Lorenzo Mazzaschi
Research Subjects
Research Interests
Lorenzo Mazzaschi has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Andrew J. King
Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
1 shared publication - Benjamin D. B. Willmore
University of Oxford
1 shared publication - Nicol Spencer Harper
Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
1 shared publication
Latest Publications
- Open Access
Corticofugal gated recurrency captures auditory cortical responses
Memory is essential for the neural processing of natural sounds. It has been proposed that cortical memory is subserved by gated recurrency, a powerful machine learning method that enables memory of sequential dependencies. However, standard forms lack biological realism. We built a computational model using a subtractive form of gated recurrency, consistent with cortical circuitry and feedback that resembles corticofugal projections. This architecture outperformed state-of-the-art models with delay lines or non-biological gated recurrency at predicting auditory cortical responses to natural sounds. Its performance was further improved by incorporating a network layer that mirrors the high neuronal density in superficial cortical layers. Analyzing the model, we found that memory retention is longer in secondary than in primary auditory cortex, and that gated recurrency particularly helps capture responses during abrupt changes and quiet periods in the sounds. This work lays out the fundamental functional circuitry of the auditory pathway for processing the sequential dependencies in natural sounds.
Open AccessCorticofugal gated recurrency captures auditory cortical responses
Memory is essential for the neural processing of natural sounds. It has been proposed that cortical memory is subserved by gated recurrency, a powerful machine learning method that enables memory of sequential dependencies. However, standard forms lack biological realism. We built a computational model using a subtractive form of gated recurrency, consistent with cortical circuitry and feedback that resembles corticofugal projections. This architecture outperformed state-of-the-art models with delay lines or non-biological gated recurrency at predicting auditory cortical responses to natural sounds. Its performance was further improved by incorporating a network layer that mirrors the high neuronal density in superficial cortical layers. Analyzing the model, we found that memory retention is longer in secondary than in primary auditory cortex, and that gated recurrency particularly helps capture responses during abrupt changes and quiet periods in the sounds. This work lays out the fundamental functional circuitry of the auditory pathway for processing the sequential dependencies in natural sounds.