Biology subjects

Lak, A.

Publications and source records attributed to Lak, A..

25 records · Page 2Linked to original sources

bioRxiv2023

Reward-Bases: Dopaminergic Mechanisms for Adaptive Acquisition of Multiple Reward Types

Animals can adapt their preferences for different types for reward according to physiological state, such as hunger or thirst. To describe this ability, we propose a simple extension of temporal difference model that learns multiple values of each state according to different reward dimensions such as food or water. By weighting these learned values according to the current needs, behaviour may be flexibly adapted to present demands. Our model predicts that different dopamine neurons should be selective for different reward dimensions. We reanalysed data from primate dopamine neurons and observed that in addition to subjective value, dopamine neurons encode a gradient of reward dimensions; some neurons respond most to food rewards while the others respond more to fluids. Moreover, our model reproduces instant generalization to new physiological state seen in dopamine responses and in behaviour. Our results demonstrate how simple neural circuit can flexibly optimize behaviour according to animals needs.

neuroscience↗

bioRxiv2023

Cooperative dynamics of DNA grafted magnetic nanoparticles optimize magnetic biosensing and coupling to DNA origami

Magnetic nanoparticles (MNPs) enable unique capabilities for biosensing and actuation via coupling to DNA origami, yet how DNA grafting density affects their dynamics and accessibility remains poorly understood. Here, we demonstrate functionalization of MNPs with single-stranded DNA (ssDNA) via click chemistry conjugation with tunable grafting density. Several complementary methods show that particle translational and rotational dynamics exhibit a sigmoidal dependence on ssDNA grafting density. At low densities ssDNA strands are coiled and cause small changes to particle dynamics, while at high densities they form polymer brushes that cooperatively change particle dynamics. Intermediate ssDNA densities show the highest magnetic biosensing sensitivity for detection of target nucleic acids. Finally, we demonstrate that MNPs with high grafting densities are required to efficiently couple them to DNA origami. These results together establish ssDNA grafting density as a critical parameter for functionalization of MNPs for use in a broad range of applications.

biochemistry↗

bioRxiv2022

Amplification and extraction free quantitative detection of viral nucleic acids and single-base mismatches using magnetic signal amplification circuit

Established nucleic acid detection assays require extraction and purification before sequence amplification and/or enzymatic reactions, hampering their widespread applications in point-of-care (POC) formats. Magnetic immunoassays based on magnetic particle spectroscopy and magnetic nanoparticles (MNPs) are isothermal, extraction- and purification-free, and can be quantitative and benchtop, making them suitable for POC settings. Here, we demonstrate a Magnetic signal Amplification Circuit (MAC) that combines specificity of toehold-mediated DNA strand displacement with magnetic response of MNPs to a clustering/declustering process. Our MAC assays require neither amplification nor extraction of target nucleic acids, and reveal four times better sensitivity than that of a magnetic circuit without signal amplification. Using MAC, we detect a highly specific 43 nucleotides sequence of SARS-CoV-2 virus. The MAC enables sensing both DNA and RNA targets with varying lengths and resolving single-base mismatches. Our MAC can be a powerful tool for translating research of nucleic acids detection to the clinic.

biochemistry↗

bioRxiv2021

Midbrain dopamine neurons provide teaching signals for goal-directed navigation

In naturalistic environments, animals navigate in order to harvest rewards. Successful goal-directed navigation requires learning to accurately estimate location and select optimal state-dependent actions. Midbrain dopamine neurons are known to be involved in reward value learning1-13. They have also been linked to reward location learning, as they play causal roles in place preference14,15 and enhance spatial memory16-21. Dopamine neurons are therefore ideally placed to provide teaching signals for goal-directed navigation. To test this, we imaged dopamine neural activity as mice learned to navigate in a closed-loop virtual reality corridor and lick to report the reward location. Across learning, phasic dopamine responses developed to visual cues and trial outcome that resembled reward prediction errors and indicated the animals estimate of the reward location. We also observed the development of pre-reward ramping activity, the slope of which was modulated by both learning stage and task engagement. The slope of the dopamine ramp was correlated with the accuracy of licks in the next trial, suggesting that the ramp sculpted accurate location-specific action during navigation. Our results indicate that midbrain dopamine neurons, through both their phasic and ramping activity, provide teaching signals for improving goal-directed navigation. HighlightsO_LIWe investigated midbrain dopamine activity in mice learning a goal-directed navigation task in virtual reality C_LIO_LIPhasic dopamine signals reflected prediction errors with respect to subjective estimate of reward location C_LIO_LIA slow ramp in dopamine activity leading up to reward location developed over learning and was enhanced with task engagement C_LIO_LIPositive ramp slopes were followed by improved performance on subsequent trials, suggesting a teaching role during goal-directed navigation C_LI

