Recurrent spiking neural networks (RSNN) in the brain learn to perform a wide range of perceptual, cognitive and motor tasks very efficiently in terms of time and energy consumption.  This is due to the optimality of coding and learning schemes, which have yet to be unveiled. The formulation of biologically inspired networks capable to perform complex computations can mediate a synergetic interaction between Machine Learning and Neuroscience bringing to mutual benefits and helping to improve our understanding of biological and artificial intelligence. Even though several models have been proposed , it remains a challenging task to design RSNNs which use biologically plausible mechanisms . We propose a general probabilistic framework which relies on the principle of maximizing the likelihood for the network to solve the task . This principle permits to analytically work out an explicit and completely local plasticity rulesupporting the efficient solution of several tasks. We show that the learning algorithm can be achieved in a very few iterations, and that the online approximation of the likelihood maximization is extremely beneficial to fast learning . Our model is very general and it can be applied to a wide variety of network architectures and types of biological neurons. The derived plasticity learning rule is specific to each neuron model producing a theoretical prediction which can be verified experimentally .
Recurrent spiking neural networks (RSNN) in the brain learn to perform a wide range of perceptual, cognitive and motor tasks very efficiently in terms of time and energy consumption. Moreover, learning can happen rapidly after very few examples. This is due to the optimality of coding and learning schemes, which have yet to be clearly understood. This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones. Several spiking network models have been proposed but it remains a challenge to design RSNNs that use biologically plausible mechanisms capable of solving complex temporal tasks. We use a general probabilistic framework that relies on the principle of maximizing the likelihood for the network to reproduce some temporal dynamics . This principle permits to analytically work out an explicit and biologically plausible plasticity rule. Here we propose a novel target-based learning scheme in which such a rule can be used to efficiently train a RSNN to solve several temporal tasks such as learning multidimensional trajectory and an implementation of the temporal XOR. We finally show that an online approximation of the gradient ascent, in addition to guaranteeing complete locality in time and space, allows learning after very few presentations of the target output . Our model is general and it can be applied to a wide variety of network architectures and types of biological neurons. The derived plasticity learning rule is specific to each neuron model and can produce a theoretical prediction to be experimentally verified .
A
Moreover, learning can happen rapidly after very few examples.
R
unveiled. The
clearly understood. This naturally challenges the
R
networks capable to perform complex computations can mediate a synergetic interaction between Machine Learning and Neuroscience bringing to mutual benefits and helping to
RSNNs in order to
R
and artificial intelligence. Even though several
intelligence and the efficiency of artificial ones. Several spiking network
R
,
but
R
challenging task
challenge
R
which
that
R
. We propose
capable of solving complex temporal tasks. We use
R
which
that
R
solve the task
reproduce some temporal dynamics
R
completely local plasticity rulesupporting the efficient solution of several tasks. We show that the learning algorithm can be achieved in a very few iterations, and that the
biologically plausible plasticity rule. Here we propose a novel target-based learning scheme in which such a rule can be used to efficiently train a RSNN to solve several temporal tasks such as learning multidimensional trajectory and an implementation of the temporal XOR. We finally show that an
R
likelihood maximization is extremely beneficial to fast learning
gradient ascent, in addition to guaranteeing complete locality in time and space, allows learning after very few presentations of the target output
D
very
R
producing
and can produce
R
which can be verified experimentally
to be experimentally verified