Biology subjects

Dunovan, K.

Publications and source records attributed to Dunovan, K..

5 recordsLinked to original sources

bioRxiv2020

Effects of beta- and gamma-band rhythmic stimulation on motor inhibition

Voluntary movements are accompanied by an increase in gamma-band oscillatory activity (60-100Hz) and a strong desynchronization of beta-band activity (13-30Hz) in the motor system at both the cortical and subcortical level. Conversely, successful motor inhibition is associated with increased beta power in a fronto-basal-ganglia network. Intriguingly, gamma activity also increases in response to a stop-signal. In this study, we used transcranial alternating current stimulation to drive beta and gamma oscillations to investigate whether these frequencies are causally related to motor inhibition. We found that 20Hz stimulation targeted at the pre-supplementary motor area enhanced inhibition and increased beta oscillatory activity around the time of the stop-signal in trials directly following stimulation. In contrast, 70Hz stimulation seemed to slow down the braking process, and predominantly affected go task performance. These results demonstrate that the effects of tACS are state-dependent and that especially fronto-central beta activity is a functional marker for successful motor inhibition.

neuroscience↗

bioRxiv2020

Dynamic decision policy reconfiguration under outcome uncertainty

In uncertain or unstable environments, sometimes the best decision is to change your mind. To shed light on this flexibility, we evaluated how the underlying decision policy adapts when the most rewarding action changes. Human participants performed a dynamic two-armed bandit task that manipulated the certainty in relative reward (conflict) and the reliability of action-outcomes (volatility). Continuous estimates of conflict and volatility contributed to shifts in exploratory states by changing both the rate of evidence accumulation (drift rate) and the amount of evidence needed to make a decision (boundary height), respectively. At the trialwise level, following a switch in the optimal choice, the drift rate plummets and the boundary height weakly spikes, leading to a slow exploratory state. We find that the drift rate drives most of this response, with an unreliable contribution of boundary height across experiments. Surprisingly, we find no evidence that pupillary responses associated with decision policy changes. We conclude that humans show a stereotypical shift in their decision policies in response to environmental changes.

neuroscience↗

bioRxiv2019

Corticostriatal synaptic weight evolution in a two-alternative forced choice task

In natural environments, mammals can efficiently select actions based on noisy sensory signals and quickly adapt to unexpected outcomes to better exploit opportunities that arise in the future. Such feedback-based changes in behavior rely on long term plasticity within cortico-basal-ganglia-thalamic networks, driven by dopaminergic modulation of cortical inputs to the direct and indirect pathway neurons of the striatum. While the firing rates of corticostriatal neurons have been shown to adapt across a range of feedback conditions, it remains difficult to directly assess the corticostriatal synaptic weight changes that contribute to these adaptive firing rates. In this work, we simulate a computational model for the evolution of corticostriatal synaptic weights based on a spike timing-dependent plasticity rule driven by dopamine signaling that is induced by outcomes of actions in the context of a two-alternative forced choice task. Results show that plasticity predominantly impacts direct pathway weights, which evolve to drive action selection toward a more-rewarded action in settings with deterministic reward outcomes. After the model is tuned based on such fixed reward scenarios, its performance agrees with the results of behavioral experiments carried out with probabilistic reward paradigms.

animal behavior and cognition↗

bioRxiv2018

Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making

Cortico-basal-ganglia-thalamic (CBGT) networks are critical for adaptive decision-making, yet how changes to circuit-level properties impact cognitive algorithms remains unclear. Here we explore how dopaminergic plasticity at corticostriatal synapses alters competition between striatal pathways, impacting the evidence accumulation process during decision-making. Spike-timing dependent plasticity simulations showed that dopaminergic feedback based on rewards modified the ratio of direct and indirect corticostriatal weights within opposing action channels. Using the learned weight ratios in a full spiking CBGT network model, we simulated neural dynamics and decision outcomes in a reward-driven decision task and fit them with a drift diffusion model. Fits revealed that the rate of evidence accumulation varied with inter-channel differences in direct pathway activity while boundary height varied with overall indirect pathway activity. This multi-level modeling approach demonstrates how complementary learning and decision computations can emerge from corticostriatal plasticity. Author summaryCognitive process models such as reinforcement learning (RL) and the drift diffusion model (DDM) have helped to elucidate the basic algorithms underlying error-corrective learning and the evaluation of accumulating decision evidence leading up to a choice. While these relatively abstract models help to guide experimental and theoretical probes into associated phenomena, they remain uninformative about the actual physical mechanics by which learning and decision algorithms are carried out in a neurobiological substrate during adaptive choice behavior. Here we present an "upwards mapping" approach to bridging neural and cognitive models of value-based decision-making, showing how dopaminergic feedback alters the network-level dynamics of cortico-basal-ganglia-thalamic (CBGT) pathways during learning to bias behavioral choice towards more rewarding actions. By mapping "up" the levels of analysis, this approach yields specific predictions about aspects of neuronal activity that map to the quantities appearing in the cognitive decision-making framework.

neuroscience↗

bioRxiv2017

Dual learning mechanisms drive adaptive inhibitory control

Goal-directed behavior requires integrating action selection processes with learning systems that adapt control using environmental feedback. These functions intersect in the basal ganglia (BG), which has at least two targets of plasticity: a dopaminergic modulation of striatal pathways and cortical modulation of the subthalamic nucleus (STN). Dual learning mechanisms suggests that feedback signals have a multifaceted impact on BG-dependent decisions. Using a hybrid of accumulation-to-bound decision models and reinforcement learning, we modeled the performance of humans in a stop-signal task where participants (N=75) learned the prior distribution of the timing of a stop signal through trial-and-error feedback. Changes in the drift-rate of the action execution process were driven by errors in action timing, whereas adaptation in the boundary height served to increase caution following failed stops. These findings highlight two interactive learning mechanisms for adapting the control of goal-directed actions based on dissociable dimensions of feedback error.\n\nAuthor SummaryMany complex behavioral goals rely on ones ability to regulate the timing of action execution while also maintaining enough control to cancel actions in response to \"Stop\" cues in the environment. Here we examined how these two fundamental components of behavior become tuned to the control demands of the environment by combining principles of reinforcement learning with accumulator models of decision making. The synthesis of these two theoretical frameworks is motivated by previous work showing that reinforcement learning and control rely on overlapping circuitry in the basal ganglia. Leveraging knowledge about the interaction of learning and control signals in this network, we formulated a computational model in which performance feedback is used to modulate key mechanisms of the decision process to facilitate goal acquisition. Model-based analysis of behavioral data collected on an adaptive stop-signal task revealed two critical learning mechanisms: one that adjusts the accumulation rate of the \"Go\" signal to errors in action timing and another that exercises caution by raising the height of the execution boundary after a failed Stop trial. We show how these independent learning mechanisms interact over the course of learning, shedding light on the behavioral effects plasticity in different pathways of the basal ganglia.

neuroscience↗