Biology subjects

Daie, K.

Publications and source records attributed to Daie, K..

9 recordsLinked to original sources

bioRxiv2026

Gradients of local circuit architecture shape stability and information integration across the cortical hierarchy

The mammalian neocortex is a complex structure with many interacting regions and cell types. Multiple efforts have recently generated large datasets from multiple cortical regions to determine generalizing principles of cortical organization and activity. One such generalizing principle is the concept of a cortical hierarchy - low-dimensional macroscopic gradients of biological properties across cortical areas and a hierarchy of timescales for information processing. Such gradients in properties along a cortical hierarchy may explain how different cortical regions serve different computational roles (i.e. sensory processing versus working memory). We set out to investigate such principles using biologically realistic simulations. Starting from our previously published model of the mouse primary visual cortex (VISp) with updated connectivity reflecting recent large-scale connectomics and synaptic physiology datasets, we generated multiple versions of the cortical circuit model with increased connectivity as measured along the cortical hierarchy. We then measure the resulting neural activity in response to a simulated single-cell photostimulation paradigm and thalamocortical inputs. We found that overall circuit stability increased across the cortical hierarchy, due to both increases in connection density and increases in the relative proportion of SST cells. The results of this study link cortical cell types to population-level activity, generating insight into the function of local circuits across cortex.

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bioRxiv2026

Functional reorganization of motor cortex connectivity during learning

Learning new tasks requires the brain to reshape the flow of neural activity, but how these changes arise from dynamic neural connectivity remains unclear. Here, we used two-photon photostimulation and calcium imaging to map learning-related changes in connectivity in layer 2/3 of mouse motor cortex, induced by learning of an optical brain-computer interface (BCI) task. Mice rapidly (within minutes) learned to change activity in a conditioned neuron to earn rewards. Activity changes were sparse; the conditioned neuron increased activity more than surrounding neurons. Mapping connectivity before and after learning revealed changes in motor cortex connectivity, enriched in neurons that were active before trial initiation, analogous to motor cortex populations that are active preceding movement. Motor cortex plasticity reroutes preparatory activity to neurons that are active later and control the conditioned neuron. Our findings show how rapid learning can be achieved through structured changes in motor cortex connectivity.

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bioRxiv2025

Neural dynamics outside task-coding dimensions drive decision trajectories through transient amplification

Linking neural activity to behavior typically involves identifying activity subspaces that encode task information, such as stimuli, memory, and choices. However, it is unclear whether activity in these "coding dimensions" drives behavior or merely reflects the underlying computations. Neural activity in other "residual dimensions" is typically ignored. We developed a recurrent neural network that explicitly models interactions between coding and residual subspaces. Applied to multi-regional Neuropixels recordings from mice performing a delayed movement task, the model reveals that perturbations of residual dimensions reliably alter behavior, whereas perturbations of the choice dimension, which encodes the animals decision, are less effective. These effects arise because residual dimensions drive amplification across an intermediate number ([~]10) of dimensions, before the dynamics settle into discrete attractors corresponding to choice. Our findings show that neural activity previously considered task-irrelevant can have critical roles in driving behavior.

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bioRxiv2024

A combinatorial neural code for long-term motor memory

Motor skill repertoire can be stably retained over long periods, but the neural mechanism underlying stable memory storage remains poorly understood. Moreover, it is unknown how existing motor memories are maintained as new motor skills are continuously acquired. Here we tracked neural representation of learned actions throughout a significant portion of a mouses lifespan, and we show that learned actions are stably retained in motor memory in combination with context, which protects existing memories from erasure during new motor learning. We used automated home-cage training to establish a continual learning paradigm in which mice learned to perform directional licking in different task contexts. We combined this paradigm with chronic two-photon imaging of motor cortex activity for up to 6 months. Within the same task context, activity driving directional licking was stable over time with little representational drift. When learning new task contexts, new preparatory activity emerged to drive the same licking actions. Learning created parallel new motor memories while retaining the previous memories. Re-learning to make the same actions in the previous task context re-activated the previous preparatory activity, even months later. At the same time, continual learning of new task contexts kept creating new preparatory activity patterns. Context-specific memories, as we observed in the motor system, may provide a solution for stable memory storage throughout continual learning. Learning in new contexts produces parallel new representations instead of modifying existing representations, thus protecting existing motor repertoire from erasure.

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bioRxiv2023

Connectivity underlying motor cortex activity during naturalistic goal-directed behavior

Neural representations of information are shaped by local network interactions. Previous studies linking neural coding and cortical connectivity focused on stimulus selectivity in the sensory cortex 1-4. Here we study neural activity in the motor cortex during naturalistic behavior in which mice gathered rewards with multidirectional tongue reaching. This behavior does not require training and thus allowed us to probe neural coding and connectivity in motor cortex before its activity is shaped by learning a specific task. Neurons typically responded during and after reaching movements and exhibited conjunctive tuning to target location and reward outcome. We used an all-optical 5,4,6,7 method for large-scale causal functional connectivity mapping in vivo. Mapping connectivity between > 20,000,000 excitatory neuronal pairs revealed fine-scale columnar architecture in layer 2/3 of the motor cortex. Neurons displayed local (< 100 {micro}m) like-to-like connectivity according to target-location tuning, and inhibition over longer spatial scales. Connectivity patterns comprised a continuum, with abundant weakly connected neurons and sparse strongly connected neurons that function as network hubs. Hub neurons were weakly tuned to target-location and reward-outcome but strongly influenced neighboring neurons. This network of neurons, encoding location and outcome of movements to different motor goals, may be a general substrate for rapid learning of complex, goal-directed behaviors.

