A Novel Form of Local Plasticity in Tuft Dendrites of Neocortical Somatosensory Layer 5 Pyramidal Neurons

Sandler, Maya; Shulman, Yoav; Schiller, Jackie

doi:10.1016/j.neuron.2016.04.032

Cited by 43 publications

(41 citation statements)

References 75 publications

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“…In this study, we did not consider various other plasticity mechanisms: intrinsic and homeostatic plasticity [Triesch, 2007, Turrigiano, 2011, Turrigiano, 2012, Yger and Gilson, 2015, structural plasticity [Caroni et al, 2012], heterosynaptic plasticity [Chistiakova et al, 2014, Chistiakova et al, 2015 and branch strength plasticity [Losonczy et al, 2008, Makara et al, 2009, Legenstein and Maass, 2011. Moreover, we only considered basal dendrites, and different learning rules might be present in apical tuft dendrites [Kim et al, 2015, Sandler et al, 2016, especially in cortical layer 5 pyramidal neurons which are known to possess a powerful calcium spike initiation zone in this region [Schiller et al, 1997]. Understanding how this spike interacts with plasticity could provide further insight into the interaction of bottom-up inputs arriving on the basal dendrites and top-down feedback arriving at the tuft.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, an increasing number of experimental studies have revealed plasticity mechanisms which do not rely on postsynaptic action potential generation, but instead on local postsynaptic dendritic spikes [Golding et al, 2002, Gordon et al, 2006, Gambino et al, 2014, Brandalise and Gerber, 2014, Kim et al, 2015, Cichon and Gan, 2015 or subthreshold events for dendritic spikes [Weber et al, 2016, Sandler et al, 2016.…”

Section: Introductionmentioning

confidence: 99%

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Beyond spike-timing-dependent plasticity: a computational study of plasticity gradients across basal dendrites

Bono

Clopath

2016

Preprint

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Synaptic plasticity is thought to be the principal mechanism underlying learning in the brain. Models of plastic networks typically combine point neurons with spike-timing-dependent plasticity (STDP) as the learning rule. However, a point neuron does not capture the complexity of dendrites, which allow non-linear local processing of the synaptic inputs. Furthermore, experimental evidence suggests that STDP is not the only learning rule available to neurons. Implementing biophysically realistic neuron models, we studied how dendrites allow for multiple synaptic plasticity mechanisms to coexist in a single cell. In these models, we compared the conditions for STDP and for the synaptic strengthening by local dendritic spikes. We further explored how the connectivity between two cells is affected by these plasticity rules and the synaptic distributions. Finally, we show how memory retention in associative learning can be prolonged in networks of neurons with dendrites.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Beyond spike-timing-dependent plasticity: a computational study of plasticity gradients across basal dendrites

Bono

Clopath

2016

Preprint

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“…NMDA spikes are known to be involved in facilitating long term potentiation (LTP) and depression (LTD) (Gambino et al 2014;Sandler et al 2016;Gordon et al 2006;Brandalise et al 2016;Golding et al 2002). We showed in Figure 6 that precisely timed inhibition could gradually reduce the NMDA-dependent current influx by up to 60% of its original charge simultaneously in a group of spines cooperatively involved in generating the NMDA spike.…”

Section: Dendritic Inhibition Controlling Nmda-induced Plasticity In mentioning

confidence: 98%

“…It has been shown that dendritic Na + -, Ca 2+ -and NMDA-spikes can implement a variety of computational functions, including input pattern classifications (Mel 1992), coincidence detection (Larkum & Nevian 2008;Schiller & Schiller 2001), and directional selectivity (Smith et al 2013;. Importantly, the Ca 2+ influx associated with dendritic spikes plays a key role in modulating the plasticity of dendritic synapses (Gordon et al 2006;Sandler et al 2016;Gambino et al 2014;Villa et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Shaping dendritic NMDA spikes by timed synaptic inhibition: Implications for the I/O properties of cortical neurons

Doron

Chindemi

Müller

et al. 2017

Preprint

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The pronounced, long lasting, regenerative NMDA-spike is initiated in individual dendritic branches of different types of neurons and is known to play a key role in dendritic computations and plasticity. Combining dynamic system theory and computational approaches, we systematically analyzed how timed synaptic inhibition activated during the NMDA-spike timecourse, sculpts this spike and its associated current influx. When impinging on its early phase, individual GABAergic synapse activation transiently, but strongly, dampened the NMDA-spike; later inhibition prematurely terminated it. This inhibition reduced the NMDA-mediated Ca 2+ current by up to 60%. NMDA-spikes in distal dendritic branches/spines are longer lasting and more resilient to inhibition, and thus enhance synaptic plasticity at these branches. Examination of this sensitivity of the NMDA-spike to well-timed synaptic inhibition suggests that NMDA-spikes are highly modifiable signals which enable sparse weak distal dendritic inhibition to finely tune both the neuron's output spikes as well as the branch's/spine's Ca 2+ current associated with the local NMDA spike.not peer-reviewed)

