Biomedical subjects

Anne C Smith

Publications and source records attributed to Anne C Smith.

7 recordsLinked to original sources

PubMed2006

Bayesian analysis of interleaved learning and response bias in behavioral experiments.

Accurate characterizations of behavior during learning experiments are essential for understanding the neural bases of learning. Whereas learning experiments often give subjects multiple tasks to learn simultaneously, most analyze subject performance separately on each individual task. This analysis strategy ignores the true interleaved presentation order of the tasks and cannot distinguish learning behavior from response preferences that may represent a subject's biases or strategies. We present a Bayesian analysis of a state-space model for characterizing simultaneous learning of multiple tasks and for assessing behavioral biases in learning experiments with interleaved task presentations. Under the Bayesian analysis the posterior probability densities of the model parameters and the learning state are computed using Monte Carlo Markov Chain methods. Measures of learning, including the learning curve, the ideal observer curve, and the learning trial translate directly from our previous likelihood-based state-space model analyses. We compare the Bayesian and current likelihood-based approaches in the analysis of a simulated conditioned T-maze task and of an actual object-place association task. Modeling the interleaved learning feature of the experiments along with the animal's response sequences allows us to disambiguate actual learning from response biases. The implementation of the Bayesian analysis using the WinBUGS software provides an efficient way to test different models without developing a new algorithm for each model. The new state-space model and the Bayesian estimation procedure suggest an improved, computationally efficient approach for accurately characterizing learning in behavioral experiments.

Bayes Theorem↗

PubMed2006

A set probability technique for detecting relative time order across multiple neurons.

With the development of multielectrode recording techniques, it is possible to measure the cell firing patterns of multiple neurons simultaneously, generating a large quantity of data. Identification of the firing patterns within these large groups of cells is an important and a challenging problem in data analysis. Here, we consider the problem of measuring the significance of a repeat in the cell firing sequence across arbitrary numbers of cells. In particular, we consider the question, given a ranked order of cells numbered 1 to N, what is the probability that another sequence of length n contains j consecutive increasing elements? Assuming each element of the sequence is drawn with replacement from the numbers 1 through N, we derive a recursive formula for the probability of the sequence of length j or more. For n<2j, a closed-form solution is derived. For n>or=2j, we obtain upper and lower bounds for these probabilities for various combinations of parameter values. These can be computed very quickly. For a typical case with small N (<10) and large n (<3000), sequences of 7 and 8 are statistically very unlikely. A potential application of this technique is in the detection of repeats in hippocampal place cell order during sleep. Unlike most previous articles on increasing runs in random lists, we use a probability approach based on sets of overlapping sequences.

Action Potentials↗

PubMed2005

Functional magnetic resonance imaging activity during the gradual acquisition and expression of paired-associate memory.

Recent neurophysiological findings from the monkey hippocampus showed dramatic changes in the firing rate of individual hippocampal cells as a function of learning new associations. To extend these findings to humans, we used blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to examine the patterns of brain activity during learning of an analogous associative task. We observed bilateral, monotonic increases in activity during learning not only in the hippocampus but also in the parahippocampal and right perirhinal cortices. In addition, activity related to simple novelty signals was observed throughout the medial temporal lobe (MTL) memory system and in several frontal regions. A contrasting pattern was observed in a frontoparietal network in which a high level of activity was sustained until the association was well learned, at which point the activity decreased to baseline. Thus, we found that associative learning in humans is accompanied by striking increases in BOLD fMRI activity throughout the MTL as well as in the cingulate cortex and frontal lobe, consistent with neurophysiological findings in the monkey hippocampus. The finding that both the hippocampus and surrounding MTL cortex exhibited similar associative learning and novelty signals argues strongly against the view that there is a clear division of labor in the MTL in which the hippocampus is essential for forming associations and the cortex is involved in novelty detection. A second experiment addressed a striking aspect of the data from the first experiment by demonstrating a substantial effect of baseline task difficulty on MTL activity capable of rendering mnemonic activity as either "positive" or "negative."

Adult↗

PubMed2004

Analysis and design of behavioral experiments to characterize population learning.

