pynn

Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network

As it is confronted to inherent neural delays, how does the visual system create a coherent representation of a rapidly changing environment? In this paper, we investigate the role of motion-based prediction in estimating motion trajectories …

Anisotropic connectivity implements motion-based prediction in a spiking neural network

Predictive coding hypothesizes that the brain explicitly infers upcoming sensory input to establish a coherent representation of the world. Although it is becoming generally accepted, it is not clear on which level spiking neural networks may …

Functional consequences of correlated excitatory and inhibitory conductances in cortical networks

Neurons in the neocortex receive a large number of excitatory and inhibitory synaptic inputs. Excitation and inhibition dynamically balance each other, with inhibition lagging excitation by only few milliseconds. To characterize the functional …

NeuralEnsemble: Towards a meta-environment for network modeling and data analysis

NeuralEnsemble (http://neuralensemble.org) is a multilateral effort to coordinate and organise neuroscience software development efforts based around the Python programming language into a larger, meta-simulator software system. To this end, …

PyNN: A Common Interface for Neuronal Network Simulators

Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading …

PyNN: towards a universal neural simulator API in Python

Trends in programming language development and adoption point to Python as the high-level systems integration language of choice. Python leverages a vast developer-base external to the neuroscience community, and promises leaps in simulation …