A Sparse Reformulation of the Green’s Function Formalism Allows Efficient Simulations of Morphological Neuron Models

Wybo, Willem A.M.; Boccalini, Daniele; Torben‐Nielsen, Benjamin; Gewaltig, Marc‐Oliver

doi:10.1162/neco_a_00788

Cited by 14 publications

(13 citation statements)

References 33 publications

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“…This is not achievable with a classical point neuron model, although we do not exclude the possibility of a formulation of a point neuron that generates an effective electrotonic distance. Whereas recent works such as Memmesheimer and Timme ( 2012 ) and Wybo et al ( 2015 ) demonstrate that point neuron models can successfully model the somatic effect of non-linear integration of dendritic inputs, our work shows that at the network level, the electrotronic distance in a compartmental model can result in very different network dynamics.…”

Section: Discussioncontrasting

confidence: 71%

Effects of Calcium Spikes in the Layer 5 Pyramidal Neuron on Coincidence Detection and Activity Propagation

Chua

Morrison

2016

Front. Comput. Neurosci.

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The role of dendritic spiking mechanisms in neural processing is so far poorly understood. To investigate the role of calcium spikes in the functional properties of the single neuron and recurrent networks, we investigated a three compartment neuron model of the layer 5 pyramidal neuron with calcium dynamics in the distal compartment. By performing single neuron simulations with noisy synaptic input and occasional large coincident input at either just the distal compartment or at both somatic and distal compartments, we show that the presence of calcium spikes confers a substantial advantage for coincidence detection in the former case and a lesser advantage in the latter. We further show that the experimentally observed critical frequency phenomenon, in which action potentials triggered by stimuli near the soma above a certain frequency trigger a calcium spike at distal dendrites, leading to further somatic depolarization, is not exhibited by a neuron receiving realistically noisy synaptic input, and so is unlikely to be a necessary component of coincidence detection. We next investigate the effect of calcium spikes in propagation of spiking activities in a feed-forward network (FFN) embedded in a balanced recurrent network. The excitatory neurons in the network are again connected to either just the distal, or both somatic and distal compartments. With purely distal connectivity, activity propagation is stable and distinguishable for a large range of recurrent synaptic strengths if the feed-forward connections are sufficiently strong, but propagation does not occur in the absence of calcium spikes. When connections are made to both the somatic and the distal compartments, activity propagation is achieved for neurons with active calcium dynamics at a much smaller number of neurons per pool, compared to a network of passive neurons, but quickly becomes unstable as the strength of recurrent synapses increases. Activity propagation at higher scaling factors can be stabilized by increasing network inhibition or introducing short term depression in the excitatory synapses, but the signal to noise ratio remains low. Our results demonstrate that the interaction of synchrony with dendritic spiking mechanisms can have profound consequences for the dynamics on the single neuron and network level.

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Section: Discussioncontrasting

confidence: 71%

Effects of Calcium Spikes in the Layer 5 Pyramidal Neuron on Coincidence Detection and Activity Propagation

Chua

Morrison

2016

Front. Comput. Neurosci.

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“…Furthermore, due to dendritic filtering, the somatic postsynaptic potentials in the multicompartment model neurons are not identical to those of their point-neuron counterparts. This inconsistency could, at least partially, be resolved by adjusting the amplitudes and temporal shapes of the synaptic currents in either the multicompartment neurons or the point neurons ( Koch and Poggio 1985 ; Wybo et al 2013 ; 2015 ).…”

Section: Discussionmentioning

confidence: 99%

“…Synapses should be positioned on the dendritic tree according to anatomical data, and synaptic weights and time constants should be adapted such that the somatic membrane potential or somatic current match the point-neuron counterparts. Such mapping between point neurons and passive multicompartment neurons is feasible ( Koch and Poggio 1985 ; Wybo et al 2013 ; 2015 ).…”

Section: Methods and Materials: Hybrid Lfp Modeling Schemementioning

confidence: 99%

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks

et al. 2016

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With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.

