At the 2015 Computational Neuroscience Meeting in Prague, the Green Brain team is organizing the following workshop:
Invertebrates as Models of Cognition
Prague, Czech Republic
Wednesday and Thursday, July 22 and 23 2015
This workshop will discuss how invertebrate brains may provide useful models of the bases of cognition, in spite of their limited size and apparently limited cognitive abilities. Important aspects of the discussion will be how minimal cognitive substrates (micro-brains) can, in conjunction with embodiment, lead to surprisingly sophisticated behaviours, and what lessons we can draw from these examples for the general understanding of cognition. The workshop should be exciting to the computational neuroscience community, because it brings together ideas of full-brain models, embodied cognition, and promising new technologies in the form of GPU super-computing and autonomous robotics.
The invited speakers cover a variety of interesting topics ranging from the visual navigation of ants to odour processing in bees. Furthermore, the mixture of speakers from both the computational and experimental sides provide an important ingredient for future progress in the field.
James Marshall, Department of Computer Science, University of Sheffield
Kevin Gurney, Department of Psychology, University of Sheffield
Eleni Vasilaki, Department of Computer Science, University of Sheffield
Thomas Nowotny, Department of Informatics, University of Sheffield
(Click on names to see talk details)
9:20 – 9:25: Welcoming Remarks
9:25 – 10:10: Jeremy Niven (University of Sussex)
10:10 – 10:40: Coffee
10:40 – 11:25: Esin Yavuz (University of Sussex)
11:25 – 12:10: Alex Cope (University of Sheffield)
12:10 – 13:30: Lunch
13:30 – 14:15: Chelsea Sabo (University of Sheffield)
14:15 – 15:00: Jean-Marc Devaud (Universite Paul Sabatier)
15:00 – 15:30: Coffee
15:30 – 16:15: Martin Nawrot (Free University of Berlin)
17:00 – 17:15: Lianne Meah (University of Sheffield)
17:15 – 17:30: James Turner (University of Sussex)
17:30 – 17:35: Close of first day
Thursday, 23rd July:
9:20 – 9:25: Introduction
9:25 – 10:10: Lars Chittka (Queen Mary, University of London)
10:10 – 10:40: Coffee
10:40 – 11:25: Andy Philippides (University of Sussex)
11:25 – 12:10: Natalie Hempel de Ibarra (University of Exeter)
12:10 – 13:30: Lunch
13:30 – 14:15: Andrew Straw (Research Institute of Molecular Pathology)
14:15 – 15:00: Barbara Webb (University of Edinburgh)
15:00 – 15:20: Coffee
15:20 – 16:05: Giovanni Galizia (University of Konstanz)
16:05 – 16:50: Andrew Barron (Macquarie University)
16:50 – 17:35: Jeri Wright (University of Newcastle)
17:35 – 17:40: Closing remarks
Programme and Abstracts:
A Computational Model of Appetitive Conditioning-Induced Responses in the Early Olfactory System of the Honeybee
Speaker: Esin Yavuz, University of Sussex
Time: 10:40 – 11:25 Wednesday, 22nd July
Modelling Honeybee Vision at Multiple Levels of Detail
Speaker: Alex Cope, University of Sheffield
Time: 11:25 – 12:10 Wednesday, 22nd July
Quadcopters as a Basis for Studying Honeybee Cognition
Time: 13:30 – 14:15 Wednesday, 22nd July
The Crucial Role of Inhibitory Transmission for the Resolution of Ambiguities during Olfactory Reversal Learning
Time: 14:15 – 15:00 Wednesday, 22nd July
Reversal learning is a task which, unlike elemental learning, requires solving ambiguities about the links between events. In Pavlovian learning, for instance, two levels of stimulus ambiguity are represented by differential and reversal conditioning. In differential conditioning, a conditioned stimulus A is associated univocally with an Unconditioned Stimulus (US) while a distinct conditioned stimulus B is unambiguously associated with the absence of US (A+ B-). In reversal conditioning, a first conditioning phase (A+ B-) is followed by a second conditioning phase, which introduces an inversion of stimulus contingencies (A- B+). Thus, the addition of the second phase generates transient stimulus ambiguity (A+ A- and B- B+) that needs to be overcome to solve the problem.
