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Fushiki, A.

Publications and source records attributed to Fushiki, A..

7 recordsLinked to original sources

Reinforcement sculpts synaptic ensembles for operant dendritic control

Reinforcement learning requires the selective modification of synapses that contribute to outcome-relevant neural activity, yet the principles underlying these selective modifications remain unclear. Here we combined longitudinal two-photon imaging of identified dendritic spines with a closed-loop optical brain-machine interface (BMI), in which activity from selected apical dendrites of layer 5 motor-cortical neurons controlled an auditory cursor, with target acquisition triggering optogenetic reinforcement. Mice learned to reliably generate the specific dendritic activity that leads to reinforcement. Learning was accompanied by selective strengthening and weakening of individual spines on reinforced dendrites. At individual synapses, the direction of plasticity depended on both the timing of activity relative to reinforced dendritic events and the pre-existing temporal organization of that activity. Furthermore, plasticity was enriched in small task-relevant synaptic groups: spines in synergistic groups were preferentially strengthened, whereas those in antagonistic groups were preferentially weakened. These changes were accompanied by the emergence of increasingly reproducible, selectively recruited, and temporally focused synaptic ensembles, and the incorporation of newly formed spines into these ensembles. Finally, blocking striatal dopamine receptor signaling impaired BMI learning and cortical synaptic reorganization of target dendrites, demonstrating a role for striatal reinforcement in cortical plasticity. Together, these findings reveal how reinforcement of specific dendritic activity sculpts synaptic ensembles that support learned operant dendritic control.

neuroscience↗

Generation of knock-in Cre and FlpO mouse lines for precise targeting of striatal projection neurons and dopaminergic neurons

The basal ganglia and midbrain dopaminergic systems are critical for motor control, reward processing, and reinforcement learning, with dysfunction in these systems implicated in numerous neurodegenerative and neuropsychiatric disorders. To enable precise genetic targeting of key neuronal populations, we generated and characterized five knock-in mouse lines: Drd1-Cre, Adora2a-Cre, Drd1-FlpO, Adora2a-FlpO, and DAT-FlpO. These lines allow for Cre-or FlpO-mediated recombination in dopamine D1 receptor-expressing spiny projection neurons (SPNs), adenosine A2a receptor-expressing SPNs, and dopamine transporter (DAT)-expressing neurons in the midbrain. Histological analyses confirmed recombinase activity in expected brain regions, and whole-cell electrophysiological recordings validated the intrinsic excitability profiles of each neuronal subpopulation. These tools provide high specificity and reliability for studying basal ganglia circuitry and dopaminergic neurons. By enabling targeted manipulations, these openly available knock-in lines will advance research into the neural mechanisms underlying motor control, reward, and neuropsychiatric diseases.

neuroscience↗

A Vulnerable Subtype of Dopaminergic Neurons Drives Early Motor Deficits in Parkinson's Disease

In Parkinsons disease (PD), dopaminergic neurons (DANs) in the midbrain gradually degenerate, with ventral substantia nigra pars compacta (SNc) DANs exhibiting greater vulnerability. However, it remains unclear whether specific molecular subtypes of ventral SNc DANs are more susceptible to degeneration in PD, and if they contribute to the early motor symptoms associated with the disease. We identified a subtype of Sox6+ DANs, Anxa1+, which are selectively lost earlier than other DANs, and with a time course that aligns with the development of motor symptoms in MitoPark mice. We generated a knock-in Cre mouse line for Anxa1+ DANs and showed differential anatomical inputs and outputs of this population. Further, we found that the inhibition of transmitter release in Anxa1+ neurons led to bradykinesia and tremor. Therefore, Anxa1+ is not only a biomarker of a selectively vulnerable subtype of DANs, but is also sufficient to drive early motor symptoms in Parkinsons disease.

