A Cooperative Switch Determines the Sign of Synaptic Plasticity in Distal Dendrites of Neocortical Pyramidal Neurons
ABSTRACT Pyramidal neurons in the cerebral cortex span multiple cortical layers. How the excitable properties of pyramidal neuron dendrites allow these neurons to both integrate activity and store associations between different layers is not well understood, but is thought to rely in part on dendritic backpropagation of action potentials. Here we demonstrate that the sign of synaptic plasticity in neocortical pyramidal neurons is regulated by the spread of the backpropagating action potential to the synapse. This creates a progressive gradient between LTP and LTD as the distance of the synaptic contacts from the soma increases. At distal synapses, cooperative synaptic input or dendritic depolarization can switch plasticity between LTD and LTP by boosting backpropagation of action potentials. This activity-dependent switch provides a mechanism for associative learning across different neocortical layers that process distinct types of information.
Full-textDOI: · Available from: Per Jesper Sjöström, Sep 27, 2015
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- ", 2002 ; Waters et al . , 2003 ; Sjöström and Häusser , 2006 ) . Because of their relation to global variables such as concentration of calcium in neurons or firing rate of neuronal populations , mechanisms of homeostatic regulation of synaptic weights might be embedded into a multi - level system of neuronal homeostasis and thus could be triggered by signals from several different levels . "
ABSTRACT: Homosynaptic Hebbian-type plasticity provides a cellular mechanism of learning and refinement of connectivity during development in a variety of biological systems. In this review we argue that a complimentary form of plasticity-heterosynaptic plasticity-represents a necessary cellular component for homeostatic regulation of synaptic weights and neuronal activity. The required properties of a homeostatic mechanism which acutely constrains the runaway dynamics imposed by Hebbian associative plasticity have been well-articulated by theoretical and modeling studies. Such mechanism(s) should robustly support the stability of operation of neuronal networks and synaptic competition, include changes at non-active synapses, and operate on a similar time scale to Hebbian-type plasticity. The experimentally observed properties of heterosynaptic plasticity have introduced it as a strong candidate to fulfill this homeostatic role. Subsequent modeling studies which incorporate heterosynaptic plasticity into model neurons with Hebbian synapses (utilizing an STDP learning rule) have confirmed its ability to robustly provide stability and competition. In contrast, properties of homeostatic synaptic scaling, which is triggered by extreme and long lasting (hours and days) changes of neuronal activity, do not fit two crucial requirements for a hypothetical homeostatic mechanism needed to provide stability of operation in the face of on-going synaptic changes driven by Hebbian-type learning rules. Both the trigger and the time scale of homeostatic synaptic scaling are fundamentally different from those of the Hebbian-type plasticity. We conclude that heterosynaptic plasticity, which is triggered by the same episodes of strong postsynaptic activity and operates on the same time scale as Hebbian-type associative plasticity, is ideally suited to serve a homeostatic role during on-going synaptic plasticity.Frontiers in Computational Neuroscience 07/2015; 9:89. DOI:10.3389/fncom.2015.00089 · 2.20 Impact Factor
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- "Since all types of principal neurons as well as interneurons in all layers are possible targets of horizontal projections (Yang et al. 2013), it will be crucial to investigate the horizontal connectivity of other cell types apart from L5B-pyr covered in this study. On the single neuron level, the idea of dendritic computation (London and Häusser 2005) suggests that spatially extended neurons like L5B-pyr employ compartmentalized processing, using multiple mechanisms such as Ca 2+ -/NMDAspikes , location dependence of synaptic activation, and differential expression of plasticity (Schiller et al. 2000; Sjöström and Häusser 2006; Kampa et al. 2007; Larkum et al. 2009; Branco et al. 2010; Behabadi et al. 2012). Therefore, it will be essential to extend the study of horizontal projections to other compartments of the dendritic tree of L5B-pyr. "
ABSTRACT: Cortical information processing at the cellular level has predominantly been studied in local networks, which are dominated by strong vertical connectivity between layers. However, recent studies suggest that the bulk of axons targeting pyramidal neurons most likely originate from outside this local range, emphasizing the importance of horizontal connections. We mapped a subset of these connections to L5B pyramidal neurons in rat somatosensory cortex with photostimulation, identifying intact projections up to a lateral distance of 2 mm. Our estimates of the spatial distribution of cells presynaptic to L5B pyramids support the idea that the majority is located outside the local volume. The synaptic physiology of horizontal connections does not differ markedly from that of local connections, whereas the layer and cell-type-dependent pattern of innervation does. Apart from L2/3, L6A provides a strong source of horizontal connections. Implementing our data into a spiking neuronal network model shows that more horizontal connections promote robust asynchronous ongoing activity states and reduce noise correlations in stimulus-induced activity. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: firstname.lastname@example.org.Cerebral Cortex 11/2014; DOI:10.1093/cercor/bhu265 · 8.67 Impact Factor
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- "Proximal synapses with a distance to the soma smaller than 200 μm were assumed to be dominated by NMDA receptors and did not contain any AMPA receptors (see Methods). The experimental data (triangles) show a similar switch from LTP to LTD as a function of distance (Sjöström and Häusser, 2006). "
ABSTRACT: Long-term synaptic plasticity is fundamental to learning and network function. It has been studied under various induction protocols and depends on firing rates, membrane voltage, and precise timing of action potentials. These protocols show different facets of a common underlying mechanism but they are mostly modeled as distinct phenomena. Here, we show that all of these different dependencies can be explained from a single computational principle. The objective is a sparse distribution of excitatory synaptic strength, which may help to reduce metabolic costs associated with synaptic transmission. Based on this objective we derive a stochastic gradient ascent learning rule which is of differential-Hebbian type. It is formulated in biophysical quantities and can be related to current mechanistic theories of synaptic plasticity. The learning rule accounts for experimental findings from all major induction protocols and explains a classic phenomenon of metaplasticity. Furthermore, our model predicts the existence of metaplasticity for spike-timing-dependent plasticity Thus, we provide a theory of long-term synaptic plasticity that unifies different induction protocols and provides a connection between functional and mechanistic levels of description.Frontiers in Synaptic Neuroscience 03/2014; 6:3. DOI:10.3389/fnsyn.2014.00003