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| Hauptseite > Publikationsdatenbank > Can Spatio-Temporal Spike Patterns Found in Experimental Data be Explained by the Synfire Chain Model |
| Hauptseite > Publikationsdatenbank > Can Spatio-Temporal Spike Patterns Found in Experimental Data be Explained by the Synfire Chain Model |
| Poster (Other) | FZJ-2019-05075 |
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2019
Please use a persistent id in citations: http://hdl.handle.net/2128/23095
Abstract: To investigate cortical network interactions during a reach-to-grasp task [1], we analyzed spatio-temporal patterns (STPs) in massively parallel spike data. Using the SPADE analysis [2,3], we found significant STPs in about 100 simultaneously recorded single units. For each of the four task types, we observe up to 50 patterns during the movement period. The STPs differ in spatial and temporal arrangement of spikes, and are composed of 2 to 6 units which belong to the same set of maximal 10 units. Here, we investigate if the characteristics of the found STPs can be explained by a simple assembly network model, the synfire chain (SFC) model [4].In the SFC model, neurons form groups connected in a feedforward, highly convergent-divergent manner. Synchronous stimulation of neurons in the first group results in volleys of spikes reliably propagating through the chain [5]. Spike recordings from a subset of cells in this model would reveal recurring STPs similar to those observed in the data, provided the same SFCs are repeatedly stimulated.We investigate if the observed STP statistics is consistent with a network model where SFCs are spatially distributed in accordance with biologically realistic connection probabilities [6,7]. In the context of this model, we evaluate the probability of observing multiple neurons involved in the same STP by means of a 10x10 Utah electrode array spanning 4x4 mm2 of cortical space. We explore how model parameters such as the neuron density, the distance dependence of lateral connections between cortical neurons and the spatial reach of extracellular electrodes constrain the spatial arrangement of SFCs (see figure) and the number of observable SFC neurons.In future work, we will equip the current network model with a temporal dynamics [8], and further, embed it into a balanced network [similar to 9] to study the temporal characteristics of STPs.
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