A Computational Model for the Insect Brain

Arena, Paolo; Patané, Luca; Termini, Pietro Savio

doi:10.1007/978-3-319-02362-5_2

Cited by 5 publications

(7 citation statements)

References 30 publications

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“…Furthermore, while some vertebrate-inspired models (Gaussier et al, 2000 ; Jauffret et al, 2015 ) offer underlying spatial learning mechanisms based on place and view cells, many insect-inspired models have not linked PI and navigational capabilities to spatial learning and memory. A notable exception is a recent model based on the Drosophila brain show impressive results to generate adaptive behaviors in an autonomous agent, including exploration, visual landmark learning, and homing (Arena et al, 2014 ). However, the model has not been explicitly shown to be scalable for long-distance central-place foraging as observed in social insects.…”

Section: Introductionmentioning

confidence: 99%

A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents

2017

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Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called the home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories globally to the nest or based on visual landmarks. Although existing computational models reproduced similar behaviors, a neurocomputational model of vector navigation including the acquisition of vector representations has not been described before. Here we present a model of neural mechanisms in a modular closed-loop control—enabling vector navigation in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. In simulation, we show that the neural mechanisms enable robust homing and localization, even in the presence of external sensory noise. The proposed learning rules lead to goal-directed navigation and route formation performed under realistic conditions. Consequently, we provide a novel approach for vector learning and navigation in a simulated, situated agent linking behavioral observations to their possible underlying neural substrates.

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Section: Introductionmentioning

confidence: 99%

A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents

2017

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“…The Neural Reuse approach, on the other hand, states that the same neural structure can be concurrently exploited for different tasks. The insect MBs were already addressed as centers where such characteristics could be found, and the control structure herewith introduced makes a step forward to derive an efficient computational model directly useful as a robot behavioral controller (Arena and Patané, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

Arena

Strauß

et al. 2017

Front. Neurorobot.

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In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recurrent spiking neural network (SNN) with novel characteristics, able to memorize time evolutions of key parameters of the neural motor controller, so that existing motor primitives can be improved. The adopted control scheme enables the structure to efficiently cope with goal-oriented behavioral motor tasks. Here, a six-legged structure, showing a steady-state exponentially stable locomotion pattern, is exposed to the need of learning new motor skills: moving through the environment, the structure is able to modulate motor commands and implements an obstacle climbing procedure. Experimental results on a simulated hexapod robot are reported; they are obtained in a dynamic simulation environment and the robot mimicks the structures of Drosophila melanogaster.

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“…Within the insect brain, the mushroom bodies (MBs) and the central complex (CX) are the most studied neural assemblies for their enhanced characteristics in olfactory and visual learning: for example, rewarding and punishing olfactory associations were peculiarly addressed into the MBs of the insect brain [11, 12]. Efficient computational models were recently designed and implemented, which proved useful in addressing more complex behaviours like attention, expectation and decision-making [13]. On the other side, visual learning and visual targeting were addressed in the CX.…”

Section: The Spiking-based Neural Controllermentioning

confidence: 99%

“…On the other side, visual learning and visual targeting were addressed in the CX. A complete, updated insect brain computational architecture was recently presented in [13]. Here, the neural controller is a reduced model of the entire architecture, retaining the essential features needed for the assigned task.…”

Section: The Spiking-based Neural Controllermentioning

confidence: 99%

Emergence of Diversity in a Group of Identical Bio-Robots

Vitanza

Patané

Arena

2015

International Journal of Advanced Robotic Systems

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Learning capabilities, often guided by competition/ cooperation, play a fundamental and ubiquitous role in living beings. Moreover, several behaviours, such as feeding and courtship, involve environmental exploration and exploitation, including local competition, and lead to a global benefit for the colony. This can be considered as a form of global cooperation, even if the individual agent is not aware of the overall effect. This paper aims to demonstrate that identical biorobots, endowed with simple neural controllers, can evolve diversified behaviours and roles when competing for the same resources in the same arena. These behaviours also produce a benefit in terms of time and energy spent by the whole group. The robots are tasked with a classical foraging task structured through the cyclic activation of resources. The result is that each individual robot, while competing to reach the maximum number of available targets, tends to prefer a specific sequence of subtasks. This indirectly leads to the global result of task partitioning, whereby the cumulative energy spent, in terms of the overall travelled distance and the time needed to complete the task, tends to be minimized. A series of simulation experiments is conducted using different numbers of robots and scenarios: the common emergent result obtained is the role-specialization of each robot. The description of the neural controller and the specialization mechanisms are reported in detail and discussed.

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A Computational Model for the Insect Brain

Cited by 5 publications

References 30 publications

A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents

A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents

Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

Emergence of Diversity in a Group of Identical Bio-Robots

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