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bioRxiv2023

Feedforward amplification in recurrent networks underliesparadoxical neural coding

The activity of single neurons encodes behavioral variables, such as sensory stimuli (Hubel & Wiesel 1959) and behavioral choice (Britten et al. 1992; Guo et al. 2014), but their influence on behavior is often mysterious. We estimated the influence of a unit of neural activity on behavioral choice from recordings in anterior lateral motor cortex (ALM) in mice performing a memory-guided movement task (H. K. Inagaki et al. 2018). Choice selectivity grew as it flowed through a sequence of directions in activity space. Early directions carried little selectivity but were predicted to have a large behavioral influence, while late directions carried large selectivity and little behavioral influence. Consequently, estimated behavioral influence was only weakly correlated with choice selectivity; a large proportion of neurons selective for one choice were predicted to influence choice in the opposite direction. These results were consistent with models in which recurrent circuits produce feedforward amplification (Goldman 2009; Ganguli et al. 2008; Murphy & Miller 2009) so that small amplitude signals along early directions are amplified to produce low-dimensional choice selectivity along the late directions, and behavior. Targeted photostimulation experiments (Daie et al. 2021b) revealed that activity along the early directions triggered sequential activity along the later directions and caused predictable behavioral biases. These results demonstrate the existence of an amplifying feedforward dynamical motif in the motor cortex, explain paradoxical responses to perturbation experiments (Chettih & Harvey 2019; Daie et al. 2021b; Russell et al. 2019), and reveal behavioral relevance of small amplitude neural dynamics.

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bioRxiv2022

BCI learning phenomena can be explained by gradient-based optimization

Brain-computer interface (BCI) experiments have shown that animals are able to adapt their recorded neural activity in order to receive reward. Recent studies have highlighted two phenomena. First, the speed at which a BCI task can be learned is dependent on how closely the required neural activity aligns with pre-existing activity patterns: learning "out-of-manifold" tasks is slower than "in-manifold" tasks. Second, learning happens by "re-association": the overall distribution of neural activity patterns does not change significantly during task learning. These phenomena have been presented as distinctive aspects of BCI learning. Here we show, using simulations and theoretical analysis, that both phenomena result from the simple assumption that behaviour and representations are improved via gradient-based algorithms. We invoke Occams Razor to suggest that this straightforward explanation should be preferred when accounting for these experimental observations.

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bioRxiv2019

A comparison of neuronal population dynamics measured with calcium imaging and electrophysiology

Calcium imaging with fluorescent protein sensors is widely used to record activity in neuronal populations. The transform between neural activity and calcium-related fluorescence involves nonlinearities and a low-pass filter, but the effects of the transformation on analyses of neural populations are not well understood. We compared neuronal spikes and fluorescence in matched neural populations in behaving mice. We report multiple discrepancies between analyses performed on the two types of data, which were only partially resolved by spike inference algorithms applied to fluorescence. To model the relation between spiking and fluorescence we simultaneously recorded spikes and fluorescence from individual neurons. Using these recordings we developed a model transforming spike trains to synthetic-imaging data. The model recapitulated the differences in analyses. Our analysis highlights challenges in relating electrophysiology and imaging data, and suggests forward modeling as an effective way to understand differences between these data.

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bioRxiv2019

Targeted photostimulation uncovers circuit motifs supporting short-term memory

Short-term memory is associated with persistent neural activity without sustained input, arising from the interactions between neurons with short time constants1,2. A variety of neural circuit motifs could account for measured neural activity3-7. A mechanistic understanding of the neural circuits supporting short-term memory requires probing network connectivity between functionally characterized neurons8. We performed targeted photostimulation of small (< 10) groups of neurons, while imaging the response of hundreds of other neurons9,10, in anterior-lateral motor cortex (ALM) of mice performing a delayed response task11. Mice were instructed with brief auditory stimuli to make directional movements (lick left or lick right), but only after a three second delay epoch. ALM contains neurons with delay epoch activity that is selective for left or right choices. Targeted photostimulation of groups of neurons during the delay epoch allowed us to observe the functional organization of population activity and recurrent interactions underlying short-term memory. These experiments revealed strong coupling between neurons sharing similar selectivity. Brief photostimulation of functionally related neurons produced changes in activity in sparse subpopulations of nearby neurons that persisted for several seconds following stimulus offset, far outlasting the duration of the perturbation. Photostimulation produced behavioral biases that were predictable based on the selectivity of the perturbed neuronal population. These results suggest that ALM contains multiple intercalated modules, consisting of recurrently coupled neurons, that can independently maintain persistent activity.

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