Identification and Validation of Structures in Neural Population Responses

A fundamental challenge of neuroscience is to understand how interconnected populations of neurons give rise to the remarkable computational abilities of our brains. Large neural datasets offer promise, but they are perilous: they are too complex to be studied with traditional single-neuron analyses, and thus require new analyses that can uncover structure at the level of the population. However, since these analyses operate on large datasets, our intuition whether structure is significant breaks down. Hence, we run the risk of over-interpreting structure from the population data that may have a simple explanation. Thus, with population analysis methods, there is also a need for methods that can validate the significance of structure identified. In this dissertation, I discuss topics covering both the identification and the validation of structure in population data. In the first part, I discuss novel methods for uncovering the computational strategy employed by the motor cortex to flexibly switch between different neural computations. I demonstrate that collective activity patterns of motor cortex neurons related to different computations are orthogonal yet can still be linked, indicating a degree of flexibility that was not displayed or predicted by existing cortical models. In the second part, I discuss a novel analytical framework to rigorously test the novelty of population-level findings, given a specified set of primary features such as correlations across time, neurons and experimental conditions. This framework provides a general tool for validating population findings across the brain and across population-level hypotheses

Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames

Automatically discovering composable abstractions from raw perceptual data is a long-standing challenge in machine learning. Recent slot-based neural networks that learn about objects in a self-supervised manner have made exciting progress in this direction. However, they typically fall short at adequately capturing spatial symmetries present in the visual world, which leads to sample inefficiency, such as when entangling object appearance and pose. In this paper, we present a simple yet highly effective method for incorporating spatial symmetries via slot-centric reference frames. We incorporate equivariance to per-object pose transformations into the attention and generation mechanism of Slot Attention by translating, scaling, and rotating position encodings. These changes result in little computational overhead, are easy to implement, and can result in large gains in terms of data efficiency and overall improvements to object discovery. We evaluate our method on a wide range of synthetic object discovery benchmarks namely CLEVR, Tetrominoes, CLEVRTex, Objects Room and MultiShapeNet, and show promising improvements on the challenging real-world Waymo Open dataset.Comment: Accepted at ICML 2023. Project page: https://invariantsa.github.io

Dataset from "Farzaneh Najafi, Gamaleldin F Elsayed, Robin Cao, Eftychios Pnevmatikakis, Peter E. Latham, John P Cunningham, Anne K Churchland (bioRxiv, 2018); Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning.”

This package contains data, in NWB (Neurodata Without Borders) format, from the 4 mice included in "Farzaneh Najafi, Gamaleldin F Elsayed, Robin Cao, Eftychios Pnevmatikakis, Peter E. Latham, John P Cunningham, Anne K Churchland (bioRxiv, 2018); Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning.” The "FN_dataSharing/nwb' folder contains NWB files for all recorded sessions for four mice discussed in the paper. Each NWB file represents the data and metadata associated with one recording session. In each NWB file, the metadata related to the session (mouse name, session date/time, lab/institution name, etc.) can be found under "general". Information related to ROI-segmentation such as ROI mask, ROI type (excitatory or inhibitory), poor or good quality, etc. can be found under "modules/Image-Segmentation/pln-seg". Trial information (e.g. start, end times, trial types, trial outcomes, etc.) can be found under "trials". Recorded trial-segmented neuronal responses aligned to different time event (e.g. stimulus start, animal choice, etc.) can be found under "modules/ Trial-based-Segmentation". A jupyter notebook presenting in detail how to work with NWB files is provided at https://github.com/ttngu207/najafi-2018-nwb/blob/master/notebooks/Najafi-2018_example.ipynb

Conservation of preparatory neural events in monkey motor cortex regardless of how movement is initiated

A time-consuming preparatory stage is hypothesized to precede voluntary movement. A putative neural substrate of motor preparation occurs when a delay separates instruction and execution cues. When readiness is sustained during the delay, sustained neural activity is observed in motor and premotor areas. Yet whether delay-period activity reflects an essential preparatory stage is controversial. In particular, it has remained ambiguous whether delay-period-like activity appears before non-delayed movements. To overcome that ambiguity, we leveraged a recently developed analysis method that parses population responses into putatively preparatory and movement-related components. We examined cortical responses when reaches were initiated after an imposed delay, at a self-chosen time, or reactively with low latency and no delay. Putatively preparatory events were conserved across all contexts. Our findings support the hypothesis that an appropriate preparatory state is consistently achieved before movement onset. However, our results reveal that this process can consume surprisingly little time