Abstract. We developed a computational model of learning in the Mushroom Body, a region of multimodal integration in the insect brain. Using realistic neural dynamics and a biologically-based learning rule (spike timing dependent plasticity), the model is tested as part of an insect brain inspired architecture within a closed loop behavioural task. Replicating in simulation an experiment carried out on bushcrickets, we show the system can successfully associate visual to auditory cues, so as to maintain a steady heading towards an intermittent sound source. 

Webb, Barbara

Wessnitzer, Jan

Jan Wessnitzer

Barbara Webb

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A neural model of cross-modal association in insects

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     Edinburgh Research Explorer                                      A neural model of cross-modal association in insectsCitation for published version:Wessnitzer, J & Webb, B 2007, A neural model of cross-modal association in insects. in ESANN'2007proceedings - European Symposium on Artificial Neural Networks. pp. 415-420.Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Publisher's PDF, also known as Version of recordPublished In:ESANN'2007 proceedings - European Symposium on Artificial Neural NetworksGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 05. Apr. 2019A neural model of cross-modal associationin insectsJan Wessnitzer and Barbara Webb ∗Institute of Perception, Action and Behaviour - School of InformaticsUniversity of Edinburgh - United KingdomAbstract. We developed a computational model of learning in the Mush-room Body, a region of multimodal integration in the insect brain. Usingrealistic neural dynamics and a biologically-based learning rule (spike tim-ing dependent plasticity), the model is tested as part of an insect braininspired architecture within a closed loop behavioural task. Replicatingin simulation an experiment carried out on bushcrickets, we show the sys-tem can successfully associate visual to auditory cues, so as to maintain asteady heading towards an intermittent sound source.1 IntroductionA large body of evidence suggests that the Mushroom Body (MB), a distinctregion in the insect brain, plays a cardinal role in adaptive behaviour (see [1] for areview). We propose a minimalist architecture, based on this neural circuit, ableto modulate reﬂex behaviours in closed-loop conditions i.e. where the system’soutput inﬂuences the system’s inputs. We test it for the simulated task of usingvisual cues for maintaining a course towards a sound source, as observed in thebushcricket Poecilimon aﬃnis [2].Male bushcrickets are able to maintain a straight course to a female, bycoupling visual cues to an acoustically detected direction [2]. Stabilising eﬀectsof visual information on course maintenance are found in other insects (c.f., [3]),but in this case it was also shown that optical cues could stand in for (temporarilyabsent) auditory signals. In particular, the animal could quickly learn to walk atan arbitrary angle to a visual landmark, corresponding to the sound direction.In the absence of sound it would follow the displacement of the landmark withan appropriate change in walking direction. In a comparable task, the MB ofthe cockroach have been shown to play a role in place memory relating distantvisual cues to an invisible target [4]. Here we show how the neural architectureof the MB can account for such capabilities, using a biologically plausible neuralrepresentation and learning rule.2 Model description2.1 Neuron modelWe chose the neuron model proposed by Izhikevich [5] since it exhibits biologi-cally plausible dynamics, similar to Hodgkin-Huxley-type neurons, but is com-∗This work was supported by the European Commission under project FP6-2003-IST2-004690 SPARK.415ESANN'2007 proceedings - European Symposium on Artificial Neural NetworksBruges (Belgium), 25-27 April 2007, d-side publi., ISBN 2-930307-07-2.putationally less expensive and thus, suitable for large-scale simulation. Changein membrane potential v (mV) is