In population learning studies, between-subject response differences are an important source of variance that must be characterized to identify accurately the features of the learning process common to the population. Although learning is a dynamic process, current population analyses do not use dynamic estimation methods, do not compute both population and individual learning curves, and use learning criteria that are less than optimal. We develop a state-space random effects (SSRE) model to estimate population and individual learning curves, ideal observer curves, and learning trials, and to make dynamic assessments of learning between two populations and within the same population that avoid multiple hypothesis tests. In an 80-trial study of an NMDA antagonist's effect on the ability of rats to execute a set-shift task, our dynamic assessments of learning demonstrated that both the treatment and control groups learned, yet, by trial 35, the treatment group learning was significantly impaired relative to control. We used our SSRE model in a theoretical study to evaluate the design efficiency of learning experiments in terms of the number of animals per group and number of trials per animal required to characterize learning differences between two populations. Our results demonstrated that a maximum difference in the probability of a correct response between the treatment and control group learning curves of 0.07 (0.20) would require 15 to 20 (5 to 7) animals per group in an 80 (60)-trial experiment. The SSRE model offers a practical approach to dynamic analysis of population learning and a theoretical framework for optimal design of learning experiments.

Algorithms↗

PubMed2004

Dynamic analysis of learning in behavioral experiments.

Understanding how an animal's ability to learn relates to neural activity or is altered by lesions, different attentional states, pharmacological interventions, or genetic manipulations are central questions in neuroscience. Although learning is a dynamic process, current analyses do not use dynamic estimation methods, require many trials across many animals to establish the occurrence of learning, and provide no consensus as how best to identify when learning has occurred. We develop a state-space model paradigm to characterize learning as the probability of a correct response as a function of trial number (learning curve). We compute the learning curve and its confidence intervals using a state-space smoothing algorithm and define the learning trial as the first trial on which there is reasonable certainty (>0.95) that a subject performs better than chance for the balance of the experiment. For a range of simulated learning experiments, the smoothing algorithm estimated learning curves with smaller mean integrated squared error and identified the learning trials with greater reliability than commonly used methods. The smoothing algorithm tracked easily the rapid learning of a monkey during a single session of an association learning experiment and identified learning 2 to 4 d earlier than accepted criteria for a rat in a 47 d procedural learning experiment. Our state-space paradigm estimates learning curves for single animals, gives a precise definition of learning, and suggests a coherent statistical framework for the design and analysis of learning experiments that could reduce the number of animals and trials per animal that these studies require.

Algorithms↗

PubMed2003

Single neurons in the monkey hippocampus and learning of new associations.

The medial temporal lobe is crucial for the ability to learn and retain new declarative memories. This form of memory includes the ability to quickly establish novel associations between unrelated items. To better understand the patterns of neural activity during associative memory formation, we recorded the activity of hippocampal neurons of macaque monkeys as they learned new associations. Hippocampal neurons signaled learning by changing their stimulus-selective response properties. This change in the pattern of selective neural activity occurred before, at the same time as, or after learning, which suggests that these neurons are involved in the initial formation of new associative memories.

Action Potentials↗

PubMed2003

Estimating a state-space model from point process observations.

A widely used signal processing paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, we develop an algorithm to estimate a state-space model observed through point process measurements. We represent the latent process modulating the neural spiking activity as a gaussian autoregressive model driven by an external stimulus. Given the latent process, neural spiking activity is characterized as a general point process defined by its conditional intensity function. We develop an approximate expectation-maximization (EM) algorithm to estimate the unobservable state-space process, its parameters, and the parameters of the point process. The EM algorithm combines a point process recursive nonlinear filter algorithm, the fixed interval smoothing algorithm, and the state-space covariance algorithm to compute the complete data log likelihood efficiently. We use a Kolmogorov-Smirnov test based on the time-rescaling theorem to evaluate agreement between the model and point process data. We illustrate the model with two simulated data examples: an ensemble of Poisson neurons driven by a common stimulus and a single neuron whose conditional intensity function is approximated as a local Bernoulli process.

Action Potentials↗