Update science.md (#140)

Fixed typo: "these model's perform too poorly" -> "these models perform too poorly"

paper/sections/science.md

@@ -4,7 +4,7 @@ In the [Cosyne tutorial](https://neural-reckoning.github.io/cosyne-tutorial-2022
 
 Animals localise sounds by detecting location- or direction-specific cues in the signals that arrive at their ears. Some of the most important sources of cues (although not the only ones) come from differences in the signals between two ears, including both level and timing differences. Respectively, termed interaural level difference (ILD) and interaural timing difference (ITD). In some cases humans are able to detect arrival time differences as small as 20 $\mu$s.
 
-The classic model of ITD sensitivity is the delay line model of {cite:t}`Jeffress1948` in which an array of binaural coincidence detector neurons receive inputs from the two ears with different delays. When a neurons' delays exactly match the acoustic delays induced by the sound location, it will be maximally active. Therefore, the identity of the most active neuron indicates the direction of the sound. This model is widely accepted, though was shown to be inefficient with respect to neural noise by {cite:t}`McAlpine2003`, who proposed an alternative model based on the two binaural hemispheres average firing rates - which is optimally robust to neural noise. However, {cite:t}`goodman_decoding_2013` showed that these model's perform too poorly to account for behavioural data, especially in situations where sounds had complex and unknown spectral properties, or in the presence of background noise, and proposed an alternative based on a perceptron-like neural network - which is both robust to neural noise and performed well across a range of conditions. 
+The classic model of ITD sensitivity is the delay line model of {cite:t}`Jeffress1948` in which an array of binaural coincidence detector neurons receive inputs from the two ears with different delays. When a neurons' delays exactly match the acoustic delays induced by the sound location, it will be maximally active. Therefore, the identity of the most active neuron indicates the direction of the sound. This model is widely accepted, though was shown to be inefficient with respect to neural noise by {cite:t}`McAlpine2003`, who proposed an alternative model based on the two binaural hemispheres average firing rates - which is optimally robust to neural noise. However, {cite:t}`goodman_decoding_2013` showed that these models perform too poorly to account for behavioural data, especially in situations where sounds had complex and unknown spectral properties, or in the presence of background noise, and proposed an alternative based on a perceptron-like neural network - which is both robust to neural noise and performed well across a range of conditions. 
 
 Building on this literature, and our Cosyne tutorial, the starting point of this 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 in a simple way? What properties would the solution have in terms of robustness to noise, generalisation, and so forth? Could the solutions found by optimisation throw light on features found in the auditory systems of different animals? 
 
@@ -52,4 +52,4 @@ Finally, we developed a more detailed model in which we used over 170,000 units,
 
 In short, input spectrograms representing sounds at azimuth angles from -90° to +90° were converted into spikes, then passed forward to populations representing the globular and spherical bushy cells, and subsequently the lateral and medial superior olivary nuclei, from which we readout sound source angle predictions. Note that, unlike the work with our base model, we used no learnable parameters in this model, and instead based parameters on neurophysiological data. For example, the MSO units had excitatory inputs from both the ipsi and contralateral SBCs and dominant inhibition from contralateral GBCs.
 
-The model generated realistic tuning curves for lateral and medial superior olive (LSO and MSO) neurons. Moreover, removing inhibition shifted ITD sensitivity to the midline, as in [@Brand2002;@Pecka2008].
+The model generated realistic tuning curves for lateral and medial superior olive (LSO and MSO) neurons. Moreover, removing inhibition shifted ITD sensitivity to the midline, as in [@Brand2002;@Pecka2008].