An Efficient Simulation Environment for Modeling Large-Scale Cortical Processing

Richert, Micah; Nageswaran, Jayram Moorkanikara; Dutt, Nikil; Krichmar, Jeffrey L.

doi:10.3389/fninf.2011.00019

Cited by 59 publications

(62 citation statements)

References 44 publications

(77 reference statements)

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“…To develop our model, we used a publicly available simulator, which has been shown to simulate large-scale spiking neural networks efficiently and flexibly [21]. The model contained a subcortical area composed of an input, TRN, LGN, and NB and a four-layered cortical microcircuit ( Figure 1).…”

Section: B Network Modelmentioning

confidence: 99%

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Spiking neuron model of basal forebrain enhancement of visual attention

Avery¹,

Krichmar²,

Dutt³

2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Attentional mechanisms allow the brain to enhance the representation and transmission of certain signals at the expense of others. The basal forebrain has been shown to play an important role in attention through its diverse set of interactions with sensory and associational areas. A recent empirical study indicates that the nucleus basalis, a subset of neurons located in the basal forebrain, is important for improving sensory processing by increasing reliability and decreasing redundancy in the cortex and thalamus [1, 2]. We developed a spiking neural network model that simulates the nucleus basalis' interaction with the thalamus and visual cortex. In this model, we simulated two modes of action by which it is thought that the nucleus basalis may be influencing sensory processing: (1) inhibitory projections from the nucleus basalis to the thalamic reticular nucleus, which disinhibit the lateral geniculate nucleus (LGN) and gate information into the cortex, and (2) cholinergic excitation of inhibitory neurons in the visual cortex. We showed that the inhibition of the thalamic reticular nucleus GABAergic neurons leads to an increase in the reliability of spikes in the LGN and cortex. We observed that a decrease in the burst to tonic firing ratio in the LGN, coupled with the cholinergic system increasing inhibition in the visual cortex caused decorrelation in the cortex. These findings will help us better understand the mechanisms behind the control of attention by the basal forebrain and shed light on how the orchestrated action of the basal forebrain on multiple target areas can improve information processing in the brain.

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Section: B Network Modelmentioning

confidence: 99%

“…The synaptic input, I, driving each neuron was dictated by simulated AMPA, NMDA, GABA A and GABA B conductances [21,24]. The total synaptic input seen by each neuron was given by:…”

Section: B Network Modelmentioning

confidence: 99%

Spiking neuron model of basal forebrain enhancement of visual attention

Avery¹,

Krichmar²,

Dutt³

2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

Self Cite

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“…Due to their superior neurophysiological realism KIe,i sets and Izhikevich columns are more useful to inquire about the effectiveness of certain connectivity metrics for data recorded in specific brain locations with known dynamics (Friston et al, 2014). For example, in (Freeman, 1987) coupled KI sets are used to simulate chaotic EEG emanating from the olfactory system and in (Richert et al, 2011) Izhikevich columns are used to simulate a large-scale model of cortical areas V1, V4, and middle temporal (MT) with color, orientation and motion selectivity. Forward models lead to a decrease in the absolute value of GGC, specially the forward BOLD model where negative DOIs could be found in worst scenarios.…”

Section: Discussionmentioning

confidence: 99%

Synthetic neuronal datasets for benchmarking directed connectivity metrics

Rodrigues

Andrade

2014

Preprint

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Background: Datasets consisting of synthetic neural data generated with quantifiable and controlled parameters are a valuable asset in the process of testing and validating directed connectivity metrics. Considering the recent debate in the neuroimaging community concerning the use of directed functional connectivity metrics for fMRI data, synthetic datasets that emulate BOLD dynamics have played a central role by supporting claims that argue in favor, or against, certain metrics. Generative models often used in studies that simulate neuronal activity, with the aim of gaining insight into specific brain regions and functions, have different requirements from the generative models for benchmarking datasets. Even though the latter must be realistic, there is a tradeoff between realism and computational demand that needs to be contemplated and simulations that efficiently mimic the real behavior of single neurons or neuronal populations are preferred, instead of more cumbersome and marginally precise ones. Methods: this work explores how simple generative models are able to produce neuronal datasets, for benchmarking purposes, that reflect the simulated effective connectivity and, how these can be used to obtain synthetic recordings of EEG and fMRI BOLD. The generative models covered here are AR processes, neural mass models consisting of linear and non-linear stochastic differential equations and populations with thousands of spiking units. Forward models for EEG consist in the simple three-shell head model while fMRI BOLD is modeled with the Balloon-Windkessel model or by convolution with a hemodynamic response function. Results: the simulated datasets are tested for causality with the original spectral formulation for Granger causality. Modeled effective connectivity can be detected in the generated data for varying connection strengths and interaction delays. Discussion: all generative models produce synthetic neuronal data with detectable causal effects although the relation between modeled and detected causality varies and less biophysically realistic models offer more control in causal relations such as modeled strength and frequency location.

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“…In recent years, the power of these workstations has increased drastically with the advent of powerful GPUs with general purpose programming interfaces for scientific computing tasks, bringing huge momentum to the field of deep learning in artificial neural networks (Raina, Madhavan, & Ng, 2009). In the field of spiking neural networks, GPU acceleration is implemented by the simulator CARLsim (Richert, Nageswaran, Dutt, & Krichmar, 2011) using NVIDIA CUDA (NVIDIA Corporation, 2015). In a study by Richert et al (2011), CARLsim was able to simulate a cortical network with 138 K neurons and 30 M synapses slightly faster than real-time.…”

Section: Neuromorphic Chips For Robotic Applicationsmentioning

confidence: 99%

“…In the field of spiking neural networks, GPU acceleration is implemented by the simulator CARLsim (Richert, Nageswaran, Dutt, & Krichmar, 2011) using NVIDIA CUDA (NVIDIA Corporation, 2015). In a study by Richert et al (2011), CARLsim was able to simulate a cortical network with 138 K neurons and 30 M synapses slightly faster than real-time. These results render GPUs a strong competitor for neuromorphic hardware designs.…”

Section: Neuromorphic Chips For Robotic Applicationsmentioning

confidence: 99%

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks

2015

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a b s t r a c tThe application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks.

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An Efficient Simulation Environment for Modeling Large-Scale Cortical Processing

Cited by 59 publications

References 44 publications

Spiking neuron model of basal forebrain enhancement of visual attention

Spiking neuron model of basal forebrain enhancement of visual attention

Synthetic neuronal datasets for benchmarking directed connectivity metrics

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks

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