This webinar brings together three leaders in theoretical and computational neuroscience—Larry Abbott, Haim Sompolinsky, and Terry Sejnowski—to discuss how neural circuits generate fundamental aspects of the mind. Abbott illustrates mechanisms in electric fish that differentiate self-generated electric signals from external sensory cues, showing how predictive plasticity and two-stage signal cancellation mediate a sense of self. Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaotic dynamics underlying spontaneous behaviors. He further highlights the concept of object manifolds in high-level sensory representations and raises open questions on integrating connectomics with theoretical frameworks. Sejnowski bridges these motifs with modern artificial intelligence, demonstrating how large-scale neural networks capture language structures through distributed representations that parallel biological coding. Together, their presentations emphasize the synergy between empirical data, computational modeling, and connectomics in explaining the neural basis of cognition—offering insights into perception, memory, language, and the emergence of mind-like processes.

Spike-driven adaptation involves intracellular mechanisms that are initiated by neural firing and lead to the subsequent reduction of spiking rate followed by a recovery back to baseline. We report on long (>0.5 second) recovery times from adaptation in a thalamic-like structure in weakly electric fish. This adaptation process is shown via modeling and experiment to encode in a spatially invariant manner the time intervals between event encounters, e.g. with landmarks as the animal learns the location of food. These cells also come in two varieties, ones that care only about the time since the last encounter, and others that care about the history of encounters. We discuss how the two populations can share in the task of representing sequences of events, supporting path integration and converting from ego-to-allocentric representations. The heterogeneity of the population parameters enables the representation and Bayesian decoding of time sequences of events which may be put to good use in path integration and hilus neuron function in hippocampus. Finally we discuss how all the cells of this gateway to the pallium exhibit mixed selectivity of social features of their environment. The data and computational modeling further reveal that, in contrast to a long-held belief, these gymnotiform fish are endowed with a corollary discharge, albeit only for social signalling.

The electrosensory lobe (ELL) in mormyrid electric fish is a cerebellar-like structure that cancels the sensory effects of self-generated electric fields, allowing prey to be detected. Like the cerebellum, the ELL involves two stages of processing, analogous to the Purkinje cells and cells of the deep cerebellar nuclei. Through the work of Curtis Bell and others, a model was previously developed to describe the output stage of the ELL, but the role of the Purkinje-cell analogs, the medium ganglion (MG) cells, in the circuit had remained mysterious. I will present a complete, multi-layer circuit description of the ELL, developed in collaboration with Nate Sawtell and Salomon Muller, that reveals a novel role for the MG cells. The resulting model provides an example of how a biological system solves well-known problems associated with learning in multi-layer networks, and it reveals that ELL circuitry is organization on the basis of learning rather than by the response properties of neurons.