Biology subjects

Latimer, K. W.

Publications and source records attributed to Latimer, K. W..

6 recordsLinked to original sources

bioRxiv2023

Experience shapes initial exploration for non-generalizable spatial learning

Experience can change how individuals learn. Learning to solve a new problem can be accelerated by generalizing known rules in the new context, but the impact of experience on solving problems where generalization cannot be applied remains unclear. To study the impact of experience on solving new problems that are distinct from previously learned tasks, we examined how rats learned a new spatial navigation task after having previously learned different sets of spatial navigation tasks. The new task differed from the previous tasks in spatial layout and navigation rule, and could not be solved by applying previously learned rules. We found that different experience histories did not impact task performance in the new task. However, by examining navigation choices made by rats, we found exploration patterns during the early stage of learning in the new task was dependent on experience history. We identified these behavioral differences by analyzing each rats navigation choices and by modeling their choice sequences with a modified distance dependent Chinese restaurant process. We further pinpointed the behavioral difference to sequential turn/no turn decisions made at choice points. Our results indicate that experience can influence problem-solving strategies when learning to solve new problems. Individuals with distinct experience histories can approach new problems from different starting points but converge on the same solution.

neuroscience↗

bioRxiv2021

Low-dimensional encoding of decisions in parietal cortex reflects long-term training history

Neurons in parietal cortex exhibit task-related activity during decision-making tasks. However, it remains unclear how long-term training to perform different tasks over months or even years shapes neural computations and representations. We examine lateral intraparietal area (LIP) responses during a visual motion delayed-match-to-category (DMC) task. We consider two pairs of monkeys with different training histories: one trained only on the DMC task, and another first trained to perform fine motion-direction discrimination. We introduce generalized multilinear models to quantify low-dimensional, task-relevant components in population activity. During the DMC task, we found stronger cosine-like motion-direction tuning in the pretrained monkeys than in the DMC-only monkeys, and that the pretrained monkeys performance depended more heavily on sample-test stimulus similarity. These results suggest that sensory representations in LIP depend on the sequence of tasks that the animals have learned, underscoring the importance of training history in studies with complex behavioral tasks.

neuroscience↗

bioRxiv2021

Dopamine Neurons Evaluate Natural Fluctuations in Performance Quality

Many motor skills are learned by comparing ongoing behavior to internal performance benchmarks. Dopamine neurons encode performance error in behavioral paradigms where error is externally induced, but it remains unknown if dopamine also signals the quality of natural performance fluctuations. Here we recorded dopamine neurons in singing birds and examined how spontaneous dopamine spiking activity correlated with natural fluctuations in ongoing song. Antidromically identified basal ganglia-projecting dopamine neurons correlated with recent, and not future, song variations, consistent with a role in evaluation, not production. Furthermore, dopamine spiking was suppressed following the production of outlying vocal variations, consistent with a role for active song maintenance. These data show for the first time that spontaneous dopamine spiking can evaluate natural behavioral fluctuations unperturbed by experimental events such as cues or rewards.

neuroscience↗

bioRxiv2019

Capturing multiple timescales of adaptation to second-order statistics with generalized linear models: gain scaling and fractional differentiation

Single neurons can dynamically change the gain of their spiking responses to account for shifts in stimulus variance. Moreover, gain adaptation can occur across multiple timescales. Here, we examine the ability of a simple statistical model of spike trains, the generalized linear model (GLM), to account for these adaptive effects. The GLM describes spiking as a Poisson process whose rate depends on a linear combination of the stimulus and recent spike history. The GLM successfully replicates gain scaling observed in Hodgkin-Huxley simulations of cortical neurons that occurs when the ratio of spike-generating potassium and sodium conductances approaches one. Gain scaling in the GLM depends on the length and shape of the spike history filter. Additionally, the GLM captures adaptation that occurs over multiple timescales as a fractional derivative of the stimulus variance, which has been observed in neurons that include long timescale after hyperpolarization conductances. Fractional differentiation in GLMs requires long spike history that span several seconds. Together, these results demonstrate that the GLM provides a tractable statistical approach for examining single-neuron adaptive computations in response to changes in stimulus variance.

neuroscience↗

bioRxiv2018

Discrete stepping and nonlinear ramping dynamics underlie spiking responses of LIP neurons during decision-making

Neurons in macaque area LIP exhibit gradual ramping in their trial-averaged spike responses during sensory decision-making. However, recent work has sparked debate over whether single-trial LIP spike trains are better described by discrete \"stepping\" or continuous drift-diffusion (\"ramping\") dynamics. Here we address this controversy using powerful model-based analyses of LIP spike responses. We extended latent dynamical models of LIP spike trains to incorporate non-Poisson spiking, baseline firing rates, and various nonlinear relationships between the latent variable and firing rate. Moreover, we used advanced model-comparison methods, including cross-validation and a fully Bayesian information criterion, to evaluate and compare models. These analyses revealed that when non-Poisson spiking was incorporated into existing stepping and ramping models, a majority of neurons remained better described by stepping dynamics, even when conditioning on evidence level or choice. However, an extended ramping model with a non-zero baseline and a compressive output nonlinearity accounted for roughly as many neurons as the stepping model. The latent dynamics inferred under this model exhibited high diffusion variance for many neurons, making them qualitatively different than slowly-evolving continuous dynamics. These findings generalized to alternative tasks, suggesting that a robust fraction of LIP neurons are better described by each model class.

neuroscience↗

bioRxiv2017

No cause for pause: new analyses of ramping and stepping dynamics in LIP (Rebuttal to Response to Reply to Comment on Latimer et al. 2015)

We recently presented a statistical comparison between two models of latent dynamics in macaque lateral intraparietal (LIP) area spike trains--a continuous ramping (diffusion-to-bound) model, and a discrete stepping model--and found that a substantial fraction of neurons (recorded in two different studies) were better supported by the stepping model (Latimer et al., 2015). Here, we respond to a recent challenge to the validity of these findings that focuses primarily on the possibility of a lower bound on LIP firing rates (Zylberberg & Shadlen, 2016). The paper in question proposed alternate formulations of the ramping model, and argued (via indirect analyses) that half the neurons in the population were better explained by the new model; if correct, this would lead to an even split in the number of neurons better explained by each model. These analyses, while interesting, do not alter the conclusions of our original paper. Here, we review the criticisms raised by Zylberberg & Shadlen and report several new analyses using models with lower bounds. First, we show that the stepping model continued to provide a better description of LIP spike trains when fit using only an early period of each trial. Second, we performed a direct model comparison between our stepping model and a ramping-with-baseline model proposed by Zylberberg & Shadlen; we found that (in a pleasing moment of agreement) roughly half the neurons were better explained by each model. Interestingly, inspection of the cells that switched classifications revealed that many did not strictly exhibit the classical ramping PSTHs that motivated these analyses in the first place. We also examined two other issues raised in recent discussions of LIP: (1) We show that a non-integrating model is consistent with some core aspects of behavioral data previously offered as evidence for continuous integration; and (2) We examine analyses based on the response covariance (\"CorCE\"), and show that it does not reliably distinguish ramping and stepping dynamics for our dataset. Taken together, these discussions highlight the value of data-driven characterizations of both neural and behavioral dynamics with appropriate statistical tools.

neuroscience↗