Camilo J. Mininni

The paper shows that speed-dependent place- and time-field shifts in hippocampal neurons can occur without explicit temporal coding, using computational models and recurrent neural networks.

This paper challenges the interpretation of hippocampal firing field shifts as evidence for space-time integration by proposing an alternative model based on velocity integration.

This paper introduces a method to construct binary recurrent neural networks with pre-specified dynamics and demonstrates its application in modeling various types of network behavior.

The paper introduces a method to construct neural networks by solving a system of linear equations to probe structure-function relationships in a memory task, finding networks that traditional optimization algorithms fail to produce.

This paper shows that a single neural population cannot learn a serial reversal task due to limitations in integrating information and selecting responses, proposing a model that separates these functions.