Modelling questions / aims¶
Starting point:
What are the best strategies for localising sounds with a spiking neural network?
Can we train networks end-to-end to perform as well or better than hand-crafted solutions?
Zachary: I’m going to try a few things to improve performance
Can we understand what these trained networks are doing in a high level way?
Dan: I’m going to try out a few ideas for this.
Do these networks do something similar to what has been proposed in the literature (labelled line models, hemispheric models, pattern match decoders) or something completely different?
Lavinia: I’m interested into looking at this (how spiking neural networks can be used to model observed neural representations).
Do different optimal models emerge in different parameter regimes (head size, signal to noise ratio, multiple sound sources)?
Danish: I have some previous work on head size scaling which could be relevant here.
How do the results depend on the neuron model and available dynamics? For example, does adaptation matter and in which conditions?
Alberto: I’m interested into looking at this (neuron and synapse model, plasticity).
Danish: I’m also interested in looking at this from the perspective of synaptic plasticity and auditory spatial working memory models.
Dilay: I’m interested in using/adapting the filter-and-fire neuron model (recently proposed by Beniaguev et al., 2022) in the SNN.
Technical challenges¶
Delay learning: can we train delays with surrogate gradient descent?
Jakub: I’m interested in looking into this.
Balázs: I’m looking into this using DCLS as in https://
arxiv .org /pdf /2306 .17670 .pdf
Alternatives to surrogate gradient descent?
Danish: I use a correlation learning model of synaptic plasticity which could in principle capture timing delays.
Add a more realistic auditory periphery (cochlear filtering and more realistic spiking model).
Danish: I have a simple vertebrate biophysical model of filtering in the auditory periphery which I could quickly test here.
Number of time steps might be an issue for a more realistic model. May want to use dt=0.1ms and duration>=100ms so more than 1000 time steps.
Add a more realistic sound localisation task (natural sounds, background noise, multiple sound sources, different frequencies).
Tomas: experiment with this, starting with multi-frequency sounds
Danish: I have looked into multi-frequency cochlear filtering for speech localization. It may be useful here.
Divyansh: How well does the model perform at different frequencies (even if still just pure tones)
Consider multiple architectures, potentially matching the auditory system.
Alicja: I’ll look into that, beginning with checking the effect of adding more hidden layers.
Mingxuan: I’ll experiment with this starting with altering the output neurons.
Consider more complicated neuron models and perhaps make some of these neuron model parameters trainable; for what situations can that help?
Danish: I use a correlation learning model of synaptic plasticity which could in principle learn timing and ITD information.
Ido: Might be interesting to consider multi compartment biophysical neuron models. For starters, I’d be interested to try to add dendrites to the individual units (for example by using https://
github .com /Poirazi -Lab /dendrify).
How to understand what the learned network does?
Helena: This is just a warm up, and I am starting with a simple thresholding on the W1W2 plot to improve the contrast. I am going to push file named as SNN_sound_W1W2_threshold_plot.ipynb. I will probably come back to this later.
Use regression instead of classification
Mingxuan: I’d like to experiment with this idea.
Which level of biological realisms does the learning need to comply to? Which observables are available at the synapse as input to a learning rule?
Danish: Very interesting question that I would like to explore.
Level of biological realism and impact on performance - Dale law.
Jose: Looking into this
Distribution of inhibitory and excitatory neurons using Dale’s law
Sara: First exploratory and interesting results, warrant further investigation