Plasticity of olfactory bulb inputs mediated by dendritic NMDA-spikes in rodent piriform cortex

Abstract

The piriform cortex (PCx) is essential for learning of odor information. The current view postulates odor learning in the PCx is mainly due to plasticity in intracortical (IC) synapses, while odor information from the olfactory bulb carried via the lateral olfactory tract (LOT) is 'hardwired'. Here we revisit this notion by studying location and pathway dependent plasticity rules. We find that in contrast to the prevailing view, synaptic and optogenetically activated LOT synapses undergo strong and robust long-term potentiation (LTP) mediated by only few local NMDA-spikes delivered at theta frequency, while global spike timing dependent plasticity protocols (STDP) failed to induce LTP in these distal synapses. In contrast, IC synapses in apical and basal dendrites undergo plasticity with both NMDA-spikes and STDP protocols but to a smaller extent compared with LOT synapses. These results are consistent with a self-potentiating mechanism of odor information via NMDA-spikes which can form branch-specific memory traces of odors that can further associate with contextual IC information via STDP mechanisms to provide cognitive and emotional value to odors.

Asymmetric voltage attenuation in dendrites can enable hierarchical heterosynaptic plasticity

“…If dendritic NMDA spikes indeed act as heterosynaptic supervisors for other spines on the dendrite, the dendritic branching structure and the location of NMDA spike induction become essential to the implementation and functional consequences of neural plasticity. Location-sensitive NMDA spike-dependent plasticity rules are particularly critical in light of findings that backpropagating somatic action potentials may not reach distal synapses for the purpose of plasticity induction, while NMDA spikes can induce plasticity at distal synapses [15].…”

“…While some plasticity-inducing protocols such as spike-timing-dependent plasticity (STDP) require postsynaptic depolarization, in many cases it is possible to produce long-term potentiation (LTP) or long-term depression (LTD) via presynaptic stimulation alone (e.g. using high or low frequency stimulation, respectively), and some have argued that presynaptic inputs (without post synaptic response) are the primary driver of plasticity in the hippocampus [12][13][14] as well as in some cases in the cortex (Kumar et al, 2021).Over the past decades, since first proposed by John Lisman [16], evidence has mounted for a calcium-based theory of plasticity, known as the calcium control hypothesis [16][17][18][19][20][21]. In this framework, synapses change their strength depending on the calcium concentration ([Ca 2+ ]) at the postsynaptic dendritic spine.…”

Fast and slow feedforward inhibitory circuits for cortical odor processing

“…Indeed, it has been directly shown in vitro that a single layer 1 interneuron can inhibit Ca 2+ signaling in the distal dendrites of PCx principal cells in a branch-specific fashion ( Stokes et al, 2014 ). Feedforward inhibition might then provide a mechanism for regulating processes that involve dendritic electrogenesis and plasticity – including burst-firing ( Tseng and Haberly, 1989 ; Protopapas and Bower, 2001 ), spike timing-dependent plasticity ( Kanter et al, 1996 ; Johenning et al, 2009 ; Cassenaer and Laurent, 2012 ), and NMDA spikes ( Kumar et al, 2018 ; Kumar et al, 2021 ) – and which may only become apparent during experimental paradigms that engage olfactory learning ( Wilson and Stevenson, 2006 ; Ghosh et al, 2015 ; Shakhawat et al, 2015 ; Meissner-Bernard et al, 2019 ). Future work would need to explore this possibility.…”

A dendritic mechanism for balancing synaptic flexibility and stability

“…Convergent spatiotemporal input patterns arriving at a dendritic compartment can drive NMDAR-mediated calcium influx 17 and trigger molecular cascades for synaptic modification 18 . Afferents from specific presynaptic populations can also target particular dendritic compartments 16,19,20 , where plasticity rules can be location-dependent [21][22][23][24][25][26][27] . However, it is unclear how distinct inputs utilize these mechanisms, and less is understood how inputspecific synaptic weights are maintained across experience [28][29][30] .…”

“…In artificial learning systems, this problem manifests as catastrophic forgetting, where previously acquired information is overwritten unless weight changes are limited (Hassabis et al, 2017; McClelland and Rumelhart, 1985; McCloskey and Cohen, 1989). While half a century of experimental and theoretical work on biological synaptic plasticity has focused on how plasticity is induced (Froemke et al, 2005; Golding et al, 2002; Gordon et al, 2006; Kampa et al, 2006; Kumar et al, 2021; Letzkus et al, 2006; Magó et al, 2020; Markram et al, 1997; Sjöström and Häusser, 2006; Weber et al, 2016) and maintained within a working range (Abraham, 2008; Bienenstock et al, 1982; Cooper and Bear, 2012; Lee and Kirkwood, 2019; Li et al, 2019; Turrigiano, 2008), little is currently known about how neurons sustain fundamental information throughout a lifetime of experience-dependent plasticity.…”