Publikationen von E Giannakakis

Giannakakis, E.; Khajehabdollahi, S.; Levina, A.: Network bottlenecks and task structure control the evolution of interpretable learning rules in a foraging agent. Artificial Life Epub ahead (2024)

Giannakakis, E.; Vinogradov, O.; Buendía, V.; Levina, A.: Structural influences on synaptic plasticity: the role of presynaptic connectivity in the emergence of E/I co-tuning. PLOS Computational Biology 20 (10), e1012510 (2024)

Khajehabdollahi, S.; Prosi, J.; Giannakakis, E.; Martius, G.; Levina, A.: When to Be Critical? Performance and Evolvability in Different Regimes of Neural Ising Agents. Artificial Life 28 (4), S. 458 - 478 (2022)

Hamidi, M.; Khajehabdollahi, S.; Giannakakis, E.; Schäfer, T.; Levina, A.; Wu, C.: Modular Growth of Hierarchical Networks: Efficient, General, and Robust Curriculum Learning. In: ALIFE 2024: Proceedings of the 2024 Artificial Life Conference. 2024 Conference on Artificial Life, Copenhagen, Denmark, 22. Juli 2024 - 26. Juli 2024. (2024)

Giannakakis, E.; Khajehabdollahi, S.; Levina, A.: Environmental variability and network structure determine the optimal plasticity mechanisms in embodied agents. In: ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference, S. 157 - 166 (Hg. Iizuka, H.; Suzuki, K.; Uno, R.; Damiano, L.; Spychala, N. et al.). ALIFE 2023: Ghost in the Machine: The 2021 Conference on Artificial Life, Sapporo, Japan, 24. Juli 2023 - 28. Juli 2023. MIT Press (2023)

Khajehabdollahi, S.; Giannakakis, E.; Buendia, V.; Martius, G.; Levina, A.: Locally adaptive cellular automata for goal-oriented self-organization. In: ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference, S. 410 - 419 (Hg. Iizuka, H.; Suzuki, K.; Uno, R.; Damiano, L.; Spychala, N. et al.). ALIFE 2023: Ghost in the Machine: The 2021 Conference on Artificial Life, Sapporo, Japan, 24. Juli 2023 - 28. Juli 2023. MIT Press (2023)

Khajehabdollahi, S.; Giannakakis, E.; Prosi, J.; Levina, A.: Reservoir computing with self-organizing neural oscillators. In: Artificial Life Conference Proceedings, Bd. 2021, 78. ALIFE 2021: The 2021 Conference on Artificial Life, Praha, Czech Republic, 19. Juli 2021 - 23. Juli 2021. MIT Press (2021)

Prosi, J.; Khajehabdollahi, S.; Giannakakis, E.; Martius, G.; Levina, A.: The dynamical regime and its importance for evolvability, task performance and generalization. In: Artificial Life Conference Proceedings, Bd. 2021, 79. ALIFE 2021: The 2021 Conference on Artificial Life, Praha, Czech Republic, 19. Juli 2021 - 23. Juli 2021. MIT Press (2021)

Giannakakis, E.; Vinogradov, O.; Buendía, V.; Khajehabdollahi, S.; Levina, A.: Local excitation and lateral inhibition enable the simultaneous processing of multiple signals in recurrent neural networks. In Journal of Computational Neuroscience, 52 (Supplement 1), O5, S. S8 - S9. 32nd Annual Computational Neuroscience Meeting (CNS*2023), Leipzig, Germany, 15. Juli 2023 - 19. Juli 2023. Kluwer Academic Publishers, Boston (2024)

Zeraati, R.; Khajehabdollahi, S.; Giannakakis, E.; Schafer, T.; Martius, G.; Levina, A.: Long timescales needed for memory tasks arise from distinct mecha- nisms shaped by learning curricula. In Computational and Systems Neuroscience Meeting (COSYNE 2024), T-35, S. 47 - 48. Computational and Systems Neuroscience Meeting (COSYNE 2024), Lisboa, Portugal, 29. Februar 2024 - 05. März 2024. (2024)

Zeraati, R.; Khajehabdollahi, S.; Giannakakis, E.; Schäfer, T.; Martius, G.; Levina, A.: Role of single-neuron and network-mediated timescales in recurrent neural networks solving long-memory tasks. In Bernstein Conference 2023, CT 4. Bernstein Conference 2023, Berlin, Germany, 26. September 2023 - 29. September 2023. (2023)

Giannakakis, E.; Bird, A.; Jedlicka, P.; Cuntz, H.; Levina, A.: Dendritic nonlinearities and synapse type-specific input clustering enable the development of input selectivity in a wide range of settings. Bernstein Conference 2024, Frankfurt/Main, Germany (2024)

Schäfer, T.; Giannakakis, E.; Schmidt-Barbo, P.; Levina, A.; Vinogradov, O.: Unsupervised clustering of burst shapes reveals the increasing complexity of developing networks in vitro. Bernstein Conference 2024, Frankfurt/Main, Germany (2024)

Vinogradov, O.; Giannakakis, E.; Uysal, B.; Ron, S.; Weinreb, E.; Lerche, H.; Moses, E.; Levina, A.: Effective excitability: a determinant of the network bursting dynamics revealed by parameter invariance. Bernstein Conference 2024, Frankfurt/Main, Germany (2024)

Giannakakis, E.; Buendia, V.; Vinogradov, O.; Khajehabdollahi, S.; Levina, A.: Overlapping E/I neuronal assemblies generate rich network dynamics and enable complex computations. Research in Encoding and Decoding of Neural Ensembles (AREADNE 2024), Milos, Greece (2024)

Giannakakis, E.; Buendia, V.; Vinogradov, O.; Khajehabdollahi, S.; Levina, A.: Distinct excitatory and inhibitory connectivity structures control the dynamics and computational capabilities of recurrent networks. Computational and Systems Neuroscience Meeting (COSYNE 2024), Lisboa, Portugal (2024)

Giannakakis, E.; Buendía, V.; Vinogradov, O.; Khajehabdollahi, S.; Levina, A.: Inhomogeneous connectivity structures in E/I networks enable the processing of multiple chaotic time series. Bernstein Conference 2023, Berlin, Germany (2023)

Sagtekin, A.; Giannakakis, E.; Levina, A.: Emergent E/I anti-tuning and balance during surrogate gradient learning. Bernstein Conference 2023, Berlin, Germany (2023)

Vinogradov, O.; Giannakakis, E.; Buendia, V.; Uysal, B.; Weinreb, E.; Ron, S.; Lerche, H.; Moses, E.; Levina, A.: Network excitability determines collective bursting dynamics of neuronal networks in vitro. Bernstein Conference 2023, Berlin, Germany (2023)

Giannakakis, E.; Levina, A.; Buendia, V.; Khajehabdollahi, S.: Synaptic-type-specific clustering optimizes the computational capabilities of balanced recurrent networks. Computational and Systems Neuroscience Meeting (COSYNE 2023), Montreal, Quebec, Canada (2023)