Spatiotemporally Graded NMDA Spike/Plateau Potentials in Basal Dendrites of Neocortical Pyramidal Neurons

School of Biosciences, Cardiff University, Museum Avenue, Cardiff, Wales, UK.
Journal of Neurophysiology (Impact Factor: 2.89). 06/2008; 99(5):2584-601. DOI: 10.1152/jn.00011.2008
Source: PubMed


Glutamatergic inputs clustered over approximately 20-40 microm can elicit local N-methyl-D-aspartate (NMDA) spike/plateau potentials in terminal dendrites of cortical pyramidal neurons, inspiring the notion that a single terminal dendrite can function as a decision-making computational subunit. A typical terminal basal dendrite is approximately 100-200 microm long: could it function as multiple decision-making subunits? We test this by sequential focal stimulation of multiple sites along terminal basal dendrites of layer 5 pyramidal neurons in rat somatosensory cortical brain slices, using iontophoresis or uncaging of brief glutamate pulses. There was an approximately sevenfold spatial gradient in average spike/plateau amplitude measured at the soma, from approximately 3 mV for distal inputs to approximately 23 mV for proximal inputs. Spike/plateaus were NMDA receptor (NMDAR) conductance-dominated at all locations. Large Ca(2+) transients accompanied spike/plateaus over a approximately 10- to 40-microm zone around the input site; smaller Ca(2+) transients extended approximately uniformly to the dendritic tip. Spike/plateau duration grew with increasing glutamate and depolarization; high Ca(2+) zone size grew with spike/plateau duration. The minimum high Ca(2+) zone half-width (just above NMDA spike threshold) increased from distal (approximately 10 microm) to proximal locations (approximately 25 microm), as did the NMDA spike glutamate threshold. Depolarization reduced glutamate thresholds. Simulations exploring multi-site interactions based on this demonstrate that if appropriately timed and localized inputs occur in vivo, a single basal dendrite could correspond to a cascade of multiple co-operating dynamic decision-making subunits able to retain information for hundreds of milliseconds, with increasing influence on neural output from distal to proximal. Dendritic NMDA spike/plateaus are thus well-suited to support graded persistent firing.

