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Scientific Understanding of Consciousness |
Dendritic Microcircuit Computations
Science 20 January 2012: Vol. 335 no. 6066 pp. 353-356 Locally Synchronized Synaptic Inputs Naoya Takahashi1, Kazuo Kitamura2,3, Naoki Matsuo3,4, Mark Mayford5, Masanobu Kano2, Norio Matsuki1, Yuji Ikegaya1,3 1Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Bunkyo-ku, Tokyo, Japan. 2Department of Neurophysiology, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan. 3Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan. 4The HAKUBI Center, Kyoto University, Sakyo-ku, Kyoto, Japan. 5Department of Cell Biology, The Scripps Research Institute, La Jolla, CA, USA.
Coordinating Synapses (editor’s summary)Cortical microcircuits produce cell assemblies that emit spatiotemporally orchestrated spiking activity. These activity patterns are decoded by the dendrites of downstream neurons. Whether synaptic inputs are clustered or dispersed over target dendrites at a given time is critical for determining dendritic computational power. However, such subcellular dynamics are poorly understood. In rodent organotypic slice cultures, researchers found that dendritic spine activities were frequently synchronized within a group of spines in the immediate vicinity of one another. This local synchronization seems to reflect convergent synaptic inputs from intrinsically synchronized presynaptic neuron populations.
[paraphrase] Synaptic inputs on dendrites are nonlinearly converted to action potential outputs, yet the spatiotemporal patterns of dendritic activation remain to be elucidated at single-synapse resolution. In rodents, we optically imaged synaptic activities from hundreds of dendritic spines in hippocampal and neocortical pyramidal neurons ex vivo and in vivo. Adjacent spines were frequently synchronized in spontaneously active networks, thereby forming dendritic foci that received locally convergent inputs from presynaptic cell assemblies. This precise subcellular geometry manifested itself during NMDA receptor–dependent circuit remodeling. Thus, clustered synaptic plasticity is innately programmed to compartmentalize correlated inputs along dendrites and may reify nonlinear synaptic integration. Cortical microcircuits are nonrandomly intertwined and form cell assemblies that fire in a spatiotemporally orchestrated manner. This patterned activity is decoded by the dendrites of downstream neurons. Dendrites are arborized and electrically active, which allows them to exhibit local nonlinear membrane potential dynamics and to transform different spatiotemporal sequences of incoming inputs into different output patterns. Therefore, knowing whether synaptic inputs are clustered or dispersed over dendrites at a given time is critical for determining the dendritic computational power; however, these dynamics are still poorly understood. We monitored spontaneous synaptic inputs using dual patch-clamp recordings under confocal visualization from different apical dendritic branches of individual CA3 pyramidal cells in rat hippocampal slices that were cultured for 12 to 19 days. Large postsynaptic potentials (that is, putative synchronized inputs) often occurred in only one branch. The Euclidean distance in membrane potentials between two branches was distributed with a long tail, suggesting that dendrites received spatially biased synchronous inputs. The locations of spines were three-dimensionally determined post hoc to measure the path length from the soma along the dendrites. The spines did not differ with respect to activity levels between the basal and radial oblique dendrites or between the proximal and distal dendrites. Therefore, all data were pooled in the following analyses. The activity frequency and the spine head size, each of which approximated a log-normal distribution, correlated only weakly with each other. In regard to the population dynamics, 85.0% of the assemblets occurred sporadically, whereas the remaining 15.0% occurred in synchrony with other assemblets. When synchronized, the assemblets tended to appear more than 80 μm apart from one other. Spines that participated in assemblets were larger in head size than nonparticipants, which suggests that assemblets are shaped by long-term synaptic plasticity. Thus, functional synaptic clustering is likely to develop through NMDA receptor–dependent circuit remodeling. The clustered plasticity may result from interspine interactions that heterosynaptically modulate the threshold for long-term potentiation, such as local depolarization-induced Mg2+ unblock of nearby NMDA receptors or intracellular diffusion of plasticity-associated molecules. We found that synaptic inputs were frequently synchronized within a group of spines in the immediate vicinity of one another. Given that ex vivo networks are subject to massive axon reorganization during cultivation without external inputs, our data indicate that the locally convergent connectivity that generates assemblets emerges through self-organization. Thus, the default principle for designing circuit topology is biased to facilitate dendritic compartmentalization. The resultant clustered synchrony may offer opportunities for associative learning, because vicinal spines encode different information. Because the video frame rate of our spine imaging was limited to a maximum of 20 Hz to maintain the signal-to-noise ratio, we could not determine the internal structure of assemblets; however, given that the ex vivo hippocampal network synchrony accompanies sharp waves and ripples, assemblets are expected to coordinate temporal activity sequences. Such sequential activation would facilitate nonlinear synaptic integration and enhance the computational power of a single neuron. Two recent studies have reported phenomena demonstrating activity-dependent clustering of synaptic inputs to developing dendrites of hippocampal slice cultures and clustered synaptic plasticity in the developing somatosensory cortex. [end of paraphrase] Return to — Dendritic TreesReturn to — Neural Network |