Comparison Against Task Driven Artificial Neural Networks Reveals Functional Organization of Mouse Visual Cortex Shi, Jianghong, Shea-Brown, Eric, and Buice, Michael NeurIPS 2019
Partially inspired by features of computation in visual cortex, deep neural networks compute hierarchical representations of their inputs. While these networks have been highly successful in machine learning, it remains unclear to what extent they can aid our understanding of cortical function. Several groups have developed metrics that provide a quantitative comparison between representations computed by networks and representations measured in cortex. At the same time, neuroscience is well into an unprecedented phase of large-scale data collection, as evidenced by projects such as the Allen Brain Observatory. Despite the magnitude of these efforts, in a given experiment only a fraction of units are recorded, limiting the information available about the cortical representation. Moreover, only a finite number of stimuli can be shown to an animal over the course of a realistic experiment. These limitations raise the question of how and whether metrics that compare representations of deep networks are meaningful on these datasets. Here, we empirically quantify the capabilities and limitations of these metrics due to limited image presentations and neuron samples. We find that the comparison procedure is robust to different choices of stimuli set and the level of subsampling that one might expect in a large-scale brain survey with thousands of neurons. Using these results, we compare the representations measured in the Allen Brain Observatory in response to natural image presentations to deep neural network. We show that the visual cortical areas are relatively high order representations (in that they map to deeper layers of convolutional neural networks). Furthermore, we see evidence of a broad, more parallel organization rather than a sequential hierarchy, with the primary area VISp (V1) being lower order relative to the other areas.
A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex Vries, Saskia E. J., Lecoq, Jerome, Buice, Michael A., Groblewski, Peter A., Ocker, Gabriel K., Oliver, Michael, Feng, David, Cain, Nicholas, Ledochowitsch, Peter, Millman, Daniel, Roll, Kate, Garrett, Marina, Keenan, Tom, Kuan, Leonard, Mihalas, Stefan, Olsen, Shawn, Thompson, Carol, Wakeman, Wayne, Waters, Jack, Williams, Derric, Barber, Chris, Berbesque, Nathan, Blanchard, Brandon, Bowles, Nicholas, Caldejon, Shiella, Casal, Linzy, Cho, Andrew, Cross, Sissy, Dang, Chinh, Dolbeare, Tim, Edwards, Melise, Galbraith, John, Gaudreault, Nathalie, Griffin, Fiona, Hargrave, Perry, Howard, Robert, Huang, Lawrence, Jewell, Sean, Keller, Nika, Knoblich, Ulf, Larkin, Josh, Larsen, Rachael, Lau, Chris, Lee, Eric, Lee, Felix, Leon, Arielle, Li, Lu, Long, Fuhui, Luviano, Jennifer, Mace, Kyla, Nguyen, Thuyanh, Perkins, Jed, Robertson, Miranda, Seid, Sam, Shea-Brown, Eric, Shi, Jianghong, Sjoquist, Nathan, Slaughterbeck, Cliff, Sullivan, David, Valenza, Ryan, White, Casey, Williford, Ali, Witten, Daniela, Zhuang, Jun, Zeng, Hongkui, Farrell, Colin, Ng, Lydia, Bernard, Amy, Phillips, John W., Reid, R. Clay, and Koch, Christof bioRxiv 2018
To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.
Dynamical Decomposition of Markov Processes without Detailed Balance Ao, Ping, Chen, Tianqi, and Shi, Jianghong Chinese Physics Letters 2013
We introduce a dynamical decomposition view in dealing with Markov processes without detailed balance. This work generalizes a previous decomposition framework on continuous-state Markov processes and explicitly gives its correspondence in discrete-state case. We investigate the dynamical roles of decomposed parts by studying the evolution of relative-entropy-like functions. We find a special definition of relative entropy to unify the dynamical roles played by the detailed balance part and the breaking detailed balance part. The evolution of the relative entropy naturally bounds the convergence of the process.
Relation of a New Interpretation of Stochastic Differential Equations to Ito Process Shi, Jianghong, Chen, Tianqi, Yuan, Ruoshi, Yuan, Bo, and Ao, Ping Journal of Statistical Physics 2012
Stochastic differential equations (SDE) are widely used in modeling stochastic dynamics in literature. However, SDE alone is not enough to determine a unique process. A specified interpretation for stochastic integration is needed. Different interpretations specify different dynamics. Recently, a new interpretation of SDE is put forward by one of us. This interpretation has a built-in Boltzmann-Gibbs distribution and shows the existence of potential function for general processes, which reveals both local and global dynamics. Despite its powerful property, its relation with classical ones in arbitrary dimension remains obscure. In this paper, we will clarify such connection and derive the concise relation between the new interpretation and Ito process. We point out that the derived relation is experimentally testable.