Behavioral Neuroscience seminars

Behavioral time scale synaptic plasticity underlies CA1 place fields

Join Us for the Memory Decoding Journal Club! A collaboration of the Carboncopies Foundation and BPF Aspirational Neuroscience. This time, we’re diving into a groundbreaking paper: "Motor learning selectively strengthens cortical and striatal synapses of motor engram neurons

Why do individuals, both humans and animals, exhibit personal biases in two-alternative decision-making tasks, even when no clear reason exists to favor one alternative over another? In this talk, I will explore two competing hypotheses to explain these idiosyncratic biases. The first suggests that such tendencies arise from unique personal experiences, where past associations between actions and feedback influence future choices. The second hypothesis proposes that the bias reflects irreducible microscopic heterogeneities in the dynamics of decision-making networks. I will present experimental data and theoretical findings that support the latter hypothesis, shedding new light on the neural mechanisms behind seemingly irrational preferences. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-03-05. Recording duration: 00:50:36.

The dynamics of learning in natural and artificial environments is a problem of great interest to both neuroscientists and artificial intelligence experts. However, standard analyses of animal training data either treat behavior as fixed, or track only coarse performance statistics (e.g., accuracy and bias), providing limited insight into the dynamic evolution of behavioral strategies over the course of learning. To overcome these limitations, we propose a dynamic psychophysical model that efficiently tracks trial-to-trial changes in behavior over the course of training. In this talk, I will describe recent work based on a dynamic logistic regression model that captures the time-varying dependencies of behavior on stimuli and other task covariates, which we applied to mouse training data from the International Brain Lab (IBL). Secondly, I will discuss efforts to infer animal learning rules from time-varying behavior in order to characterize how they adjust their policy in response to reward. Finally, I will describe recent work on adaptive optimal training, which combines ideas from reinforcement learning and adaptive experimental design to formulate methods for inferring animal learning rules from behavior, and using these rules to speed up animal training. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-01-15. Recording duration: 00:49:39.

Keynote Address to British Association of Cognitive Neuroscience, London, 10th September 2024

2024 BACN Early-Career Prize Lecture Many of our decisions affect other people. Our choices can decelerate climate change, stop the spread of infectious diseases, and directly help or harm others. Prosocial behaviours – decisions that help others – could contribute to reducing the impact of these challenges, yet their computational and neural mechanisms remain poorly understood. I will present recent work that examines prosocial motivation, how willing we are to incur costs to help others, prosocial learning, how we learn from the outcomes of our choices when they affect other people, and prosocial preferences, our self-reports of helping others. Throughout the talk, I will outline the possible computational and neural bases of these behaviours, and how they may differ from young adulthood to old age.

Persistent, stimulus-dependent neuronal activity has been observed in numerous brain areas during tasks that require the temporary maintenance of information. Several competing hypotheses for the neuronal mechanisms underlying persistent activity have been proposed. We have employed data-driven models in conjunction with optogenetic disruptions of neural circuits within memory-guided motor tasks. Our findings revealed a mechanism governed by dynamic attractors, pivotal in sustaining neuronal activity. This mechanism, shaped by time-varying inputs reflecting temporal predictions, is instrumental in regulating the impact of sensory information on the premotor cortex, thereby preserving memory traces from distracting stimuli. We then asked how persistent activity driven by attractor dynamics emerges during motor learning. It has been proposed that activity-dependent synaptic plasticity underpins motor learning, as it can reconfigure network architectures to produce the appropriate neural dynamics for specific behaviors. To verify this hypothesis, we investigated how the mouse premotor cortex acquires specific neural dynamics that govern the planning of movement at different stages of motor learning. We developed network models that replicated the effects of acute manipulations of synaptic plasticity. The models, which display attractor dynamics, also explain flexible behavior after learning has ended. By leveraging the model's predictions, we can formulate testable hypotheses regarding the distinct mechanisms governing movement planning at various stages of the learning process. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2024-05-01. Recording duration: 00:39:52.

In theoretical neuroscience, the brain is usually described as an information processing system that encodes and manipulates representations of knowledge to produce plans of action. This view leads to a decomposition of brain functions into putative processes such as object recognition, working memory, decision-making, action planning, etc., inspiring the search for the neural correlates of these processes. However, neurophysiological data do not support many of the predictions of these classic subdivisions. Instead, there is divergence and broad distribution of functions that should be unified, mixed representations combining functions that should be distinct, and a general incompatibility with the conceptual subdivisions posited by theories of information processing. In this talk, I will explore the possibility of resynthesizing a different set of functional subdivisions, guided by the growing body of data on the evolutionary process that produced the human brain. I will summarize, in chronological order, a proposed sequence of innovations that appeared in nervous systems along the lineage that leads from the earliest multicellular animals to humans. Along the way, functional subdivisions and elaborations will be introduced in parallel with the neural specializations that made them possible, gradually building up an alternative conceptual taxonomy of brain functions. These functions emphasize mechanisms for real-time interaction with the world, rather than for building explicit knowledge of the world, and the relevant representations emphasize pragmatic outcomes rather than decoding accuracy, mixing variables in the way seen in real neural data. I suggest that this alternative taxonomy may better delineate the real functional pieces into which the brain is organized, and can offer a more natural mapping between behavior and neural mechanisms. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2024-03-13. Recording duration: 00:52:50.

