Biology subjects

Golub, M. D.

Publications and source records attributed to Golub, M. D..

5 recordsLinked to original sources

bioRxiv2026

Measuring neuronal input-output operations in vivo with random access projection microscopy

Neurons transform thousands of synaptic inputs into spiking output, and understanding this transformation requires measuring many submicron synapses at millisecond timescales in vivo. Here we present SLAP2, a two-photon random access projection microscope with no per-target access time cost, yielding over 5-fold faster imaging than existing methods, and performant real-time hardware motion correction. Flexible scanning modes enable measurement of hundreds of synapses at ~200 Hz, >800 synapses volumetrically at 50-100 Hz, and compartment-resolved dendritic voltage at 2-10 kHz in behaving mice. Simultaneous dendritic glutamate and somatic calcium imaging in visual cortex pyramidal neurons revealed nonlinear input-output amplification of stimulus onset and preferred stimulus orientation responses. Amplified features are associated with spatially organized inputs and stronger trial-to-trial coupling between synaptic activity and somatic output. These measurements establish SLAP2 as a powerful tool for recording intricate patterns of subcellular fluorescence dynamics in behaving animals.

neuroscience↗

bioRxiv2024

A theory of brain-computer interface learning via low-dimensional control

A remarkable demonstration of the flexibility of mammalian motor systems is primates ability to learn to control brain-computer interfaces (BCIs). This constitutes a completely novel motor behavior, yet primates are capable of learning to control BCIs under a wide range of conditions. BCIs with carefully calibrated decoders, for example, can be learned with only minutes to hours of practice. With a few weeks of practice, even BCIs with randomly constructed decoders can be learned. What are the biological substrates of this learning process? Here, we develop a theory based on a re-aiming strategy, whereby learning operates within a low-dimensional subspace of task-relevant inputs driving the local population of recorded neurons. Through comprehensive numerical and formal analysis, we demonstrate that this theory can provide a unifying explanation for disparate phenomena previously reported in three different BCI learning tasks, and we derive a novel experimental prediction that we verify with previously published data. By explicitly modeling the underlying neural circuitry, the theory reveals an interpretation of these phenomena in terms of biological constraints on neural activity.

neuroscience↗

bioRxiv2022

Learning alters neural activity to simultaneously support memory and action

How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map, in a way that was specific to the learning experience. That is, learning left a "memory trace." This memory trace co-existed with proficient performance under the familiar map, primarily by altering dimensions of neural activity that did not impact behavior. Such a memory trace could provide the neural underpinning for the joint learning of multiple motor behaviors without interference.

neuroscience↗

bioRxiv2020

Learning is shaped by abrupt changes in neural engagement

Internal states such as arousal, attention, and motivation are known to modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain must modify the neural activity it produces to improve behavioral performance. How do internal states affect the evolution of this learning process? Using a brain-computer interface (BCI) learning paradigm in non-human primates, we identified large fluctuations in neural population activity in motor cortex (M1) indicative of arousal-like internal state changes. These fluctuations drove population activity along dimensions we term neural engagement axes. Neural engagement increased abruptly at the start of learning, and then gradually retreated. In a BCI, the causal relationship between neural activity and behavior is known. This allowed us to understand how these changes impacted behavioral performance for different task goals. We found that neural engagement interacted with learning, helping to explain why animals learned some task goals more quickly than others.

neuroscience↗

bioRxiv2020

Skill-specific changes in cortical preparatory activity during motor learning

Animals have a remarkable capacity to learn new motor skills, but it remains an open question as to how learning changes neural population dynamics underlying movement1. Specifically, we asked whether changes in neural population dynamics relate purely to newly learned movements or if additional patterns are generated that facilitate learning without matching motor output. We trained rhesus monkeys to learn a curl force field2 task that elicited new arm-movement kinetics for some but not all reach directions3,4. We found that along certain neural dimensions, preparatory activity in motor cortex reassociated existing activity patterns with new movements. These systematic changes were observed only for learning-altered reaches. Surprisingly, we also found prominent shifts of preparatory activity along a nearly orthogonal neural dimension. These changes in preparatory activity were observed uniformly for all reaches including those unaltered by learning. This uniform shift during learning implies formation of new neural activity patterns, which was not observed in other short-term learning contexts5-8. During a washout period when the curl field was removed, movement kinetics gradually reverted, but the learning-induced uniform shift of preparatory activity persisted and a second, orthogonal uniform shift occurred. This persistent shift may retain a motor memory of the learned field9-11, consistent with faster relearning of the same curl field observed behaviorally and neurally. When multiple different curl fields were learned sequentially, we found distinct uniform shifts, each reflecting the identity of the field applied and potentially separating the associated motor memories12,13. The neural geometry of these shifts in preparatory activity could serve to organize skill-specific changes in movement production, facilitating the acquisition and retention of a broad motor repertoire.

neuroscience↗