modelled as:Cdvdt= k(v − vr)(v − vt)− u + I + [ξ ∼ N(0, σ)] (1)dudt= a(b(v − vr)− u), (2)where u is the recovery current and I the synaptic input. a = 0.3, b = −0.2,c = −65, d = 8, and k = 2 are model parameters. C = 100 (pF) is thecapacitance, vr = −60mV is the resting potential, and vt = −40mV is theinstantaneous threshold potential. ξ is a Gaussian noise term with standarddeviation σ = 1. The variables v and u are reset if membrane potential exceedsa threshold (v ≥ +35mV): {v ← cu← u + d . (3)Synaptic inputs are modelled by:I(t + Δt) = gS(t)(vrev − v(t)), (4)where vrev (mV) is the reversal potential of the synapse and g (nS) is thesynaptic conductance. The reversal potential of excitatory synapses is 0mV,whereas that of inhibitory synapses is −90mV. S(t) is the amount of neuro-transmitter active at the synapse at time t and is updated as follows:S(t + Δt) ={S(t)e−Δtτsyn + δ , if presynaptic spikeS(t)e−Δtτsyn , otherwise, (5)where δ = 0.5 is the amount of neurotransmitter released when a presynapticspike occurred, τsyn (ms) is the synaptic timescale, and the simulation timestepΔt was set to 0.25ms.2.2 Neural architectureThe neural architecture for the agent is based on the insect brain; in partic-ular, on evidence that the MB is involved in modulating more basic, reﬂexivebehaviours [1]. Insect behaviour is often viewed as collections of parallel, highlyspecialised sensorimotor loops, and such loops form the basis of our proposedarchitecture (such as the phonotaxis loop in ﬁgure 1). However, collectionsof reﬂex-loops are insuﬃcient for explaining the behavioural complexity of in-sects [6]. Higher brain centers, such as the MB, act to modulate the directsensorimotor loops in context-dependant and heterarchical manner (c.f., [6, 7]).Indirect secondary pathways via the MB, which form a parallel route for sen-sory inﬂow, are used to place information from various sensory modalities or416ESANN'2007 proceedings - European Symposium on Artificial Neural NetworksBruges (Belgium), 25-27 April 2007, d-side publi., ISBN 2-930307-07-2.other domain-speciﬁc sensorimotor loops into context, and to form associationsbetween diﬀerent inputs.We have used this architecture in previous modelling of non-elemental asso-ciative learning [8]. In the current experiment, the reﬂex pathway represents aresponse to sound (see ﬁgure 1). Each spike of the output (left or right) neu-rons of the phonotaxis (sound localising) circuit (based on [9]) cause the agent toturn by 1 degree in the direction of the sound source and also excite the extrinsicneurons (EN). The visual position of the landmark is mapped onto projectionneurons (PN) that activate the Kenyon cells (KC) that form the mushroombody. These converge on the extrinsic neurons. During conditioning, the soundis on and the agent moves towards it. After conditioning, the agent should haveassociated the required movements with a particular landmark direction, andthus be able to control its course using only visual cues.Phonotaxis Visual inputExcitatoryInhibitoryENKCPNLeft RightMotor outputFig. 1: The implemented MB network receives sensory cues from the visual ﬁeldvia projection neurons (PN), which make direct excitatory connections to theKenyon cells (KC). The MB output converges on a small number of extrinsicneurons (EN), which are also excited by the underlying direct reﬂex pathways,and can activate these pathways. Learning occurs between the KC and EN.PN layer. This layer consists of 72 neurons giving a visual resolution of36072 = 5 degrees. We assumed preprocessing of visual information and thatreceptive ﬁelds are not overlapping. Each PN encodes a particular relative an-gle towards the landmark. The neurotransmitter release (at a PN when thelandmark is in view at its particular relative angle) is calculated as follows:S(t + Δt) = S(t) + δ.KC layer. The KC layer consists of 72 neurons. The topographical organi-sation of the PN layer is maintained, i.e. each KC only receives input from onePN. The synaptic strength of PN-KC synapses (gPN,KC) were set at random inthe interval [20,30].EN layer. The EN