Performing learned behaviors requires animals to produce precisely timed motor sequences. The underlying neuronal circuits must convert incoming spike trains into precisely timed firing to indicate the onset of crucial sensory cues or to carry out well-coordinated muscle movements. Birdsong is a remarkable example of a complex, learned and precisely timed natural behavior which is controlled by a brainstem-thalamocortical feedback loop. Projection neurons within the zebra finch cortical nucleus HVC (used as a proper name), produce precisely timed, highly reliable and ultra-sparse neural sequences that are thought to underlie song dynamics. However, the origin of short timescale dynamics of the song is debated. One model posits that these dynamics reside in HVC and are mediated through a synaptic chain mechanism. Alternatively, the upstream motor thalamic nucleus Uveaformis (Uva), could drive HVC bursts as part of a brainstem-thalamocortical distributed network. Using focal temperature manipulation we found that the song dynamics reside chiefly in HVC. We then characterized the activity of thalamic nucleus Uva, which provides input to HVC. We developed a lightweight (~1 g) microdrive for juxtacellular recordings and with it performed the very first extracellular single unit recordings in Uva during song. Recordings revealed HVC-projecting Uva neurons contain timing information during the song, but compared to HVC neurons, fire densely in time and are much less reliable. Computational models of Uva-driven HVC neurons estimated that a high degree of synaptic convergence is needed from Uva to HVC to overcome the inconsistency of Uva firing patterns. However, axon terminals of single Uva neurons exhibit low convergence within HVC such that each HVC neuron receives input from 2-7 Uva neurons. These results suggest that thalamus maintains sequential cortical activity during song but does not provide unambiguous timing information. Our observations are consistent with a model in which the brainstem-thalamocortical feedback loop acts at the syllable timescale (~100 ms) and does not support a model in which the brainstem-thalamocortical feedback loop acts at fast timescale (~10 ms) to generate sequences within cortex. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2024-02-14. Recording duration: 00:39:45.

As we interact with the world around us, we experience a constant stream of sensory inputs, and must generate a constant stream of behavioral actions. What makes brains more than simple input-output machines is their capacity to integrate sensory inputs with an animal’s own internal motivational state to produce behavior that is flexible and adaptive. Working with neural recordings from subcortical structures involved in regulation of survival behaviors, we show how the dynamical properties of neural populations give rise to motivational states that change animal behavior on a timescale of minutes. We also show how neuromodulation can alter these dynamics to change behavior on timescales of hours to days. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2024-01-17. Recording duration: 00:42:58.

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Psychology and neuroscience are increasingly looking to fine-grained analyses of decision-making behaviour, seeking to characterise not just the variation between subjects but also a subject's variability across time. When analysing the behaviour of each subject in a choice task, we ideally want to know not only when the subject has learnt the correct choice rule but also what the subject tried while learning. I introduce a simple but effective Bayesian approach to inferring the probability of different choice strategies at trial resolution. This can be used both for inferring when subjects learn, by tracking the probability of the strategy matching the target rule, and for inferring subjects use of exploratory strategies during learning. Applied to data from rodent and human decision tasks, we find learning occurs earlier and more often than estimated using classical approaches. Around both learning and changes in the rewarded rules the exploratory strategies of win-stay and lose-shift, often considered complementary, are consistently used independently. Indeed, we find the use of lose-shift is strong evidence that animals have latently learnt the salient features of a new rewarded rule. Our approach can be extended to any discrete choice strategy, and its low computational cost is ideally suited for real-time analysis and closed-loop control.

Note the later time!

To understand subjective evaluation of an option, various disciplines have quantified the interaction between reward and effort during decision making, producing an estimate of economic utility, namely the subject ‘goodness’ of an option. However, those same variables that affect the utility of an option also influence the vigor (speed) of movements to acquire it. To better understand this, we have developed a mathematical framework demonstrating how utility can influence not only the choice of what to do, but also the speed of the movement follows. I will present results demonstrating that expectation of reward increases speed of saccadic eye and reaching movements, whereas expectation of effort expenditure decreases this speed. Intriguingly, when deliberating between two visual options, saccade vigor to each option increases differentially, encoding their relative value. These results and others imply that vigor may serve as a new, real-time metric with which to quantify subjective utility, and that the control of movements may be an implicit reflection of the brain’s economic evaluation of the expected outcome. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2023-10-18. Recording duration: 00:45:32.