Update README.md

README.md

@@ -3,24 +3,28 @@ language:
 - en
 pipeline_tag: tabular-classification
 tags:
-- computational neuroscience 
+- Computational Neuroscience 
 ---
 
-To reduce the effort in manual curation, we developed a machine learning approach using Neuropixels probes, incorporating quality metrics to automatically identify noise clusters 
-and isolate single-cell activity. 
-Compatible with the Spikeinterface API, our method generalizes across various probes and speices.
+To reduce the effort in manual curation, we developed a machine learning approach using Neuropixels probes,
+incorporating quality metrics to identify noise clusters and isolate single-cell activity automatically. 
+Compatible with the Spikeinterface API, our method generalizes across various probes and species.
 
+We generated a machine learning model trained on 11 mice in V1, SC, and ALM using Neuropixels on mice.
+Each recording was labeled by at least two people and in different combinations.
+The agreement amongst labelers is 80%. 
 
-The we generated a machine learning model that is trained on 11 mice in V1, SC and ALM using Neuropixels on mice. Each recording was labelled by at least two people, and in different combinations.
-The agreement amongst labellers is 80%. You can use the models "noise_meuron_model.skops" that is used to identify noise and "sua_mua_model.skops" that is used to isolate SUA.
 
 There are two tutorial notebooks:
 
-
 1. Model_based_curation_tutorial.ipynb
 
    This notebook helps you apply pretrained models to new recordings. Simply load the models and use them to label your spike-sorted data.
 
+   
+   We provide "noise_neuron_model.skops" which is used to identify noise, and "sua_mua_model.skops" which is used to isolate SUA. These models
+   can be used if you want to predict on mice data generated using Neuropixels.
+
 
    ``` python
     from spikeinterface.curation import auto_label_units
@@ -31,7 +35,7 @@ There are two tutorial notebooks:
     ```
 
 
-2. Train_new_model.ipynb
+3. Train_new_model.ipynb
 
    If you have your own manually curated data (e.g., from other species), this notebook allows you to train a new model using your specific data.