layer contains 2 neurons and each EN is connected to all417ESANN'2007 proceedings - European Symposium on Artificial Neural NetworksBruges (Belgium), 25-27 April 2007, d-side publi., ISBN 2-930307-07-2.KC, i.e., every KC-EN pair is connected (τKC,EN = 5ms). Learning occurs onlythrough modulation of the KC-EN connections. However, the synaptic conduc-tance gKC,EN for all synapses is initialised to 0, and is subsequently modiﬁedas described below. The EN neurons also receive excitatory input from the un-derlying reﬂex pathways, thus the learning reﬂects the coincidence of activity inthese pathways and the activity in the KC layer.2.3 Spike Time-Dependent Plasticity (STDP)Synapses are modiﬁed using STDP which has been observed in biological neuralsystems (e.g., [10]). In STDP, synaptic change depends on the relative timingof pre- and post-synaptic action potentials. Synaptic conductances are adaptedby the following STDP rule:Δg =⎧⎨⎩ A+etpre−tpostτ+ , if tpre − tpost < 0A−e−(tpre−tpost)τ− , if tpre − tpost ≥ 0, (6)where tpre and tpost are the spiking times of the pre- and postsynaptic neuronrespectively. A+ = 20, A− = −20, τ+ = 10ms, and τ− = 5ms are parameters.If this modiﬁcation rule of synaptic conductances g pushes the values out ofthe allowed range 0 ≤ g ≤ gmax, g is set to the appropriate limiting value(gmax = 50).A ‘forgetting’ factor is introduced in the form of a slow decay of g. At eachtimestep,g(t + Δt) = g(t)eΔtτdecay , (7)where τdecay = 105 (ms). The coupling of visual cues to auditory signalsshould only be temporary (c.f., [2]).3 ResultsThe model was tested in two scenarios. In one, the agent walks on a treadmill sothat it can only change its orientation - this is directly comparable to the origi-nal behavioural experiments on the bushcricket. In the other, the agent movesin an arena towards the sound source, which is more like the natural interac-tion of the insect with the environmental cues. In each case the conditioningtrials (with constant sound) lasted a total of 120s, with the agent starting ata random heading. The conditioning trials were repeated and reset as follows.In the ‘treadmill’ scenario the heading was randomly reset every 10s, whereasin the ‘arena’ scenario if it arrived within a small radius of the sound source itwas returned to its starting position (with a random heading) and repeated itsapproach.In the ‘arena’ scenario, coupling visual cues to phonotactic behaviour hasa stabilising eﬀect on course maintenance. As a measure we used the Vector418ESANN'2007 proceedings - European Symposium on Artificial Neural NetworksBruges (Belgium), 25-27 April 2007, d-side publi., ISBN 2-930307-07-2.(a) (b) (c)PN 2PN 1PN 3PN 2PN 1PN 3KC 2KC 1KC 3KC 2KC 1KC 3EN 1EN 2LEFTRIGHTEN 1EN 2LEFTRIGHTPN 1 2 3472 571..........Fig. 2: Schematic drawing of neural activity. As the agent zig-zags towards thesound source (a), turning is controlled by the output (left, right) of the phono-taxis network (b) activating the extrinsic neurons (EN). During these move-ments, the visual stimulus activates several diﬀerent projection neurons (PN)which activate their respective Kenyon cells (KC). The system learns to asso-ciate KC and EN ﬁring, so that after conditioning (c) the visual stimulus aloneis able to control turning.Length (VL) deﬁned as the quotient of the distance from start position to theposition of the source and the actual path length. The mean VL was 0.59(s.d.=0.1) before conditioning and 0.818 (s.d.=0.182) after conditioning. Moreimportantly, the landmark could now be used to stand in for the sound. Thiswas tested by looking at the mean angle that the agent moved with sound oﬀwhen the landmark was displaced. As shown in ﬁgure 3, the agent’s directionis appropriate to the conditioning angle and follows the landmark displacementconsistently, for both the ‘treadmill’ scenario (ﬁgure 3a, closely comparable tothe biological data seen in ﬁgure 9 in [2]) and the more naturalistic ‘arena’scenario (ﬁgure 3b). The system has associated each possible visual position ofthe landmark with the movement required to re-orient so