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Available from: Jackie Schiller, Jan 03, 2014
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    • "The synapses recognizing a given pattern have to be colocated on a dendritic segment. If they lie within 40µm of each other then as few as eight synapses are sufficient to create an NMDA spike (Major et al., 2008). If the synapses are spread out along the dendritic segment, then up to twenty synapses are needed (Major et al., 2013). "
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    ABSTRACT: Neocortical neurons have thousands of excitatory synapses. It is a mystery how neurons integrate the input from so many synapses and what kind of large-scale network behavior this enables. It has been previously proposed that non-linear properties of dendrites enable neurons to recognize multiple patterns. In this paper we extend this idea by showing that a neuron with several thousand synapses arranged along active dendrites can learn to accurately and robustly recognize hundreds of unique patterns of cellular activity, even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where some of the patterns recognized by a neuron lead to action potentials and define the classic receptive field of the neuron, whereas the majority of the patterns recognized by a neuron act as predictions by slightly depolarizing the neuron without immediately generating an action potential. We then present a network model based on neurons with these properties and show that the network learns a robust model of time-based sequences. Given the similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory is a universal property of neocortical tissue. We further propose that cellular layers in the neocortex implement variations of the same sequence memory algorithm to achieve different aspects of inference and behavior. The neuron and network models we introduce are robust over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations. The sequence capacity of the network scales linearly with the number of synapses on each neuron. Thus neurons need thousands of synapses to learn the many temporal patterns in sensory stimuli and motor sequences.
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    • "es of neocortical pyramidal neurons ( Amitai et al . , 1993 ; Markram and Sakmann , 1994 ; Yuste et al . , 1994 ; Schwindt and Crill , 1995 ; Schiller et al . , 1997 , 2000 ; Stuart et al . , 1997b ; Larkum et al . , 1999b , 2001 , 2009 ; Zhu , 2000 ; Polsky et al . , 2004 ; Gordon et al . , 2006 ; Kampa and Stuart , 2006 ; Nevian et al . , 2007 ; Major et al . , 2008 ) . However , a growing body of evidence conclusively indicates that TTL5 APs are often initiated in the low threshold axon rather than at the site of dendritic synaptic input ( Stuart et al . , 1997a ; Colbert and Pan , 2002 ; Palmer and Stuart , 2006 ; Kole et al . , 2007b ; Shu et al . , 2007 ; Fleidervish et al . , 2010 ) . Lucy Pal"
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    ABSTRACT: The thick-tufted layer 5 (TTL5) pyramidal neuron is one of the most extensively studied neuron types in the mammalian neocortex and has become a benchmark for understanding information processing in excitatory neurons. By virtue of having the widest local axonal and dendritic arborization, the TTL5 neuron encompasses various local neocortical neurons and thereby defines the dimensions of neocortical microcircuitry. The TTL5 neuron integrates input across all neocortical layers and is the principal output pathway funneling information flow to subcortical structures. Several studies over the past decades have investigated the anatomy, physiology, synaptology, and pathophysiology of the TTL5 neuron. This review summarizes key discoveries and identifies potential avenues of research to facilitate an integrated and unifying understanding on the role of a central neuron in the neocortex.
    Frontiers in Cellular Neuroscience 06/2015; 9:233. DOI:10.3389/fncel.2015.00233 · 4.29 Impact Factor
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    • "Therefore, the amplitude of the dendritic plateau potential is ∼40 mV. The amplitude of the dendritic plateau potential decreases gradually as dendritic voltage transient spreads passively into the cell body (Figure 8A, centripetal direction of propagation ), resulting in a ∼20 mV somatic depolarization (Milojkovic et al., 2004, 2005a,b, 2007; Major et al., 2008). The amplitude of the sustained somatic depolarization (neuronal UP state) depends on the physical location of the input site. "
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    ABSTRACT: SPINY NEURONS OF AMYGDALA, STRIATUM, AND CEREBRAL CORTEX SHARE FOUR INTERESTING FEATURES: (1) they are the most abundant cell type within their respective brain area, (2) covered by thousands of thorny protrusions (dendritic spines), (3) possess high levels of dendritic NMDA conductances, and (4) experience sustained somatic depolarizations in vivo and in vitro (UP states). In all spiny neurons of the forebrain, adequate glutamatergic inputs generate dendritic plateau potentials ("dendritic UP states") characterized by (i) fast rise, (ii) plateau phase lasting several hundred milliseconds, and (iii) abrupt decline at the end of the plateau phase. The dendritic plateau potential propagates toward the cell body decrementally to induce a long-lasting (longer than 100 ms, most often 200-800 ms) steady depolarization (∼20 mV amplitude), which resembles a neuronal UP state. Based on voltage-sensitive dye imaging, the plateau depolarization in the soma is precisely time-locked to the regenerative plateau potential taking place in the dendrite. The somatic plateau rises after the onset of the dendritic voltage transient and collapses with the breakdown of the dendritic plateau depolarization. We hypothesize that neuronal UP states in vivo reflect the occurrence of dendritic plateau potentials (dendritic UP states). We propose that the somatic voltage waveform during a neuronal UP state is determined by dendritic plateau potentials. A mammalian spiny neuron uses dendritic plateau potentials to detect and transform coherent network activity into a ubiquitous neuronal UP state. The biophysical properties of dendritic plateau potentials allow neurons to quickly attune to the ongoing network activity, as well as secure the stable amplitudes of successive UP states.
    Frontiers in Cellular Neuroscience 09/2014; 8:292. DOI:10.3389/fncel.2014.00292 · 4.29 Impact Factor
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