Trial-to-trial variability

A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system. Models of these neural dynamics should ideally be both interpretable and fit the observed data well. Low-rank recurrent neural networks (RNNs) exhibit such interpretability by having tractable dynamics. However, it is unclear how to best fit low-rank RNNs to data consisting of noisy observations of an underlying stochastic system. Here, we propose to fit stochastic low-rank RNNs with variational sequential Monte Carlo methods. We validate our method on severa

Cortical neurons exhibit a high degree of trial-to-trial variability in response to repeated presentations of the same stimulus. We propose that this variability serves a useful computational purpose: allowing the brain to generalize from a small number of examples. To formalize this theory, we consider a Gaussian model of few-shot classification, wherein we observe a few samples of each class, and each observation has trial-to-trial variability. Our model leads to three predictions about the optimal neural response variability that minimizes generalization error: (i) the optimal variability m

A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system. Models of these neural dynamics should ideally be both interpretable and fit the observed data well. Low-rank recurrent neural networks (RNNs) exhibit such interpretability by having tractable dynamics. However, it is unclear how to best fit low-rank RNNs to data consisting of noisy observations of an underlying stochastic system. Here, we propose to fit stochastic low-rank RNNs with variational sequential Monte Carlo methods. We validate our method on severa

…of the same stimuli, but whether this trial-to-trial variability represents noise versus unidentified signals remains unresolved (1–3). Some of the variability can be attributed to correlations of neural activity with locomotion, arousal, and other overt movements (4–7). We hypothesized that distributed brain states, i.e., patterns of neural activity observable in other brain regions, may account for more of the unexplained trial-to-trial variability in responses of sensory cortical neurons. To test this, we used Neuropixels 2.0 probe

Recent studies have proposed that the activity of both recurrent and biological neural networks is constrained to a task manifold. This manifold is often intrinsically lower dimensional than the full neural state space, and its geometry is directly related to task computations. In parallel, there has been a surge of interest in training RNNs to solve behavioural tasks in order to generate testable hypotheses regarding the computations employed by neural circuits. However, means of using task-trained RNNs to test hypotheses regarding the geometry of neural data manifolds are currently lacking.

In vivo, single neurons have high trial-to-trial variability in their information transmission, which presents a challenge for networks whose outputs must be consistent for high task performance. Reliability becomes increasingly difficult to attain for working memory tasks where information must be preserved for extended periods of time. This implies that relative to systems without noise, the brain must have mechanisms dedicated to compensating for unreliability in neuron activity. How trained networks mitigate the effects of noise while completing working memory tasks remains largely unexplo

Despite the consistency of symptoms at the cognitive level, we now know that brain disorders like Autism and Schizophrenia can each arise from mutations in >100 different genes. Presumably there is a convergence of “symptoms” at the level of neural circuits in diagnosed individuals. In this talk I will argue that redundancy in neural circuit parameters implies that we should take a circuit-function rather that circuit-component approach to understanding these disorders. Then I will present our recent empirical work testing a circuit-function theory for Autism: the idea that neural circuits sho

…of visual features due to their high trial-to-trial variability. It is not known if this “noise” extends its effects over large neural populations to impair the global encoding of stimuli. We recorded simultaneously from ∼20,000 neurons in mouse primary visual cortex (V1) and found that the neural populations had discrimination thresholds of ∼0.34° in an orientation decoding task. These thresholds were nearly 100 times smaller than those reported behaviourally in mice. The discrepancy between neural and behavioural discrimination