neuroscience↗

bioRxiv2020

Dopamine axons to dorsal striatum encode contralateral stimuli and actions

Midbrain dopamine neurons play key roles in decision-making by regulating reward valuation and actions. These roles are thought to depend on dopamine neurons innervating striatum. In addition to actions and rewards, however, efficient decisions often involve consideration of uncertain sensory signals. The functions of striatal dopamine during sensory decisions remains unknown. We trained mice in a task that probed decisions based on sensory evidence and reward value, and recorded the activity of striatal dopamine axons. Dopamine axons in ventral striatum (VS) responded to bilateral stimuli and trial outcomes, encoding prediction errors that scaled with decision confidence and reward value. By contrast, dopamine axons in dorsal striatum (DS) responded to contralateral stimuli and contralateral actions. Thus, during sensory decisions, striatal dopamine signals are anatomically organized. VS dopamine resembles prediction errors suitable for reward maximization under sensory uncertainty whereas DS dopamine encodes specific combinations of stimuli and actions in a lateralized fashion.

neuroscience↗

bioRxiv2018

Dopaminergic and frontal signals for decisions guided by sensory evidence and reward value

Making efficient decisions requires combining present sensory evidence with previous reward values, and learning from the resulting outcome. To establish the underlying neural processes, we trained mice in a task that probed such decisions. Mouse choices conformed to a reinforcement learning model that estimates predicted value (reward value times sensory confidence) and prediction error (outcome minus predicted value). Predicted value was encoded in the pre-outcome activity of prelimbic frontal neurons and midbrain dopamine neurons. Prediction error was encoded in the post-outcome activity of dopamine neurons, which reflected not only reward value but also sensory confidence. Manipulations of these signals spared ongoing choices but profoundly affected subsequent learning. Learning depended on the pre-outcome activity of prelimbic neurons, but not dopamine neurons. Learning also depended on the post-outcome activity of dopamine neurons, but not prelimbic neurons. These results reveal the distinct roles of frontal and dopamine neurons in learning under uncertainty.

neuroscience↗

bioRxiv2018

Distinct structure of cortical population activity on fast and infraslow timescales

Cortical activity is organised across multiple spatial and temporal scales. Most research on the dynamics of neuronal spiking is concerned with timescales of 1 ms - 1 s, and little is known about spiking dynamics on timescales of tens of seconds and minutes. Here, we used frequency domain analyses to study the structure of individual neurons spiking activity and its coupling to local population rate and to arousal level across frequencies ranging from 0.01 to 100 Hz. In mouse medial prefrontal cortex (mPFC), the spiking dynamics of individual neurons could be quantitatively captured by a combination of interspike interval and firing rate power spectrum distributions. The relative strength of coherence with local population often differed across timescales: a neuron strongly coupled to population rate on fast timescales could be weakly coupled on slow timescales, and vice versa. On slow but not fast timescales, a substantial proportion of neurons showed firing anti-correlated with the population. Infraslow firing rate changes were largely determined by arousal rather than by local factors, which could explain the timescale dependence of population coupling strength of individual neurons. These observations demonstrate how individual neurons simultaneously partake in fast local dynamics, and slow brain-wide dynamics, extending our understanding of infraslow cortical activity beyond the mesoscale resolution of fMRI studies.

neuroscience↗