From Biophysics to Behavior Catacomb2 and the Design of Biologically-Plausible Models for Spatial Navigation

Cannon, Robert C.; Hasselmo, Michael E.; Koene, Randal A.

doi:10.1385/ni:1:1:003

Cited by 38 publications

(15 citation statements)

References 30 publications

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“…We implemented models of PFC and EC neuronal networks and their functions in the CATACOMB2 (Hasselmo et al, 2002a;Cannon et al, 2003;Koene et al, 2003) simulation environment. The simulation used integrate-and-fire neurons for PFC and EC regions and spike timing dependent plasticity (STDP) at synapses in the PFC.…”

Section: Postoperative Testingmentioning

confidence: 99%

Cholinergic Deafferentation of the Entorhinal Cortex in Rats Impairs Encoding of Novel But Not Familiar Stimuli in a Delayed Nonmatch-to-Sample Task

McGaughy¹,

Koene²,

Eichenbaum³

et al. 2005

J. Neurosci.

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Acetylcholine may regulate working memory for novel stimuli by activating intrinsic mechanisms for sustained spiking in entorhinal cortical neurons, which have been demonstrated in slice preparations of the entorhinal cortex. Computational modeling demonstrates that loss of the cholinergic activation of intrinsic mechanisms for sustained activity could selectively impair working memory for novel stimuli, whereas working memory for familiar stimuli could be maintained because of previously modified synapses. Blockade of muscarinic cholinergic receptors and selective cholinergic lesions has been shown to impair encoding in delayed matching tasks. However, previous studies have not compared explicitly the role of cholinergic modulation in working memory for novel versus familiar stimuli. Here, we show that lesions of the cholinergic innervation of the entorhinal cortex selectively impair delayed nonmatch to sample performance for novel odors, whereas delayed nonmatch to sample for familiar odors is spared. This indicates an important role for cholinergic innervation of the entorhinal cortex in working memory for novel stimuli.

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Section: Postoperative Testingmentioning

confidence: 99%

Cholinergic Deafferentation of the Entorhinal Cortex in Rats Impairs Encoding of Novel But Not Familiar Stimuli in a Delayed Nonmatch-to-Sample Task

McGaughy¹,

Koene²,

Eichenbaum³

et al. 2005

J. Neurosci.

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“…It is hoped that careful development to strictly-defined interfaces allows such a system to work efficiently at higher inter-module communication requirements than would be the case for a looser assembly of components linked by an interpreted language like Python. This approach is exemplified by a number of object oriented simulation systems, including the Messaging Object Oriented Simulation Environment (MOOSE, http://sourceforge.net/projects/moose-g3) being developed as a successor to the Genesis framework, Catacomb2 (Cannon et al 2003), or the discontinued Neosim project (Goddard et al 2001; http://www.neosim.org).…”

Section: Technology For Run-time Interoperabilitymentioning

confidence: 99%

Interoperability of Neuroscience Modeling Software: Current Status and Future Directions

et al. 2007

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Neuroscience increasingly uses computational models to assist in the exploration and interpretation of complex phenomena. As a result, considerable effort is invested in the development of software tools and technologies for numerical simulations and for the creation and publication of models. The diversity of related tools leads to the duplication of effort and hinders model reuse. Development practices and technologies that support interoperability between software systems therefore play an important role in making the modeling process more efficient and in ensuring that published models can be reliably and easily reused. Various forms of interoperability are possible including the development of portable model description standards, the adoption of common simulation languages or the use of standardized middleware. Each of these approaches finds applications within the broad range of current modeling activity. However more effort is required in many areas to enable new scientific questions to be addressed. Here we present the conclusions of the "Neuro-IT Interoperability of Simulators" workshop, held at the 11th computational neuroscience meeting in e-mail: erik@tnb.ua.ac.be. 19-20 2006; http://www.cnsorg.org). We assess the current state of interoperability of neural simulation software and explore the future directions that will enable the field to advance. NIH Public Access

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“…Although KInNeSS does not strictly adhere to the NeuroML schema (however future development will focus on this issue), this is a key design feature of KInNeSS that will allow for easier testing and integration of model designs across various neural simulators. To the best of our knowledge, KInNeSS is one of the few software simulators, along with Catacomb2 (Cannon et al, 2002), when compared to the ones evaluated by Brette et al (2007), that allows a simulated agent to be controlled by a neural model (Gorchetchnikov and Hasselmo, 2002;. This is important for simulations that aim to link behavioral data with neural modeling.…”

Section: Tablementioning

confidence: 99%

“…These large-scale neural models are often non-linear dynamical systems which can be analytically intractable and require numerical simulation to gain insight into their behavior. Emergent properties of large-scale neural networks often remain unnoticed until the whole system is simulated and components are allowed to interact (Cannon et al, 2002).An additional level of complexity is finding a neural simulator and simulation environment that would enable the large variety of researchers from neurophysiology, psychology and computational modeling to share data and work collaboratively (an excellent review can be found in Brette et al, 2007). Most available software packages are specialized in different applications.…”

mentioning

confidence: 99%

KInNeSS: A Modular Framework for Computational Neuroscience

et al. 2008

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Making use of very detailed neurophysiological, anatomical, and behavioral data to build biologically-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalability, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multi-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions or ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further development of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effectively collaborate using a modern neural simulation platform. 3 IntroductionAdvances in functional, anatomical, and behavioral neuroscience techniques have led to an increase in the data available for modeling complex dynamics of biologically inspired neural networks at many levels of abstraction, from in-depth descriptions and analyses of individual membrane channels to large-scale investigations of whole brain activity. This wealth of data is essential for creating realistic neural models and increases our understanding of animal and human behavior. Furthermore, it has pushed the modeling community towards the design of increasingly complex models, incorporating unprecedented amount of biophysical and anatomical constraints. These large-scale neural models are often non-linear dynamical systems which can be analytically intractable and require numerical simulation to gain insight into their behavior. Emergent properties of large-scale neural networks often remain unnoticed until the whole system is simulated and components are allowed to interact (Cannon et al., 2002).An additional level of complexity is finding a neural simulator and simulation environment that would enable the large variety of researchers from neurophysiology, psychology and computational modeling to share data and work collaboratively (an excellent review can be found in Brette et al., 2007). Most available software packages are specialized in different applications.

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From Biophysics to Behavior Catacomb2 and the Design of Biologically-Plausible Models for Spatial Navigation

Cited by 38 publications

References 30 publications

Cholinergic Deafferentation of the Entorhinal Cortex in Rats Impairs Encoding of Novel But Not Familiar Stimuli in a Delayed Nonmatch-to-Sample Task

Cholinergic Deafferentation of the Entorhinal Cortex in Rats Impairs Encoding of Novel But Not Familiar Stimuli in a Delayed Nonmatch-to-Sample Task

Interoperability of Neuroscience Modeling Software: Current Status and Future Directions

KInNeSS: A Modular Framework for Computational Neuroscience

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