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Found 7 papers, 2 papers with code
Assessing the similarity of real matrices with arbitrary shape
We conclude that SAS is a suitable measure for quantifying the shared structure of matrices with arbitrary shape.
A method for the ethical analysis of brain-inspired AI
The conclusion resulting from the application of this method is that, compared to traditional AI, brain-inspired AI raises new foundational ethical issues and some new practical ethical issues, and exacerbates some of the issues raised by traditional AI.
Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density
This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience.
Connectivity Concepts in Neuronal Network Modeling
We hope that the proposed standardizations will contribute to unambiguous descriptions and reproducible implementations of neuronal network connectivity in computational neuroscience.
Cortical oscillations implement a backbone for sampling-based computation in spiking neural networks
Being permanently confronted with an uncertain world, brains have faced evolutionary pressure to represent this uncertainty in order to respond appropriately.
A Microscopic Theory of Intrinsic Timescales in Spiking Neural Networks
We develop a theory for self-consistent second-order single-neuron statistics in block-structured sparse random networks of spiking neurons.
Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space
Based on model predictions of spiking activity and LFPs, we find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike-train correlations in agreement with experimental data recorded in the sensory cortex.
