Events

Recordings of neurons in cortical structures in behaving rodents show responses to dimensions of space and time relevant to encoding and retrieval of spatiotemporal trajectories of behavior in episodic memory. This includes the coding of spatial location by grid cells in entorhinal cortex and place cells in hippocampus, some of which also fire as time cells when a rodent runs on a treadmill (Kraus et al., 2013; 2015; Mau et al., 2018). Trajectory encoding also includes coding of the direction and speed of movement. Speed is coded by both firing rate and frequency of neuronal rhythmicity (Hinman et al., 2016, Dannenberg et al., 2020), and inactivation of input from the medial septum impairs the spatial selectivity of grid cells suggesting rhythmic coding of running speed is important for spatial coding by grid cells (Brandon et al., 2011; Robinson et al., 2023).

The emerging field of NeuroAI has leveraged techniques from artificial intelligence to model brain data. In this talk, Yamins will show that the connection between neuroscience and AI can be fruitful in both directions. Towards "AI driving neuroscience", he will discuss a new candidate universal principal for functional organization in the brain, based on recent advances in self-supervised learning, that explains both fine details as well as large-scale organizational structure in the vision system, and perhaps beyond.  In the direction of "neuroscience guiding AI", Yamins will present a novel cognitively-grounded computational theory of perception that generates robust new learning algorithms for real-world scene understanding.  Taken together, these ideas illustrate how neural networks optimized to solve cognitively-informed tasks provide a unified framework for both understanding the brain and improving AI.

Generally, inference problems in machine or biological vision rely on knowledge of prior probabilities. Recently, machine learning has resulted in dramatic improvements by using artificial neural networks. These prior probabilities are implicit and intertwined with the tasks for which they are optimized.