Biomaterial Database

Gap junctions between striatal fast-spiking interneurons regulate spiking activity and synchronization as a function of cortical activity. 2009

Johannes Hjorth, and Kim T Blackwell, and Jeanette Hellgren Kotaleski

Computational Biology, School of Computer Science and Communication, Royal Institute of Technology, Albanova University Centre, 106 91 Stockholm, Sweden. hjorth@kth.se

Striatal fast-spiking (FS) interneurons are interconnected by gap junctions into sparsely connected networks. As demonstrated for cortical FS interneurons, these gap junctions in the striatum may cause synchronized spiking, which would increase the influence that FS neurons have on spiking by the striatal medium spiny (MS) neurons. Dysfunction of the basal ganglia is characterized by changes in synchrony or periodicity, thus gap junctions between FS interneurons may modulate synchrony and thereby influence behavior such as reward learning and motor control. To explore the roles of gap junctions on activity and spike synchronization in a striatal FS population, we built a network model of FS interneurons. Each FS connects to 30-40% of its neighbors, as found experimentally, and each FS interneuron in the network is activated by simulated corticostriatal synaptic inputs. Our simulations show that the proportion of synchronous spikes in FS networks with gap junctions increases with increased conductance of the electrical synapse; however, the synchronization effects are moderate for experimentally estimated conductances. Instead, the main tendency is that the presence of gap junctions reduces the total number of spikes generated in response to synaptic inputs in the network. The reduction in spike firing is due to shunting through the gap junctions; which is minimized or absent when the neurons receive coincident inputs. Together these findings suggest that a population of electrically coupled FS interneurons may function collectively as input detectors that are especially sensitive to synchronized synaptic inputs received from the cortex.

UI	MeSH Term	Description	Entries
D007395	Interneurons	Most generally any NEURONS which are not motor or sensory. Interneurons may also refer to neurons whose AXONS remain within a particular brain region in contrast to projection neurons, which have axons projecting to other brain regions.	Intercalated Neurons,Intercalated Neuron,Interneuron,Neuron, Intercalated,Neurons, Intercalated
D002540	Cerebral Cortex	The thin layer of GRAY MATTER on the surface of the CEREBRAL HEMISPHERES that develops from the TELENCEPHALON and folds into gyri and sulci. It reaches its highest development in humans and is responsible for intellectual faculties and higher mental functions.	Allocortex,Archipallium,Cortex Cerebri,Cortical Plate,Paleocortex,Periallocortex,Allocortices,Archipalliums,Cerebral Cortices,Cortex Cerebrus,Cortex, Cerebral,Cortical Plates,Paleocortices,Periallocortices,Plate, Cortical
D003342	Corpus Striatum	Striped GRAY MATTER and WHITE MATTER consisting of the NEOSTRIATUM and paleostriatum (GLOBUS PALLIDUS). It is located in front of and lateral to the THALAMUS in each cerebral hemisphere. The gray substance is made up of the CAUDATE NUCLEUS and the lentiform nucleus (the latter consisting of the GLOBUS PALLIDUS and PUTAMEN). The WHITE MATTER is the INTERNAL CAPSULE.	Lenticular Nucleus,Lentiform Nucleus,Lentiform Nuclei,Nucleus Lentiformis,Lentiformis, Nucleus,Nuclei, Lentiform,Nucleus, Lenticular,Nucleus, Lentiform,Striatum, Corpus
D000200	Action Potentials	Abrupt changes in the membrane potential that sweep along the CELL MEMBRANE of excitable cells in response to excitation stimuli.	Spike Potentials,Nerve Impulses,Action Potential,Impulse, Nerve,Impulses, Nerve,Nerve Impulse,Potential, Action,Potential, Spike,Potentials, Action,Potentials, Spike,Spike Potential
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron
D017629	Gap Junctions	Connections between cells which allow passage of small molecules and electric current. Gap junctions were first described anatomically as regions of close apposition between cells with a narrow (1-2 nm) gap between cell membranes. The variety in the properties of gap junctions is reflected in the number of CONNEXINS, the family of proteins which form the junctions.	Gap Junction,Junction, Gap,Junctions, Gap