CNS*2022 Melbourne: Program

17:15-18:15

• O1 Infomorphic Neurons: Locally learning pyramidal-inspired neurons derived from partial information decomposition (Makkeh et al.)
• O2 Balancing sequence robustness and interval variability in minimal CPG bursting models (Berbel et al. )
• O3 Temporal scaling of neural trajectories in a multiple-timescale network (Kurikawa et al. )
• O4 Deep Simplicial Manifold Learning for Neural Spike Train Decoding (Mitchell et al.)
• O5 Brain Wave Pattern Dynamics – Changes in Alzheimer’s Disease (Hoffman et al.)

• F1 Photoreceptor biophysics enables deep learning models to generalize across light levels (Idrees et al.)
• O6 A large-scale survey of spatial and motion selectivity in an entire column in mouse V1 (Abbasi-Asl)
• O7 A new formalism relating kinematic intention readout to action processing (Pullar et al.)
• O8 Context-dependent hubs in multisensory perception revealed by computational modeling of large-scale cortical networks (Nunes et al.)

• F2 Regional and circuit heterogeneity of brain abnormalities in psychiatric disorders (Segal et al.)
• O9 The topochronic map of the human brain dynamics (Sorrentino et al.)
• O10 Spectral graph modeling of Alzheimer’s disease neurophysiology (Verma et al.)
• O11 Perturbation-based approaches derived and tested in mathematical neural models generate biomarkers for seizure transitions in animal models (Qin et al.)

• F3 Activity-dependent infrared laser stimulation to assess its biophysical effects on single neurons (Garrido-Pena)
• O14 Consequences of Dale’s law on the stability-complexity relationship of partially random neural networks (Peterson et al.)
• O12 Linking connectivity to dynamics: How do coherent oscillations emerge in a partially random neural network? (Harris et al.)
• O13 Inhibitory stabilization in a cortical neural mass model (Zarei Eskikand et al.)
• O15 Evolutionary shaping of human brain dynamics (Pang et al.)

• F4 Evidence of Criticality in Brain Neuronal Networks (Habibollahi Saatlou et al.)
• O16 Exploiting brain critical dynamics to inform Brain-Computer Interfaces performance (Corsi et al.)
• O17 Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons (Haider et al.)
• O18 Reward Bases: Instant reward revaluation with temporal difference learning (Bogacz et al.)

13:30-17:00

Workshops

• Emergent perspectives and models for neuron-glia interactions (Maurizio de Pitta)
• Methods on information theory in computational neuroscience (Joseph Lizier)
• Highly comparative analysis of neural dynamics (Ben D Fulcher)
• White matter, axons, and the role of delays – modeling axonal transmission (Thomas R. Knösche)

09:00-18:00

• Emergent perspectives and models for neuron-glia interactions (Maurizio de Pitta)
• Methods on information theory in computational neuroscience (Joseph Lizier)
• Bio-inspired active AI (Thomas Nowotny)
• Graph modeling of macroscopic brain functional activity dynamics (Parul Verma)