Perceiving materials' fabrics in biology and in neuromorphic: a deeper analysis of a novel neuromorphic structure
Type and duration:
Master student (duration depends on your study program).
Humans perceive fine tactile textures (like fabrics or metal) sliding their finger on the surface of an object . Biological findings suggest that the vibrations generated by the sliding movement are captured by specific sensors on the skin (called mechanoreceptors) and transmitted to the brain . How does the brain extract the information contained in these vibrations? To answer this question our group proposed a novel structure to decode the frequency of the vibration, the neuromorphic sPLL. While preliminary results show that the designed network is in principle able to decode frequencies, deeper analyses are still lacking. During your master thesis you will explore deeper the working regime of the sPLL. Furthermore, you will compare its behaviour with biological data, provided by neuroscientists, to discover if the proposed structure's behaviour is similar to the one present in the brain.
For this project a good knowledge of Python (numpy, matplotlib ecc) is required, along with motivation and love for science. A shallow understanding of spiking neural networks and neuroscience is preferable but not mandatory.
- Understanding the novel structure sPLL  proposed in the lab for decoding texture.
- Characterization of sPLL with different stimuli with Brian2  and TouchSim .
- Comparing the sPLL behaviour with biological dataset generated by neuroscientists.
python programming, interest in computational and cognitive neurosciences. Interest in spiking neural networks.
- E. L. Mackevicius, M. D. Best, H. P. Saal, e S. J. Bensmaia, «Millisecond Precision Spike Timing Shapes Tactile Perception», Journal of Neuroscience, vol. 32, n. 44, pagg. 15309–15317, ott. 2012, doi: 10.1523/JNEUROSCI.2161-12.2012.
- A. I. Weber et al., «Spatial and temporal codes mediate the tactile perception of natural textures», Proceedings of the National Academy of Sciences, vol. 110, n. 42, pagg. 17107–17112, 2013, doi: 10.1073/pnas.1305509110.
- M. Mastella and E. Chicca, "A Hardware-friendly Neuromorphic Spiking Neural Network for Frequency Detection and Fine Texture Decoding," 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021, pp. 1-5, doi: 10.1109/ISCAS51556.2021.9401377.
- Brian2, https://brian2.readthedocs.io/en/stable/
- TouchSim, https://github.com/hsaal/touchsim
|Last modified:||21 September 2022 3.46 p.m.|