Building functional networks of spiking model neurons

Abstract

Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.

Survey on Artificial Intelligence for Vehicles

“…Human-machine concept coupling artificial intelligence performs playing with young children in school [11]. Human-machine semi-physical coupling AI is a foreground in AIV.…”

Cortical Variability and Challenges for Modeling Approaches

“…It is well known that essentially deterministic networks of balanced excitation and inhibition are able to generate a weakly correlated, often chaotic attractor state which presents Poissonian statistical properties like the observed activity (van Vreeswijk and Sompolinsky, 1996; Amit and Brunel, 1997; Shadlen and Newsome, 1998; Sussillo and Abbott, 2009; Litwin-Kumar and Doiron, 2012). However, such a chaotic state is a non-mandatory modeling choice: recently, a range of models has shown that part of the observed variability may also be explained by a different class of deterministic processes (Beck et al, 2012; Mattia et al, 2013; Renart and Machens, 2014; Bujan et al, 2015; Abbott et al, 2016; Deneve and Machens, 2016; Doiron et al, 2016; Gillary and Niebur, 2016; Hartmann et al, 2016) such as the lack of specificity in top-down processing of cognitively complex tasks (Beck et al, 2012). …”

“…It has recently been proposed that a much tighter synchronization between excitation and inhibition than considered so far, at the spike level, has an even stronger experimental support and would enable the network to operate optimally by reducing the minimum coding error (Renart et al, 2010; Boerlin et al, 2013; Abbott et al, 2016; Deneve and Machens, 2016). The precise way in which the asynchronous state is achieved however, is not unique.…”

“…The precise way in which the asynchronous state is achieved however, is not unique. For example, connectivity weights are specifically learnt in the design termed spike-coding network (Boerlin et al, 2013; Schwemmer et al, 2015; Abbott et al, 2016; Deneve and Machens, 2016); whilst connectivity is clustered in Litwin-Kumar and Doiron (2012), shaped by plasticity in Vogels et al (2011) and Landau et al (2016) and much less structured in many other dense (Renart et al, 2010; Abbott et al, 2016) or sparse (Ostojic, 2014) networks.…”

Conceptual and technical advances define a key moment for theoretical neuroscience

“…Third, using modeling to link the results to underlying mechanisms and overlying principles. The perspectives and reviews in this issue primarily address the third, and some of the second, stage, surveying new developments and modeling-based insights in topics ranging from understanding and interpreting network spiking activity 1–3 , exploring visual processing 4 and memory 5 , and studying the representation and computation of probability 6 , to investigations of higher level cognition and mental illness 7 . In addition, major advances in the other stages of analysis have driven the entire program to evolve considerably in recent years.…”