In mammals, different brain structures are associated with learning forms exhibiting different levels of ambiguity. In insects, comparable results were found in the case of the honey bee, an insect which has a model status for studies on learning and memory1-4. In particular, pharmacological blocking of the mushroom bodies (MBs), higher-order brain structures associated with memory storage and retrieval1,4 impairs olfactory reversal learning but leaves intact the capacity to achieve two successive elemental olfactory discriminations5.
Here we aimed at uncovering the mechanisms underlying the implication of MBs in reversal learning. We reasoned that GABAergic feedback from the output region back to the input region of the MBs might be crucial to inhibit responses to previously reinforced odors during the reversal phase. We thus performed pharmacological experiments blocked GABAergic signaling in the MBs during olfactory reversal learning, by locally injecting antagonists of ionotropic or metabotropic GABA receptors, into the calyces or the vertical lobes. Our results show that GABAergic signaling is required for reversal learning but is dispensable for elemental learning. We thus provide a circuit-based explanation of the implication of MBs role in reversal learning.
- Menzel, R. (1999). Memory dynamics in the honeybee. J Comp Physiol A 185: 323-340.
- Giurfa M (2003) Cognitive neuroethology: dissecting non-elemental learning in a honeybee brain. Curr Opin Neurobiol, 13: 726–735.
- Giurfa M (2007) Behavioral and neural analysis of associative learning in the honeybee: a taste from the magic well. J Comp Physiol A, 193(8): 801-24.
- Giurfa, M. and Sandoz, J. C. (2012). Invertebrate learning and memory: fifty years of olfactory conditioning of the proboscis extension response in honeybees. Learn Mem 19: 54-66.
- Devaud J.M, Blunk A, Podufall J, Giurfa M, Grünewald B (2007) Using local anaesthetics to block neuronal activity and map specific learning tasks to the mushroom bodies of an insect brain. Eur J Neurosci, 26: 3193–3206.
Distributed Plasticity in a Network Model of the Honeybee Brain can Reproduce a Variety of Classical Conditioning Experiments
Time: 15:30 – 16:15 Wednesday, 22nd July
The honeybee is a prominent model for studying the neural mechanisms underlying the formation and retrieval of associative memories (Giurfa 2007). Bees can learn stimulus-reward association in a range of elemental and non-elemental paradigms and they show the learned behavior very rapidly, typically after one or two rewarded trials (Pamir et al., 2014). We device a neural network simulation of the honeybee. Our model can reproduce the observed conditioned response (CR) behavior of honeybees in a variety of classical olfactory conditioning protocols. Our network model comprises peripheral olfactory receptor neurons, the antennal lobe (AL) network, the mushroom body (MB) and the lateral horn (LH). Plasticity is included at two levels, the AL and the MB. Plasticity in the interneurons of the AL allows for a de-correlation of odor response patterns within few trials, reducing generalization across odorants during training. Plasticity in the MB underlies the formation of associations. As a result the population of MB output neurons reliably encodes the value of a stimulus, in line with previous physiological results in honeybees (Strube-Bloss, Nawrot & Menzel, 2011; Menzel 2014) and flies (Aso et al., 2014); see also companion poster #240 (Nawrot et al., 2015). We show that predictions derived from our network model match a range of behavioral data from elemental and non-elemental learning paradigms, including absolute and differential conditioning, trace conditioning, and odor patterning, retaining the rapid learning dynamics observed in the animals’ behavior.
- Aso, Y., Sitaraman, D., Ichinose, T., Kaun, K. R., Vogt, K., Belliart-Guérin, G., … & Rubin, G. M. (2014). Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila. Elife, 3, e04580.
- Giurfa, M. (2007). Behavioral and neural analysis of associative learning in the honeybee: a taste from the magic well. Journal of comparative physiology A, 193(8), 801-824.
- Menzel, R. (2014). The insect mushroom body, an experience-dependent recoding device. Journal of Physiology-Paris, 108(2), 84-95.
- Nawrot, M., D’Albis, T., Menzel, R., Strube-Bloss, M. (2015) Neural representation of a spatial odor memory in the honeybee mushroom body. Computational Neuroscience Meeting, July 18-23, Prague, Czech Republic
- Pamir, E., Szyszka, P., Scheiner, R., & Nawrot, M. P. (2014). Rapid learning dynamics in individual honeybees during classical conditioning. Frontiers in behavioral neuroscience, 8.
- Strube-Bloss, M. F., Nawrot, M. P., & Menzel, R. (2011). Mushroom body output neurons encode odor–reward associations. The Journal of neuroscience, 31(8), 3129-3140.
