Brainlab: A Python Toolkit to Aid in the Design, Simulation, and Analysis of Spiking Neural Networks with the NeoCortical Simulator

Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading “glue” tool for managing all sorts of complex programmatic tasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS (NeoCortical Simulator) environment in particular. Brainlab is an integrated model-building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

An Evolutionary Autonomous Agent with Visual Cortex and Recurrent Spiking Columnar Neural Network

Abstract. Spiking neural networks are computationally more power-ful than conventional artificial neural networks [1]. Although this fact should make them especially desirable for use in evolutionary autono-mous agent research, several factors have limited their application. This work demonstrates an evolutionary agent with a sizeable recurrent spi-king neural network containing a biologically motivated columnar visual cortex. This model is instantiated in spiking neural network simulation software and challenged with a dynamic image recognition and memory task. We use a genetic algorithm to evolve generations of this brain mo-del that instinctively perform progressively better on the task. This early work builds a foundation for determining which features of biological neural networks are important for evolving capable dynamic cognitive agents.

Exploring the Restorative Effects of Nature: Testing A Proposed Visuospatial Theory

In this thesis, the restorative effects of exposure to nature are examined through the lens of existing restoration theories. Limitations of existing theories, such as Attention Restoration Theory and Psycho-evolutionary Restoration Theory, are highlighted. To address the limitations of existing theories, an expanded theoretical framework is proposed: The expanded framework introduces a newly proposed neural mechanism and theory of restoration that build on existing theories by proposing a link to recently discovered reward systems in the ventral visual pathway. Results from six experiments provide consistent evidence to suggest that positive and negative responses to visual scenes are related to the low-level visuospatial properties of the scenes. Specifically, a discovery is made to suggest that the power of a limited visual spatial frequency range can consistently predict responses to natural, urban, and abstract scenes on measures of restoration (blink-rates, number of fixations, self-reported stress and pleasantness). This provides the first evidence to suggest that low-level visual properties of scenes may play an important role in affective and physiological responses to scenes. Furthermore, this newly discovered relationship provides a new way to objectively predict the relative restorative value of any given scene.1 yea