There is consensus in the current literature that stable states of asynchronous irregular spiking activity require (i) large networks of 10 000 or more neurons and (ii) external background activity or pacemaker neurons. Yet already in 1963, Griffith
showed that networks of simple threshold elements can be persistently active at intermediate rates. Here, we extend Griffiths work and demonstrate that sparse networks of integrate-and-fire neurons assume stable states of self-sustained asynchronous and irregular firing without external input or pacemaker neurons. These states can be robustly induced by a brief pulse to a small fraction of the neurons, or by short a period of irregular input, and last for several minutes. Self-sustained activity states emerge when a small fraction of the synapses is strong enough to significantly influence the firing probability of a neuron, consistent with the recently proposed long-tailed distribution of synaptic weights. During self-sustained activity, each neuron exhibits highly irregular firing patterns, similar to experimentally observed activity. Moreover, the interspike interval distribution reveals that neurons switch between discrete states of high and low firing rates. We find that self-sustained activity states can exist even in small networks of only a thousand neurons. We investigated networks up to 100 000 neurons. Finally, we discuss the implications of self-sustained activity for learning, memory and signal propagation.
Almost all research work in computational neuroscience involves software. As researchers try to understand ever more complex systems, there is a continual need for software with new capabilities. Because of the wide range of questions being investiga
ted, new software is often developed rapidly by individuals or small groups. In these cases, it can be hard to demonstrate that the software gives the right results. Software developers are often open about the code they produce and willing to share it, but there is little appreciation among potential users of the great diversity of software development practices and end results, and how this affects the suitability of software tools for use in research projects. To help clarify these issues, we have reviewed a range of software tools and asked how the culture and practice of software development affects their validity and trustworthiness. We identified four key questions that can be used to categorize software projects and correlate them with the type of product that results. The first question addresses what is being produced. The other three concern why, how, and by whom the work is done. The answers to these questions show strong correlations with the nature of the software being produced, and its suitability for particular purposes. Based on our findings, we suggest ways in which current software development practice in computational neuroscience can be improved and propose checklists to help developers, reviewers and scientists to assess the quality whether particular pieces of software are ready for use in research.
