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Found 5 papers, 3 papers with code
Spike-based computation using classical recurrent neural networks
We show that this new network can achieve performance comparable to other types of spiking networks in the MNIST benchmark and its variants, the Fashion-MNIST and the Neuromorphic-MNIST.
Warming up recurrent neural networks to maximise reachable multistability greatly improves learning
This insight leads to the design of a novel way to initialise any recurrent cell connectivity through a procedure called "warmup" to improve its capability to learn arbitrarily long time dependencies.
Generalisation of neuronal excitability allows for the identification of an excitability change parameter that links to an experimentally measurable value
We show that in the singular limit of the mFHN model, the time-scale separation can be chosen such that there is a configuration of a classical phase portrait that allows for SNIC bifurcation, zero-frequency onset and a depolarising current, such as observed in Type I excitability.
A bio-inspired bistable recurrent cell allows for long-lasting memory
Standard gated cells share a layer internal state to store information at the network level, and long term memory is shaped by network-wide recurrent connection weights.
Introducing Neuromodulation in Deep Neural Networks to Learn Adaptive Behaviours
Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack.
