The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …

Neuroscience has focused on the detailed implementation of computation, studying neural
codes, dynamics and circuits. In machine learning, however, artificial neural networks tend …

We describe a method to train spiking deep networks that can be run using leaky integrate-
and-fire (LIF) neurons, achieving state-of-the-art results for spiking LIF networks on five …

Understanding how biological neural networks carry out learning using spike-based local
plasticity mechanisms can lead to the development of real-time, energy-efficient, and …

To navigate in new environments, an animal must be able to keep track of its position while
simultaneously creating and updating an internal map of features in the environment, a …

The brain is not a glorified digital computer. It does not store information in registers, and it
does not mathematically transform mental representations to establish perception or …

In this paper, we review the theoretical and software tools used to construct Spaun, the first
(and so far only) brain model capable of performing cognitive tasks. This tool set allowed us …

In this paper, we present a functional spiking-neuron model of human working memory
(WM). This model combines neural firing for encoding of information with activity-silent …

Several recent studies have shown that neural activity in vivo tends to be constrained to a
low-dimensional manifold. Such activity does not arise in simulated neural networks with …

For energy-efficient computation in specialized neuromorphic hardware, we present spiking
neural coding, an instantiation of a family of artificial neural models grounded in the theory of …