All Publications

  • Organic neuromorphic devices: Past, present, and future challenges MRS BULLETIN Tuchman, Y., Mangoma, T. N., Gkoupidenis, P., van de Burgt, Y., John, R., Mathews, N., Shaheen, S. E., Daly, R., Malliaras, G. G., Salleo, A. 2020; 45 (8): 619–30
  • A biohybrid synapse with neurotransmitter-mediated plasticity. Nature materials Keene, S. T., Lubrano, C., Kazemzadeh, S., Melianas, A., Tuchman, Y., Polino, G., Scognamiglio, P., Cinà, L., Salleo, A., van de Burgt, Y., Santoro, F. 2020


    Brain-inspired computing paradigms have led to substantial advances in the automation of visual and linguistic tasks by emulating the distributed information processing of biological systems1. The similarity between artificial neural networks (ANNs) and biological systems has inspired ANN implementation in biomedical interfaces including prosthetics2 and brain-machine interfaces3. While promising, these implementations rely on software to run ANN algorithms. Ultimately, it is desirable to build hardware ANNs4,5 that can both directly interface with living tissue and adapt based on biofeedback6,7. The first essential step towards biologically integrated neuromorphic systems is to achieve synaptic conditioning based on biochemical signalling activity. Here, we directly couple an organic neuromorphic device with dopaminergic cells to constitute a biohybrid synapse with neurotransmitter-mediated synaptic plasticity. By mimicking the dopamine recycling machinery of the synaptic cleft, we demonstrate both long-term conditioning and recovery of the synaptic weight, paving the way towards combining artificial neuromorphic systems with biological neural networks.

    View details for DOI 10.1038/s41563-020-0703-y

    View details for PubMedID 32541935

  • Organic Transistors Incorporating Lipid Monolayers for Drug Interaction Studies ADVANCED MATERIALS TECHNOLOGIES Cavassin, P., Pappa, A., Pitsalidis, C., Barbosa, H. P., Colucci, R., Saez, J., Tuchman, Y., Salleo, A., Faria, G. C., Owens, R. M. 2019
  • Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing SCIENCE Fuller, E. J., Keene, S. T., Melianas, A., Wang, Z., Agarwal, S., Li, Y., Tuchman, Y., James, C. D., Marinella, M. J., Yang, J., Salleo, A., Talin, A. 2019; 364 (6440): 570-+
  • A Universal Platform for Fabricating Organic Electrochemical Devices ADVANCED ELECTRONIC MATERIALS Duong, D. T., Tuchman, Y., Chakthranont, P., Cavassin, P., Colucci, R., Jaramillo, T. F., Salleo, A., Faria, G. C. 2018; 4 (7)
  • Network overload due to massive attacks PHYSICAL REVIEW E Kornbluth, Y., Barach, G., Tuchman, Y., Kadish, B., Cwilich, G., Buldyrev, S. 2018; 97 (5): 052309


    We study the cascading failure of networks due to overload, using the betweenness centrality of a node as the measure of its load following the Motter and Lai model. We study the fraction of survived nodes at the end of the cascade p_{f} as a function of the strength of the initial attack, measured by the fraction of nodes p that survive the initial attack for different values of tolerance α in random regular and Erdös-Renyi graphs. We find the existence of a first-order phase-transition line p_{t}(α) on a p-α plane, such that if pp_{t}, p_{f} is large and the giant component of the network is still present. Exactly at p_{t}, the function p_{f}(p) undergoes a first-order discontinuity. We find that the line p_{t}(α) ends at a critical point (p_{c},α_{c}), in which the cascading failures are replaced by a second-order percolation transition. We find analytically the average betweenness of nodes with different degrees before and after the initial attack, we investigate their roles in the cascading failures, and we find a lower bound for p_{t}(α). We also study the difference between localized and random attacks.

    View details for PubMedID 29906843