Natural Variation in Biological and Simulated Central Pattern Generators

Here we analyze natural variability within two types of systems. 1, The output of the biological spinal central pattern generator for locomotion in the cat, and 2, Sets of stochastic neural networks giving an output qualitatively similar to that observed within the biological system. Fictive locomotion contains asymmetric transitions between the flexion and extension phases. The transition from extension to flexion is: 1, Always strongly phase locked; 2, Composed of overlapping extensor burst offsets and flexor burst onsets; and 3, Invariant to changes in mean cycle period. The transition from flexion to extension is: 1, Weakly phase locked within bouts containing short cycle periods, and well phase locked in bouts containing long cycle periods; 2, Offset times of flexor bursts and the onset times of extensor bursts do not overlap; and 3, Strength of phase locking depends critically upon relative timing of flexor offset and extensor onset. Stochastic neural networks that qualitatively reproducing the timing relationships observed within the biological system have outputs that depend upon both the architecture of the network as well as model neuronal type (oscillatory-non-oscillatory). Within models designed to reproduce the bi-phasic activity observed in some muscles, correlation of the bi-phasic burst is strongly influenced by model connectivity. Additionally sets of leaky integrators have burst durations, which are sometimes well correlated even though they are well separated in time. Half-center models producing alternating output are strongly influenced by the internal structure of simulated neurons. A half-center composed of a pair of leaky-integrators has transitions between phases which are always well phase locked, and overlapping. Half-centers composed of intrinsically oscillatory Morris-Lecar neurons have transitions between phases whose phase locking is parameter dependent. This parameter dependence is mainly due to changes in the timing of burst offset and burst onset. We conclude that the output of the biological central pattern generator is likely to be strongly influenced by the intrinsically oscillatory properties of its neurons. Models containing non-intrinsically oscillatory simulated neurons are unable to account for observed variability within the output of the biological system

Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model

Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one to tens of milliseconds. In addition, neurons produce bursts of spikes spanning multiple time bins. To address these issues, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance, we applied these extensions of TE to a spiking cortical network model (Izhikevich, 2006) with known connectivity and a range of synaptic delays. For comparison, we also investigated single-delay TE, at a message length of one bin (D1TE), and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE, this dramatically improved to 73% of true connections. In addition, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE, when used on currently available desktop computers, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons

Microscopy Conference 2017 (MC 2017) - Proceedings

Das Dokument enthält die Kurzfassungen der Beiträge aller Teilnehmer an der Mikroskopiekonferenz "MC 2017", die vom 21. bis 25.08.2017, in Lausanne stattfand