[Re] Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains

A reference implementation of Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains, Masquelier T, Guyonneau R, Thorpe SJ, PLoS ONE 3(1): e1377, 2008. https://doi.org/10.1371/journal.pone.000137

Further towards unambiguous edge bundling: Investigating power-confluent drawings for network visualization

Bach et al. [1] recently presented an algorithm for constructing confluent drawings, by leveraging power graph decomposition to generate an auxiliary routing graph. We identify two problems with their method and offer a single solution to solve both. We also classify the exact type of confluent drawings that the algorithm can produce as 'power-confluent', and prove that it is a subclass of the previously studied 'strict confluent' drawing. A description and source code of our implementation is also provided, which additionally includes an improved method for power graph construction

Neural heterogeneity promotes robust learning

The brain has a hugely diverse, heterogeneous structure. Whether or not heterogeneity at the neural level plays a functional role remains unclear, and has been relatively little explored in models which are often highly homogeneous. We compared the performance of spiking neural networks trained to carry out tasks of real-world difficulty, with varying degrees of heterogeneity, and found that it substantially improved task performance. Learning was more stable and robust, particularly for tasks with a rich temporal structure. In addition, the distribution of neuronal parameters in the trained networks closely matches those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments. Summary Neural heterogeneity is metabolically efficient for learning, and optimal parameter distribution matches experimental data

Improving on legacy conferences by moving online

Scientific conferences and meetings have an important role in research, but they also suffer from a number of disadvantages: in particular, they can have a massive carbon footprint, they are time-consuming, and the high costs involved in attending can exclude many potential participants. The COVID-19 pandemic has led to the cancellation of many conferences, forcing the scientific community to explore online alternatives. Here, we report on our experiences of organizing an online neuroscience conference, neuromatch, that attracted some 3000 participants and featured two days of talks, debates, panel discussions, and one-on-one meetings facilitated by a matching algorithm. By offering most of the benefits of traditional conferences, several clear advantages, and with fewer of the downsides, we feel that online conferences have the potential to replace many legacy conferences

Towards democratizing and automating online conferences: lessons from the neuromatch conferences

Legacy conferences are costly and time consuming, and exclude scientists lacking various resources or abilities. During the 2020 pandemic, we created an online conference platform, Neuromatch Conferences (NMC), aimed at developing technological and cultural changes to make conferences more democratic, scalable, and accessible. We discuss the lessons we learned

Cannabinoid-mediated short-term plasticity in hippocampus

Endocannabinoids modulate both excitatory and inhibitory neurotransmission in hippocampus via activation of pre-synaptic cannabinoid receptors. Here, we present a model for cannabinoid mediated short-term depression of excitation (DSE) based on our recently developed model for the equivalent phenomenon of suppressing inhibition (DSI). Furthermore, we derive a simplified formulation of the calcium-mediated endocannabinoid synthesis that underlies short-term modulation of neurotransmission in hippocampus. The simplified model describes cannabinoid-mediated short-term modulation of both hippocampal inhibition and excitation and is ideally suited for large network studies. Moreover, the implementation of the simplified DSI/DSE model provides predictions on how both phenomena are modulated by the magnitude of the pre-synaptic cell's activity. In addition we demonstrate the role of DSE in shaping the post-synaptic cell's firing behaviour qualitatively and quantitatively in dependence on eCB availability and the pre-synaptic cell's activity. Finally, we explore under which conditions the combination of DSI and DSE can temporarily shift the fine balance between excitation and inhibition. This highlights a mechanism by which eCBs might act in a neuro-protective manner during high neural activity

Spike-Timing-Based Computation in Sound Localization

Spike timing is precise in the auditory system and it has been argued that it conveys information about auditory stimuli, in particular about the location of a sound source. However, beyond simple time differences, the way in which neurons might extract this information is unclear and the potential computational advantages are unknown. The computational difficulty of this task for an animal is to locate the source of an unexpected sound from two monaural signals that are highly dependent on the unknown source signal. In neuron models consisting of spectro-temporal filtering and spiking nonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that depend on source location rather than on source signal. Location-specific synchrony patterns would then result in the activation of location-specific assemblies of postsynaptic neurons. We designed a spiking neuron model which exploited this principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related transfer functions. The model was able to accurately estimate the location of previously unknown sounds in both azimuth and elevation (including front/back discrimination) in a known acoustic environment. We found that multiple representations of different acoustic environments could coexist as sets of overlapping neural assemblies which could be associated with spatial locations by Hebbian learning. The model demonstrates the computational relevance of relative spike timing to extract spatial information about sources independently of the source signal