Spatio-Temporal Memory for Navigation
in a Mushroom Body Model
Le Zhu
1(B) , Michael Mangan
2 , and Barbara Webb
1
1 School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
Le.zhu@ed.ac.uk
2 Sheffield Robotics, Department of Computer Science, University of Sheffield,
Sheffield S1 4DP, UK
Abstract. Insects, despite relatively small brains, can perform complex
navigation tasks such as memorising a visual route. The exact format
of visual memory encoded by neural systems during route learning and
following is still unclear. Here we propose that interconnections between
Kenyon cells in the Mushroom Body (MB) could encode spatio-temporal
memory of visual motion experienced when moving along a route. In our
implementation, visual motion is sensed using an event-based camera
mounted on a robot, and learned by a biologically constrained spiking
neural network model, based on simplified MB architecture and using
modified leaky integrate-and-fire neurons. In contrast to previous imagematching models where all memories are stored in parallel, the continuous visual flow is inherently sequential. Our results show that the model
can distinguish learned from unlearned route segments, with some tolerance to internal and external noise, including small displacements. The
neural response can also explain observed behaviour taken to support
sequential memory in ant experiments. However, obtaining comparable
robustness to insect navigation might require the addition of biomimetic
pre-processing of the input stream, and determination of the appropriate
motor strategy to exploit the memory output.
Keywords: Insect navigation · Mushroom body learning ·
Spatio-temporal memory · Sequence learning · Insect visual motion ·
Event-based camera
1 Introduction
In the task of finding food and going back home, visual memory is one of the
key cues used by insects. Ants can use their previous visual experience of a route
to follow it efficiently, even when other navigation cues such as path integration
are unavailable. Moreover, ants can quickly recognise familiar surroundings after
being blown off-course by a gust of wind or displaced by an experimenter [24].
Supported by China Scholarships Council (Grant No.201808060165).
c
Springer Nature Switzerland AG 2020
V. Vouloutsi et al. (Eds.): Living Machines 2020, LNAI 12413, pp. 415–426, 2020.
https://doi.org/10.1007/978-3-030-64313-3_39
in a Mushroom Body Model
Le Zhu
1(B) , Michael Mangan
2 , and Barbara Webb
1
1 School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
Le.zhu@ed.ac.uk
2 Sheffield Robotics, Department of Computer Science, University of Sheffield,
Sheffield S1 4DP, UK
Abstract. Insects, despite relatively small brains, can perform complex
navigation tasks such as memorising a visual route. The exact format
of visual memory encoded by neural systems during route learning and
following is still unclear. Here we propose that interconnections between
Kenyon cells in the Mushroom Body (MB) could encode spatio-temporal
memory of visual motion experienced when moving along a route. In our
implementation, visual motion is sensed using an event-based camera
mounted on a robot, and learned by a biologically constrained spiking
neural network model, based on simplified MB architecture and using
modified leaky integrate-and-fire neurons. In contrast to previous imagematching models where all memories are stored in parallel, the continuous visual flow is inherently sequential. Our results show that the model
can distinguish learned from unlearned route segments, with some tolerance to internal and external noise, including small displacements. The
neural response can also explain observed behaviour taken to support
sequential memory in ant experiments. However, obtaining comparable
robustness to insect navigation might require the addition of biomimetic
pre-processing of the input stream, and determination of the appropriate
motor strategy to exploit the memory output.
Keywords: Insect navigation · Mushroom body learning ·
Spatio-temporal memory · Sequence learning · Insect visual motion ·
Event-based camera
1 Introduction
In the task of finding food and going back home, visual memory is one of the
key cues used by insects. Ants can use their previous visual experience of a route
to follow it efficiently, even when other navigation cues such as path integration
are unavailable. Moreover, ants can quickly recognise familiar surroundings after
being blown off-course by a gust of wind or displaced by an experimenter [24].
Supported by China Scholarships Council (Grant No.201808060165).
c
Springer Nature Switzerland AG 2020
V. Vouloutsi et al. (Eds.): Living Machines 2020, LNAI 12413, pp. 415–426, 2020.
https://doi.org/10.1007/978-3-030-64313-3_39