A neural network model for familiarity and context learning during honeybee foraging flights

Abstract

How complex is the memory structure that honeybees use to navigate? Recently, an insect-inspired parsimonious spiking neural network model was proposed that enabled simulated ground-moving agents to follow learned routes. We adapted this model to flying insects and evaluate the route following performance in three different worlds with gradually decreasing object density. In addition, we propose an extension to the model to enable the model to associate sensory input with a behavioral context, such as foraging or homing. The spiking neural network model makes use of a sparse stimulus representation in the mushroom body and reward-based synaptic plasticity at its output synapses. In our experiments, simulated bees were able to navigate correctly even when panoramic cues were missing. The context extension we propose enabled agents to successfully discriminate partly overlapping routes. The structure of the visual environment, however, crucially determines the success rate. We find that the model fails more often in visually rich environments due to the overlap of features represented by the Kenyon cell layer. Reducing the landmark density improves the agents route following performance. In very sparse environments, we find that extended landmarks, such as roads or field edges, may help the agent stay on its route, but often act as strong distractors yielding poor route following performance. We conclude that the presented model is valid for simple route following tasks and may represent one component of insect navigation. Additional components might still be necessary for guidance and action selection while navigating along different memorized routes in complex natural environments.

High-throughput simulations indicate feasibility of navigation by familiarity with a local sensor such as scorpion pectines

“…The hypothesis appears congruent with 61 numerous behavioral observations [29][30][31][32][33][34][35][36]. Furthermore, there are now robots and 62 simulated agents that navigate autonomously via familiarity [1,18,[37][38][39], and some 63 studies [40, 41] have suggested how neural tissue, such as the central complex and 64 mushroom bodies [42-51], might be organized to accommodate familiarity-based 65 navigation. 66 June 10, 2020 5/34Navigation by familiarity with a local sensor 67In this paper, we consider the hypothesis that the dense fields of peg sensilla on pectines 68 are analogous to the tightly packed ommatidia in compound eyes, detecting matrices of 69 chemical and textural information that are used for navigation by familiarity.…”

Designing Brains for Pain: Human to Mollusc

“…There is no evidence that complex learning in insects involves sentience ( Giurfa, 2013 ; Chittka, 2017 ). There is no need to assume conscious awareness in either insects or molluscs in order to explain complex behaviors when non-conscious neural networks can effectively account for such abilities ( Ardin et al, 2016 ; Faghihi et al, 2017 ; Goldschmidt et al, 2017 ; Müller et al, 2017 ; Peng and Chittka, 2017 ; Perry et al, 2017 ; Roper et al, 2017 ).…”

Circuit and Cellular Mechanisms Facilitate the Transformation from Dense to Sparse Coding in the Insect Olfactory System

“…In Drosophila, sparse coding was found to reduce overlap between odor representations and facilitate their discrimination (Lin et al, 2014). Consequently, sparse coding is an essential feature of plasticity models for olfactory learning in insects (Huerta and Nowotny, 2009;Wessnitzer et al, 2012;Ardin et al, 2016;Peng and Chittka, 2016;Müller et al, 2018), and theoretical work has emphasized the analogy of the transformation from a dense code in projection neurons (PNs) to a sparse code in Kenyon cells (KCs) with dimensionality expansion in machine learning methods (Huerta and Nowotny, 2009;Mosqueiro and Huerta, 2014;Schmuker et al, 2014).…”