Biology subjects

Coddington, L. T.

Publications and source records attributed to Coddington, L. T..

4 recordsLinked to original sources

bioRxiv2026

Sex-specific plasticity mechanisms mediating fear extinction

Strong evidence suggests synaptic plasticity is the critical cellular mechanism underlying learning and memory. Although the forms of synaptic plasticity used by different circuits vary, a widespread presumption is that the male and female brain has evolved to use the same form of plasticity within the same circuits during learning. We used complimentary approaches to determine how synaptic plasticity within the mouse frontal cortex supports extinction of associative memories. Here, we show that both male and female mice have similar ensemble dynamics in excitatory infralimbic cortical neurons during learning. However, activity in amygdala-projecting neurons was required for extinction memories only in male mice. Likewise, only male mice showed evidence for structural synaptic remodeling and clustering of dendritic spines on infralimbic-amygdala projection neurons. Projection-specific deletion of the glutamate receptor subunit GRIN2B blocked synaptic plasticity and impaired extinction memory in male but not female mice. These distinct mechanisms could be leveraged for precise therapies for mental health conditions relative to the present one-size-fits-all approach.

neuroscience↗

bioRxiv2025

Composing trajectories for rapid inference of navigational goals

Animals efficiently learn to navigate their environment. In the laboratory, naive mice explore their environment via highly structured trajectories and can learn to localize new spatial targets in as few as a handful of trials. It is unclear how such efficient learning is possible, since existing computational models of spatial navigation require far more experience to achieve comparable performance and do not attempt to explain the evolving structure of animal behavior during learning. To inform a new algorithm for rapid learning of navigational goals, we took inspiration from the reliable structure of behavior as mice learned to intercept hidden spatial targets. We designed agents that generate behavioral trajectories by controlling the speed and angular velocity of smooth path segments between anchor points. To rapidly learn good anchors, we use Bayesian inference on the history of rewarded and unrewarded trajectories to infer the probability that an anchor will be successful, and active sampling to trim hypothesized anchors. Agents learn within tens of trials to generate compact trajectories that intercept a target, capturing the evolution of behavioral structure and matching the upper limits of learning efficiency observed in mice. We further show that this algorithm can explain how mice avoid obstacles and rapidly adapt to target switches. Finally, we show that this framework naturally encompasses both egocentric and allocentric strategies for navigation.

animal behavior and cognition↗

bioRxiv2021

Mesolimbic dopamine adapts the rate of learning from errors in performance

Recent success in training artificial agents and robots derives from a combination of direct learning of behavioral policies and indirect learning via value functions. Policy learning and value learning employ distinct algorithms that optimize behavioral performance and reward prediction, respectively. In animals, behavioral learning and the role of mesolimbic dopamine signaling have been extensively evaluated with respect to reward prediction; however, to date there has been little consideration of how direct policy learning might inform our understanding. Here we used a comprehensive dataset of orofacial and body movements to understand how behavioral policies evolve as naive, head-restrained mice learned a trace conditioning paradigm. Individual differences in initial dopaminergic reward responses correlated with the emergence of learned behavioral policy, but not the emergence of putative value encoding for a predictive cue. Likewise, physiologically-calibrated manipulations of mesolimbic dopamine produced multiple effects inconsistent with value learning but predicted by a neural network-based model that used dopamine signals to set an adaptive rate, not an error signal, for behavioral policy learning. This work provides strong evidence that phasic dopamine activity can regulate direct learning of behavioral policies, expanding the explanatory power of reinforcement learning models for animal learning.

neuroscience↗

bioRxiv2017

Emergence of reward expectation signals in identified dopamine neurons

Coherent control of purposive actions emerges from the coordination of multiple brain circuits during learning. Dissociable brain circuits and cell-types are thought to preferentially participate in distinct learning mechanisms. For example, the activity of midbrain dopamine (mDA) neurons is proposed to primarily, or even exclusively, reflect reward prediction error signals in well-trained animals. To study the specific contribution of individual circuits requires observing changes before tight functional coordination is achieved. However, little is known about the detailed timing of the emergence of reward-related representations in dopaminergic neurons. Here we recorded activity of identified dopaminergic neurons as naive mice learned a novel stimulus-reward association. We found that at early stages of learning mDA neuron activity reflected both external (sensory) and internal (action initiation) causes of reward expectation. The increasingly precise correlation of action initiation with sensory stimuli rather than an evaluation of outcomes governed mDA neuron activity. Thus, our data demonstrate that mDA neuron activity early in learning does not reflect errors, but is more akin to a Hebbian learning signal - providing new insight into a critical computation in a highly conserved, essential learning circuit.

neuroscience↗