Relational Learning by Honeybees
Speaker: Aurore Averguès-Weber, Université Paul Sabatier
Time: 16:15 – 17:00 Wednesday, 22nd July
Honeybees possess impressive cognitive abilities in the visual domain. With dedicated training a bee can extract spatial relations between elements and thus resolve tasks based on spatial configuration recognition or relational rules. Such cognitive performance implies important abstract abilities as configuration processing or learnt relations are transferrable to totally novel stimuli except from the relations linking the elements. In addition, bees preferentially use spatial configurations over local or low-level features to recognize objects and easily detect invariant relations between visual features within complex stimuli under unsupervised learning conditions. These recently reported demonstrations raise interesting questions about the biological significance for an insect brain to invest resources in such high-level cognitive skills.
An Approach to Modelling Decision-Making using the Honeybee
Speaker: Lianne Meah, University of Sheffield
Time: 17:00 – 17:15 Wednesday, 22nd July
Visual Cognition in Bees – Behavioural Capacities and Neuronal Models
Time: 9:25 – 10:10 Thursday, 23rd July
Bees display visual-cognitive capacities that in some views conform to the basic criteria of concept learning, attention, sensitivity to number and metacognition. This raises the obvious question of how such capacities may be implemented at a neuronal level in the miniature brains of insects. We need to understand the neural circuits, not just the size of brain regions, which underlie these feats. Neural network analyses show that cognitive features found in insects, such as numerosity, attention and categorisation-like processes, may require only very limited neuron numbers. Using computational models of the bees’ visual system, we explore whether seemingly advanced cognitive capacities might ‘pop out’ of the properties of relatively basic neural processes in the insect visual periphery, and their connection with the mushroom bodies, higher order learning centres in the brains of insects.
A Dynamical Systems Approach to Cognition in Drosophila: from Known Visual Circuits to Models of Behavioral Switching
Time: 13:30 – 14:15 Thursday, 23rd July
System Identification in the Insect Olfactory System: Getting the Input Right using Computational Tools
Time: 15:20 – 16:05 Thursday, 23rd July
Cognition is built on dedicated subsystems: sensory input, evaluation, memory, action selection and ultimately behavioral control. In the olfactory system, attributing a meaning to an olfactory stimulus relies on odor identification. Odor meaning can be coded in evolution (innate systems), or in a lifetime (learned odors), with both systems working in parallel within most animals. However, no sensory system can be understood without understanding all of the sensory input that reaches the brain. This is particularly true for a system that is fundamentally combinatorial, such as the olfactory system. Even though there are situations where all the relevant information about the outside world resides in single information channels (e.g. by activating a pheromone receptor, the presence of that pheromone is conveyed without ambiguity), in most cases, the picture is more complex (e.g., the same pheromone in the wrong olfactory environment will not elicit the same response). The combinatorial nature of olfactory coding has one difficult consequence: no lab will be able to record all responses in all neurons to all possible olfactory stimuli. Therefore, we need a platform that can combine data from different laboratories, using different recording approaches, in order to build a consensus database.
We have built a database of olfactory odor responses in Drosophila (http://neuro.uni.kn/door). Far from being complete, this database is growing and needs to be expanded to include odor-concentration information, odor-mixture information, and dynamic information. We will present the underlying complexity and the current state of the project, and discuss exemplary results with respect to their meaning for the insect life.
Consciousness: A Comparative Approach
Speaker: Andrew Barron, Macquarie University
Time: 16:05 – 16:50 Thursday, 23rd July
A long-term goal for modern neuroscience is to establish the contribution of neurobiological systems to conscious experience. Almost all major endeavours of neuroscience have benefited enormously from a comparative approach that synthesises information and perspectives provided by different animal systems within an evolutionary framework. The study of consciousness has been an exception. It remains questionable whether any non-human research has made any significant contribution to determining mechanisms of consciousness. As there remains no consensus understanding of what consciousness is, the field has relied on experimental paradigms that are uncontroversially associated with consciousness in human subjects. This homocentric bias means that most neuroscientists consider animals – especially invertebrates — incapable of conscious experience. Here we propose that progress can be made if rather than attempting to define what consciousness is the focal question becomes a consideration of what is needed for consciousness. This then enables analysis of what neurobiological mechanisms are necessary to support different conscious capacities. Analysis of conscious capacity is more feasible in non-human animals than analysis of conscious experience, and the wide range of nervous systems across animal phyla provide an essential comparative resource with which to test key hypotheses.