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“…Very little connectivity data is currently available at this sub-dendrite scale, though several observations, taken together, suggest that withindendrite biases of this kind are biologically feasible: (1) the axonal projections of inhibitory neurons show famously strong spatial biases at the sub-dendrite scale (Bloss et al, 2016;DeFelipe, Ballesteros-Yáñez, Inda, & Muñoz, 2006;Karube, Kubota, & Kawaguchi, 2004;Tremblay et al, 2016); (2) excitatory pathways have well known spatial biases of other kinds, for example, they can selectively target dendrites in specific layers or parts of layers (Harris & Shepherd, 2015;J. S. Lund, 1988;Petreanu, Mao, Sternson, & Svoboda, 2009); (3) excitatory axons are subject to activity-dependent clustering, producing a tendency for co-activated axons to form contacts on nearby spines (DeBello et al, 2014;Iacaruso, Gasler, & Hofer, 2017;Lee, Soares, Thivierge, & Béïque, 2016;van Bommel & Mikhaylova, 2016Weber et al, 2016); (4) individual excitatory axons can show strongly biased projections at the sub-dendrite scale (Bloss et al, 2018;Morgan, Berger, Wetzel, & Lichtman, 2016) (5) proximal vs. distal synapses can be subject to different plasticity rules, which could lead to a spatial sorting-out of functionally distinct input pathways (Froemke, Poo, & Dan, 2005;Gordon, Gribble, Syrett, & Granato, 2012;Sandler, Shulman, & Schiller, 2016); and (6) differences in EPSP rise times suggest horizontal vs. vertical axons (Yoshimura et al, 2000) and near vs. far horizontal connections onto pyramidal neurons (Schnepel, Kumar, Zohar, Aertsen, & Boucsein, 2014) do, on average, terminate at different distances from the soma.…”

Section: Dendrites Provide a Parsimonious Neural Implementation Of Thmentioning

confidence: 99%

Classical-contextual interactions in V1 may rely on dendritic computations

Jin

et al. 2018

Preprint

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A signature feature of the neocortex is the dense network of horizontal connections (HCs) through which pyramidal neurons (PNs) exchange "contextual" information. In primary visual cortex (V1), HCs are thought to facilitate boundary detection, a crucial operation for object recognition, but how HCs modulate PN responses to boundary cues within their classical receptive fields (CRF) remains unknown.We began by "asking" natural images, through a structured data collection and ground truth labeling process, what function a V1 cell should use to compute boundary probability from aligned edge cues within and outside its CRF. The "answer" was an asymmetric 2-D sigmoidal function, whose nonlinear form provides the first normative account for the "multiplicative" center-flanker interactions previously reported in V1 neurons (Kapadia et al. 1995(Kapadia et al. , 2000Polat et al. 1998). Using a detailed compartmental model, we then show that this boundary-detecting classical-contextual interaction function can be computed with near perfect accuracy by NMDAR-dependent spatial synaptic interactions within PN dendrites -the site where classical and contextual inputs first converge in the cortex. In additional simulations, we show that local interneuron circuitry activated by HCs can powerfully leverage the nonlinear spatial computing capabilities of PN dendrites, providing the cortex with a highly flexible substrate for integration of classical and contextual information.2 Significance StatementIn addition to the driver inputs that establish their classical receptive fields, cortical pyramidal neurons (PN) receive a much larger number of "contextual" inputs from other PNs through a dense plexus of horizontal connections (HCs). However by what mechanisms, and for what behavioral purposes, HC's modulate PN responses remains unclear. We pursued these questions in the context of object boundary detection in visual cortex, by combining an analysis of natural boundary statistics with detailed modeling PNs and local circuits. We found that nonlinear synaptic interactions in PN dendrites are ideally suited to solve the boundary detection problem. We propose that PN dendrites provide the core computing substrate through which cortical neurons modulate each other's responses depending on context.

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A Novel Form of Local Plasticity in Tuft Dendrites of Neocortical Somatosensory Layer 5 Pyramidal Neurons

Cited by 43 publications

References 75 publications

Beyond spike-timing-dependent plasticity: a computational study of plasticity gradients across basal dendrites

Beyond spike-timing-dependent plasticity: a computational study of plasticity gradients across basal dendrites

Shaping dendritic NMDA spikes by timed synaptic inhibition: Implications for the I/O properties of cortical neurons

Classical-contextual interactions in V1 may rely on dendritic computations

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