that the landmark fallsinto the visual position it occupied during walking towards the sound.4 Discussion and future workAnimal behaviour is a continuous closed loop, with sensory events transformedby the agent into motor actions, and these actions transformed by the envi-ronment into new sensory events. Neural learning mechanisms should thus beevaluated in a closed loop context if they are to be considered biologically rel-evant. A fundamental role of learning for behaving animals is associating thereﬂex response to one cue with another cue that can reﬁne, predict or substitutefor the original cue. In the bushcricket example used here, the visual informationcan stabilise auditory localisation behaviour and be used to maintain the correctheading when the sound is turned oﬀ. We have replicated this capability in asimulation, using a plausible model of insect brain circuitry.In future work we will evaluate the system using more realistic modelling of419ESANN'2007 proceedings - European Symposium on Artificial Neural NetworksBruges (Belgium), 25-27 April 2007, d-side publi., ISBN 2-930307-07-2.−20 0 20 40 60 80 100 120 140 160 180−20020406080100120140160180landmark displacement (in degrees)heading angle (in degrees)conditioning angle θ=45−20 0 20 40 60 80 100 120 140 160 180050100150200landmark displacement (in degrees)heading angle (in degrees)conditiong angle θ=90θ=135θ=45Fig. 3: Mean heading angle plotted versus landmark displacement. (a) The agentcan turn but does not move forward, simulating an insect on a treadmill. (b)The agent can move through the environment. In each case the agent maintainsthe appropriate relative heading as the landmark is displaced.the input (auditory and visual) and output (motor) functions, with the eventualaim of illustrating the same learning capabilities on a real robot. The sameneural architecture will also be tested for the related task of place memory, inwhich a particular home location is associated with an array of landmarks, abehaviour known to be dependent on the Mushroom Body in insects [4].References[1] J. Wessnitzer and B. Webb. Multimodal sensory integration in insects - towards insectbrain control architectures. Bioinspiration and Biomimetics, 1:63–75, 2006.[2] D. von Helversen and G. Wendler. Coupling of visual to auditory cues during phonotacticapproach in the phaneropterine bushcricket Poecilimon aﬃnis. Journal of ComparativePhysiology A, 186:729–736, 2000.[3] H. Bo¨hm, K. Schildberger, and F. Huber. Visual and acoustic course control in the cricketGryllus bimaculatus. Journal of Experimental Biology, 159:235–248, 1991.[4] M. Mizunami, J. Weibrecht, and N. Strausfeld. Mushroom bodies of the cockroach: theirparticipation in place memory. Journal of Comparative Neurology, 402:520–537, 1998.[5] E. Izhikevich. Dynamical systems in neuroscience: the geometry of excitability and burst-ing. The MIT Press, Cambridge, Massachusetts, 2006.[6] R. Menzel and M. Giurfa. Cognitive architecture of a mini-brain: the honeybee. Trendsin Cognitive Sciences, 5(2):62–71, 2001.[7] R. Okada, M. Sakura, and M. Mizunami. Distribution of dendrites of descending neu-rons and its implications for the basic organisation of the cockroach brain. Journal ofComparative Neurology, 458:158–174, 2003.[8] J. Wessnitzer, B. Webb, and D. Smith. A model of non-elemental associative learningin the Mushroom Body neuropil of the insect brain. In Proceedings of the InternationalConference on Adaptive and Natural Computing Algorithms ICANNGA’07, 2007.[9] B. Webb and T. Scutt. A simple latency dependent spiking neuron model of cricketphonotaxis. Biological Cybernetics, 82(3):247–269, 2000.[10] P. Roberts and C. Bell. Spike timing dependent synaptic plasticity in biological systems.Biological Cybernetics, 87:392–403, 2002.420ESANN'2007 proceedings - European Symposium on Artificial Neural NetworksBruges (Belgium), 25-27 April 2007, d-side publi., ISBN 2-930307-07-2.

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A neural model of cross-modal association in insects

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