neuroscience↗

The connectome of an insect brain

Brains contain networks of interconnected neurons, so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an insect brain (Drosophila larva) with rich behavior, including learning, value-computation, and action-selection, comprising 3,013 neurons and 544,000 synapses. We characterized neuron-types, hubs, feedforward and feedback pathways, and cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brains most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled powerful machine learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits. One-Sentence SummaryWe generated a synaptic-resolution brain connectome and characterized its connection types, neuron types, and circuit motifs.

neuroscience↗

Circuits for integrating learnt and innate valences in the fly brain

Animal behavior is shaped both by evolution and by individual experience. In many species parallel brain pathways are thought to encode innate and learnt behavior drives and as a result may link the same sensory cue to different actions if innate and learnt drives are in opposition. How these opposing drives are integrated into a single coherent action is not well understood. In insects, the Mushroom Body Output Neurons (MBONs) and the Lateral Horn Neurons (LHNs) are thought to provide the learnt and innate drives, respectively. However their patterns of convergence and the mechanisms by which their outputs are used to select actions are not well understood. We used electron microscopy reconstruction to comprehensively map the downstream targets of all MBONs in Drosophila larva and characterise their patterns of convergence with LHNs. We discovered convergence neurons that receive direct input from MBONs and LHNs and compare opposite behaviour drives. Functional imaging and optogenetic manipulation suggest these convergence neurons compute the overall predicted value of approaching or avoiding an odor and mediate action selection. Our study describes the circuit mechanisms allowing integration of opposing drives from parallel olfactory pathways.

neuroscience↗

An amygdala circuit mediates experience-dependent momentary exploratory arrests

Exploration of novel environments ensures survival and evolutionary fitness. This behavior is expressed through exploratory bouts and arrests, which change dynamically based on experience. Neural circuits mediating exploratory behavior should therefore integrate experience and use it to select the proper behavioral output. Using a spatial exploration assay, we uncovered an experience-dependent increase of momentary arrests in visited locations where animals previously arrested. Quantitative analyses of neuronal calcium activity in freely-exploring mice revealed that a large neuronal ensemble in basolateral amygdala is active during self-paced behavioral arrests. This ensemble was recruited in an experience-dependent manner, and closed-loop optogenetic manipulation of these neurons revealed that they are sufficient and necessary to drive experience-dependent arrests. Additionally, we found that neurons in the basolateral amygdala projecting to central amygdala mediate these momentary arrests. These findings uncover an amygdala circuit that mediates momentary exploratory arrests in familiar places, without changing place preference or anxiety/fear-like behaviors.

neuroscience↗

Multilevel feedback architecture for adaptive regulation of learning in the insect brain

Modulatory (e.g. dopaminergic) neurons provide \"teaching signals\" that drive associative learning across the animal kingdom, but the circuits that regulate their activity and compute teaching signals are still poorly understood. We provide the first synaptic-resolution connectome of the circuitry upstream of all modulatory neurons in a brain center for associative learning, the mushroom body (MB) of the Drosophila larva. We discovered afferent pathways from sensory neurons, as well as an unexpected large population of 61 feedback neuron pairs that provide one- and two-step feedback from MB output neurons. The majority of these feedback pathways link distinct memory systems (e.g. aversive and appetitive). We functionally confirmed some of the structural pathways and found that some modulatory neurons compare inhibitory input from their own compartment and excitatory input from compartments of opposite valence, enabling them to compute integrated common-currency predicted values across aversive and appetitive memory systems. This architecture suggests that the MB functions as an interconnected ensemble during learning and that distinct types of previously formed memories can regulate future learning about a stimulus. We developed a model of the circuit constrained by the connectome and by the functional data which revealed that the newly discovered architectural motifs, namely the multilevel feedback architecture and the extensive cross-compartment connections, increase the computational performance and flexibility on learning tasks. Together our study provides the most detailed view to date of a recurrent brain circuit that computes teaching signals and provides insights into the architectural motifs that support reinforcement learning in a biological system.

neuroscience↗