Brainlab: a Python toolkit to aid in the design, simulation, and analysis of spiking neural networks with the NeoCortical Simulator

Drewes, Richard P; Zou, Quan; Goodman, Philip H.

doi:10.3389/neuro.11.016.2009

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…The hippocampal model included a total of 37,500 leaky integrate-and-fire neurons with conductance-based synapses with a sampling frequency of 1,000/s. All simulations were performed using Neo Cortical Simulator, also known as NCS (Courtenay Wilson et al, 2001 ; Brette et al, 2007 ; Drewes et al, 2009 ) on a shared-memory 16-processor Sun Fire X4600. Each of five place-field subnetworks included 3,200 neurons, comprised of 2,600 pyramidal and 600 single-compartment interneurons.…”

Section: Methodsmentioning

confidence: 99%

A Circuit-Level Model of Hippocampal Place Field Dynamics Modulated by Entorhinal Grid and Suppression-Generating Cells

Bray¹,

Quoy²,

Harris³

et al. 2010

Front. Neural Circuits

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Hippocampal “place cells” and the precession of their extracellularly recorded spiking during traversal of a “place field” are well-established phenomena. More recent experiments describe associated entorhinal “grid cell” firing, but to date only conceptual models have been offered to explain the potential interactions among entorhinal cortex (EC) and hippocampus. To better understand not only spatial navigation, but mechanisms of episodic and semantic memory consolidation and reconsolidation, more detailed physiological models are needed to guide confirmatory experiments. Here, we report the results of a putative entorhinal-hippocampal circuit level model that incorporates recurrent asynchronous-irregular non-linear (RAIN) dynamics, in the context of recent in vivo findings showing specific intracellular–extracellular precession disparities and place field destabilization by entorhinal lesioning. In particular, during computer-simulated rodent maze navigation, our model demonstrate asymmetric ramp-like depolarization, increased theta power, and frequency (that can explain the phase precession disparity), and a role for STDP and KAHP channels. Additionally, we propose distinct roles for two entorhinal cell populations projecting to hippocampus. Grid cell populations transiently trigger place field activity, while tonic “suppression-generating cell” populations minimize aberrant place cell activation, and limit the number of active place cells during traversal of a given field. Applied to place-cell RAIN networks, this tonic suppression explains an otherwise seemingly discordant association with overall increased firing. The findings of this circuit level model suggest in vivo and in vitro experiments that could refute or support the proposed mechanisms of place cell dynamics and modulating influences of EC.

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Section: Methodsmentioning

confidence: 99%

A Circuit-Level Model of Hippocampal Place Field Dynamics Modulated by Entorhinal Grid and Suppression-Generating Cells

Bray¹,

Quoy²,

Harris³

et al. 2010

Front. Neural Circuits

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“…In most cases, the Python interface was added to an existing simulator written in a compiled language such as C++. This was the case for NEURON (Hines et al, 2009 ), NEST (Eppler et al, 2009 ), PCSIM (Pecevski et al, 2009 ), Nengo (Stewart et al, 2009 ), MOOSE (Ray and Bhalla, 2008 ), STEPS (Wils and De Schutter, 2009 ) and NCS (Drewes et al, 2009 ). However, as the articles by Goodman and Brette ( 2008 ) on the Brian simulator and Bednar ( 2009 ) on the Topographica simulator demonstrate, it is also possible to develop new simulation environments purely in Python, making use of the vectorization techniques available in the underlying NumPy package to obtain computational efficiency.…”

Section: Overview Of the Research Topicmentioning

confidence: 96%

Python in neuroscience

Müller

Bednar

Diesmann

et al. 2015

Front. Neuroinform.

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“…NCS (Drewes, 2005; Wilson et al, 2005; Brette et al, 2007; Drewes et al, 2009; Jayet Bray et al, 2010) is a neural simulator that can model integrate-and-fire neurons with conductance-based synapses. It uses two clusters: four SUN 4600 machines (16-processors each) connected via Infiniband with 192 GB RAM per machine, 24 Terabytes of disk storage; and 208 Opteron cores, 416 GB RAM, and more than a Terabyte of disk storage.…”

Section: Virtual Neurorobotics (Vnr)mentioning

confidence: 99%

Reward-based learning for virtual neurorobotics through emotional speech processing

Bray¹,

Ferneyhough²,

Barker³

et al. 2013

Front. Neurorobot.

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Reward-based learning can easily be applied to real life with a prevalence in children teaching methods. It also allows machines and software agents to automatically determine the ideal behavior from a simple reward feedback (e.g., encouragement) to maximize their performance. Advancements in affective computing, especially emotional speech processing (ESP) have allowed for more natural interaction between humans and robots. Our research focuses on integrating a novel ESP system in a relevant virtual neurorobotic (VNR) application. We created an emotional speech classifier that successfully distinguished happy and utterances. The accuracy of the system was 95.3 and 98.7% during the offline mode (using an emotional speech database) and the live mode (using live recordings), respectively. It was then integrated in a neurorobotic scenario, where a virtual neurorobot had to learn a simple exercise through reward-based learning. If the correct decision was made the robot received a spoken reward, which in turn stimulated synapses (in our simulated model) undergoing spike-timing dependent plasticity (STDP) and reinforced the corresponding neural pathways. Both our ESP and neurorobotic systems allowed our neurorobot to successfully and consistently learn the exercise. The integration of ESP in real-time computational neuroscience architecture is a first step toward the combination of human emotions and virtual neurorobotics.

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Brainlab: a Python toolkit to aid in the design, simulation, and analysis of spiking neural networks with the NeoCortical Simulator

Cited by 11 publications

References 8 publications

A Circuit-Level Model of Hippocampal Place Field Dynamics Modulated by Entorhinal Grid and Suppression-Generating Cells

A Circuit-Level Model of Hippocampal Place Field Dynamics Modulated by Entorhinal Grid and Suppression-Generating Cells

Python in neuroscience

Reward-based learning for virtual neurorobotics through emotional speech processing

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