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

Abstract

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.

Action potential initiation in a two-compartment model of pyramidal neuron mediated by dendritic Ca2+ spike

“…As a common cell type in mammalian brain, pyramidal neurons have been studied with theoretical approaches that incorporate dendritic Ca 2+ channel in multi-compartmental models213334353637. These complex models may express more than 10 voltage-gated channels, which are non-homegenously distributed along the somato-dendritic axis.…”

“…In latter integration mode, the synaptic input directly activates the Ca 2+ channel in dendrites and triggers dendritic spikes71415161720, which propagates forward to the axon where the global integration occurs1819. Such integration lies at the heart of neural computation, which is tightly related to coincidence detection1162122, orientation tuning22, binding of synaptic signals from brain areas23, and enhancing stimulus selectivity24. Understanding how it participates in AP output is therefore fundamental to understanding how relevant circuits function in cortical computation of mammalian brain.…”

“…In addition to above in vitro observations, there are also modeling studies that focus on the dendritic Ca 2+ activity213334353637. Most of them use biophysically realistic neurons that are modeled in NEURON or GENESIS to understand the mechanism underlying the generation and propagation of dendritic Ca 2+ spike.…”

Electrical Compartmentalization in Neurons

“…This in turn should enable neurons to learn linearly nonseparable functions (Schiess et al, 2016) and implement translation invariance (Mel et al, 1998). On the network level, independent subunits are thought to dramatically increase memory capacity (Poirazi and Mel, 2001), to allow for the stable storage of feature associations (Bono and Clopath, 2017), represent a powerful mechanism for coincidence detection (Chua and Morrison, 2016;Larkum et al, 1999), and support the back-prop algorithm to train neural networks (Guergiuev et al, 2017;Sacramento et al, 2017;Urbanczik and Senn, 2014).…”

Dynamic compartmentalization in neurons enables branch-specific learning

“…This in turn should enable them to learn linearly non-separable functions, such as XOR (Schiess et al, 2016), and to implement translation invariance (Mel et al, 1998). On the network level, independent compartments are thought to dramatically increase memory capacity (Poirazi and Mel, 2001;Wu and Mel, 2009), to allow for the stable storage of feature associations (Bono and Clopath, 2017), to represent a powerful mechanism for coincidence detection (Larkum et al, 1999;Chua and Morrison, 2016) and to support the back-prop algorithm to train neural networks (Guergiuev et al, 2016).…”