PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python

Abstract

The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations.

The Virtual Brain: a simulator of primate brain network dynamics

“…It also provides the community with a technology, that until now had not been publicly available, accessible by researchers with different levels and backgrounds, enabling systematic implementation and comparison of neural mass and neural field models, incorporating biologically realistic connectivity and cortical geometry and with the potential to become a novel tool for clinical interventions. While many other environments simulate neural activity at the level of neurons (Brian simulator, MOOSE, PCSIM, NEURON, NEST, GENESIS) (Hines and Carnevale, 2001; Gewaltig and Diesmann, 2007; Goodman and Brette, 2008; Ray and Bhalla, 2008; Pecevski et al, 2009; Brette and Goodman, 2011), even mimicking a number of specific brain functions (Eliasmith et al, 2012), they, most importantly, do not consider the space-time structure of full brain connectivity constraining whole brain neurodynamics, as a crucial component in their modeling paradigm. Other approaches to multi-modal integration such as Statistical Parametric Mapping (SPM) perform statistical fitting to experimental data at the level of a small set of nodes (Friston et al, 1995, 2003; David et al, 2006; Pinotsis and Friston, 2011) [i.e., they are data-driven as in Freestone et al (2011)], thus diverging from our approach that could be categorized as a purely “computational neural modeling” paradigm as described in Bojak et al (2011).…”

Establishing a Novel Modeling Tool: A Python-based Interface for a Neuromorphic Hardware System

“…For example, NEURON (Hines and Carnevale, 2006; Hines et al, 2009) provides an interpreter called Hoc, NEST (Diesmann and Gewaltig, 2002; Eppler et al, 2008; Gewaltig and Diesmann, 2007) comes with a stack-based interface called SLI, and GENESIS (Bower and Beeman, 1998) has a different custom script language interpreter also called SLI. Both NEURON and NEST also provide Python (Rossum, 2000) interfaces, as do the PCSIM (PCSIM, 2009; Pecevski et al, 2009), Brian (Goodman and Brette, 2008) and MOOSE (Ray and Bhalla, 2008) simulators. Facilitating the usage of neuromorphic hardware for modelers means providing them with an interface similar to these existing ones.…”

Systematic configuration and automatic tuning of neuromorphic systems

“…Experiments (equivalent to software simulation runs) can be defined, set-up, and carried out, using methods and commands analogous to those present in software modern neural simulators such as Brian [5] or PCSIM [6].…”