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“…Today this difficulty has been obviated to a great extent thanks to online databases such as NeuroMorpho (Ascoli et al 2007;Halavi et al 2008) which freely provide simulator-ready neuronal morphologies. That said, morphological reduction is still investigated as a tool for enhancing computational efficacy (Destexhe 2001;Wybo et al 2015). It is important to note that morphology is not a constant thing but rather highly variable even between neurons of the same type (Cuntz et al 2007(Cuntz et al , 2010(Cuntz et al , 2011Hay et al 2011; Torben-Nielsen and De Schutter 2014).…”

Section: Methods In Compartmental Modelingmentioning

confidence: 99%

“…The biophysics of dendrites too can nonlinearly modulate integration of synaptic potentials on one hand (Cash and Yuste 1998Yuste , 1999Yuste 2011) and on another support local electrogenesis of dendritic spikes, leading to a nonlinear integration of synaptic potentials (Gasparini et al 2004;Gulledge et al 2005;Hausser et al 2000;Losonczy and Magee 2006;Makara and Magee 2013;Mel 1993;Nettleton and Spain 2000;Polsky et al 2004) and allowing the neuron to respond over a longer timescale. The nonlinear integration of synaptic inputs occurs either in response to functional or anatomical synapse clusters (Druckmann et al 2014;Losonczy and Magee 2006;Makino and Malinow 2011;McBride et al 2008;Poirazi et al 2003b;Polsky et al 2004;Yadav et al 2012). In the case of functional clustering, synchronous stimulation of nearby synapses within the same branch results in a supralinear summation, while stimulation of the same number of synapses located between branches sum in a linear way Cash and Yuste 1998;Hausser and Mel 2003;Larkum et al 2009;Losonczy and Magee 2006;Mel 1993;Oviedo and Reyes 2012;Poirazi et al 2003a;Polsky et al 2004), suggesting that a single dendritic branch acts as a computational compartment of the neuron.…”

Section: Complex Single-neuron Computationmentioning

confidence: 99%

Is realistic neuronal modeling realistic?

Almog

Korngreen

2016

Journal of Neurophysiology

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Scientific models are abstractions that aim to explain natural phenomena. A successful model shows how a complex phenomenon arises from relatively simple principles while preserving major physical or biological rules and predicting novel experiments. A model should not be a facsimile of reality; it is an aid for understanding it. Contrary to this basic premise, with the 21st century has come a surge in computational efforts to model biological processes in great detail. Here we discuss the oxymoronic, realistic modeling of single neurons. This rapidly advancing field is driven by the discovery that some neurons don't merely sum their inputs and fire if the sum exceeds some threshold. Thus researchers have asked what are the computational abilities of single neurons and attempted to give answers using realistic models. We briefly review the state of the art of compartmental modeling highlighting recent progress and intrinsic flaws. We then attempt to address two fundamental questions. Practically, can we realistically model single neurons? Philosophically, should we realistically model single neurons? We use layer 5 neocortical pyramidal neurons as a test case to examine these issues. We subject three publically available models of layer 5 pyramidal neurons to three simple computational challenges. Based on their performance and a partial survey of published models, we conclude that current compartmental models are ad hoc, unrealistic models functioning poorly once they are stretched beyond the specific problems for which they were designed. We then attempt to plot possible paths for generating realistic single neuron models.

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A Sparse Reformulation of the Green’s Function Formalism Allows Efficient Simulations of Morphological Neuron Models

Cited by 14 publications

References 33 publications

Effects of Calcium Spikes in the Layer 5 Pyramidal Neuron on Coincidence Detection and Activity Propagation

Effects of Calcium Spikes in the Layer 5 Pyramidal Neuron on Coincidence Detection and Activity Propagation

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks

Is realistic neuronal modeling realistic?

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