typo

paper/sections/science.md

@@ -9,7 +9,7 @@ Building on this literature, and a tutorial we ran {cite:p}`10.5281/zenodo.70445
 
 Building on this literature, and a tutorial we ran {cite:p}`10.5281/zenodo.7044500`, the starting point of our project was to ask: what solutions would you find if you directly optimised a spiking neural network to localise sounds? How would those solutions depend on the available neural mechanisms and statistics of the sound? Could we understand the solutions found? What properties would the solution have in terms of robustness to noise, generalisation, and so forth? And could the solutions found by optimisation throw light on features found in the auditory systems of different animals? 
 
-Two things are worth noting here. First, our solutions may be very different to optimal solutions or other neural network-based solutions. This literature is reviewed in {cite:t}`Grumiaux2022`, including classical engineering approaches such as beamforming, and deep learning approaches such as convolutional neural networks, recurrent neural networks and attention-based networks. Secondly, our setup is fairly limited in terms of the available cues and network structure: we only use pure tones we have no spectral or cross-frequency cues, we fix the level so we have no interaural level differences, etc. We would not necessarily expect this approach to explain a wide range of observed phenomena, but it may still throw light on some fundamental aspects of interaural time or phase difference circuits.
+Two things are worth noting here. First, our solutions may be very different to optimal solutions or other neural network-based solutions. This literature is reviewed in {cite:t}`Grumiaux2022`, including classical engineering approaches such as beamforming, and deep learning approaches such as convolutional neural networks, recurrent neural networks and attention-based networks. Secondly, our setup is fairly limited in terms of the available cues and network structure: we only use pure tones, we have no spectral or cross-frequency cues, we fix the level so we have no interaural level differences, etc. We would not necessarily expect this approach to explain a wide range of observed phenomena, but it may still throw light on some fundamental aspects of interaural time or phase difference circuits.
 
 ## A simple spiking neural network model