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Spiking networks as efficient distributed controllers.

Biological cybernetics
In the brain, networks of neurons produce activity that is decoded into perceptions and actions. How the dynamics of neural networks support this decoding is a major scientific question. That is, while we understand the basic mechanisms by which neur...

A self-organizing short-term dynamical memory network.

Neural networks : the official journal of the International Neural Network Society
Working memory requires information about external stimuli to be represented in the brain even after those stimuli go away. This information is encoded in the activities of neurons, and neural activities change over timescales of tens of milliseconds...

Interhemispheric dominance switching in a neural network model for birdsong.

Journal of neurophysiology
Male zebra finches produce a sequence-invariant set of syllables, separated by short inspiratory gaps. These songs are learned from an adult tutor and maintained throughout life, making them a tractable model system for learned, sequentially ordered ...

Spiking Optical Flow for Event-Based Sensors Using IBM's TrueNorth Neurosynaptic System.

IEEE transactions on biomedical circuits and systems
This paper describes a fully spike-based neural network for optical flow estimation from dynamic vision sensor data. A low power embedded implementation of the method, which combines the asynchronous time-based image sensor with IBM's TrueNorth Neuro...

Discovering space - Grounding spatial topology and metric regularity in a naive agent's sensorimotor experience.

Neural networks : the official journal of the International Neural Network Society
In line with the sensorimotor contingency theory, we investigate the problem of the perception of space from a fundamental sensorimotor perspective. Despite its pervasive nature in our perception of the world, the origin of the concept of space remai...

Sum Rate of MISO Neuro-Spike Communication Channel With Constant Spiking Threshold.

IEEE transactions on nanobioscience
Communication among neurons, known as neuro-spike communication, is the most promising technique for realization of a bio-inspired nanoscale communication paradigm to achieve biocompatible nanonetworks. In neuro-spike communication, the information, ...

Neural network models of the tactile system develop first-order units with spatially complex receptive fields.

PloS one
First-order tactile neurons have spatially complex receptive fields. Here we use machine-learning tools to show that such complexity arises for a wide range of training sets and network architectures. Moreover, we demonstrate that this complexity ben...

A Dynamic Neural Gradient Model of Two-Item and Intermediate Transposition.

Neural computation
Transposition is a tendency for organisms to generalize relationships between stimuli in situations where training does not objectively reward relationships over absolute, static associations. Transposition has most commonly been explained as either ...

Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture.

Scientific reports
Familiarity of marketing stimuli may affect consumer behaviour at a peri-perceptual processing level. The current study introduces a method for deep learning of electroencephalogram (EEG) data using a spiking neural network (SNN) approach that reveal...

Quantum-like behavior without quantum physics II. A quantum-like model of neural network dynamics.

Journal of biological physics
In earlier work, we laid out the foundation for explaining the quantum-like behavior of neural systems in the basic kinematic case of clusters of neuron-like units. Here we extend this approach to networks and begin developing a dynamical theory for ...