatively simple organism in structure, is one of the most well-studied multicellular organisms. We developed a <I>virtual C. elegans</I> based on the actual organism to analyze motor control. We propose a dynamic body model, including muscles, controlled by a neural circuit model based on the actual nematode. The model uses neural oscillators to generate rhythmic movement. Computer simulation confirmed that the <I>virtual C. elegans</I> realizes motor control similar qualitatively to that of the actual organism. Specified classes of neurons are killed in the neural circuit model corresponding to actual <I>unc</I> mutants, demonstrating that resulting movement of the <I>virtual C. elegans</I> resembles that of actual mutants.

scholarly journals A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot

2017 ◽  
Vol 22 ◽  
pp. 33-36
Author(s):  
Ismael Baira Ojeda ◽  
Silvia Tolu ◽  
Moisés Pacheco ◽  
David Johan Christensen ◽  
Henrik Hautop Lund
Keyword(s):  
Machine Learning ◽  
Motor Control ◽  
Modular Robot

scholarly journals Deep learning: Using machine learning to study biological vision

2017 ◽  
Author(s):  
Najib J. Majaj ◽  
Denis G. Pelli
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Brain Activity ◽  
Neural Responses ◽  
Statistical Tool ◽  
Biological Vision ◽  
Vision Science ◽  
Living Organisms

ABSTRACTToday many vision-science presentations employ machine learning, especially the version called “deep learning”. Many neuroscientists use machine learning to decode neural responses. Many perception scientists try to understand how living organisms recognize objects. To them, deep neural networks offer benchmark accuracies for recognition of learned stimuli. Originally machine learning was inspired by the brain. Today, machine learning is used as a statistical tool to decode brain activity. Tomorrow, deep neural networks might become our best model of brain function. This brief overview of the use of machine learning in biological vision touches on its strengths, weaknesses, milestones, controversies, and current directions. Here, we hope to help vision scientists assess what role machine learning should play in their research.

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