26 citations · 42 across the 5 of their papers we have counts for
5 papers
The brain as an efficient and robust adaptive learner
Sophie Denève, Alireza Alemi, Ralph Bourdoukan
Understanding how the brain learns to compute functions reliably, efficiently and robustly with noisy spiking activity is a fundamental challenge in neuroscience. Most sensory and…
Exponential Capacity in an Autoencoder Neural Network with a Hidden Layer
Alireza Alemi, Alia Abbara
A fundamental aspect of limitations in learning any computation in neural architectures is characterizing their optimal capacities. An important, widely-used neural architecture is…
Learning arbitrary dynamics in efficient, balanced spiking networks using local plasticity rules
Alireza Alemi, Christian Machens, Sophie Denève +1
Understanding how recurrent neural circuits can learn to implement dynamical systems is a fundamental challenge in neuroscience. The credit assignment problem, i.e. determining the…
An attractor neural network architecture with an ultra high information capacity: numerical results
Alireza Alemi
Attractor neural network is an important theoretical scenario for modeling memory function in the hippocampus and in the cortex. In these models, memories are stored in the plastic…
A three-threshold learning rule approaches the maximal capacity of recurrent neural networks
Alireza Alemi, Carlo Baldassi, Nicolas Brunel +1
Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for…