Biology subjects

Fukai, T.

Publications and source records attributed to Fukai, T..

42 records · Page 3Linked to original sources

bioRxiv2017

Automatic sorting system for large calcium imaging data

It has become possible to observe neural activity in freely moving animals via calcium imaging using a microscope, which could not be observed previously. However, it remains difficult to extract the dynamics of nerve cells from the recorded imaging data. In this study, we greatly improved the stability, and robustness of the cell activity estimation method via non-negative matrix decomposition with shrinkage estimation of the baseline. In addition, by improving the initial state of the iterative algorithm using a newly proposed method to extract the shape of the cell via image processing, a solution could be obtained with a small number of iterations. These methods were applied to artificial and real data, and their effectiveness was confirmed.

neuroscience↗

bioRxiv2017

Chunking sequence information by mutually predicting recurrent neural networks

Interpretation and execution of complex sequences is crucial for various cognitive tasks such as language processing and motor control. The brain solves this problem arguably by dividing a sequence into discrete chunks of contiguous items. While chunking has been accounted for by predictive uncertainty, alternative mechanisms have also been suggested, and the mechanism underlying chunking is poorly understood. Here, we propose a class of unsupervised neural networks for learning and identifying repeated patterns in sequence input with various degrees of complexity. In this model, a pair of reservoir computing modules, each of which comprises a recurrent neural network and readout units, supervise each other to consistently predict others responses to frequently recurring segments. Interestingly, this system generates neural responses similar to those formed in the basal ganglia during habit formation. Our model extends reservoir computing to higher cognitive function and demonstrates its resemblance to sequence processing by cortico-basal ganglia loops.

neuroscience↗

bioRxiv2017

Unsupervised detection of cell-assembly sequences with edit similarity score

Cell assembly is a hypothetical functional unit of information processing in the brain. While technologies for recording large-scale neural activity have been advanced, mathematical methods to analyze sequential activity patterns of cell-assembly are severely limited. Here, we propose a method to extract cell-assembly sequences repeated at multiple time scales and various precisions from irregular neural population activity. The key technology is to combine \"edit similarity\" in computer science with machine-learning clustering algorithms, where the former defines a \"distance\" between two strings as the minimal number of operations required to transform one string to the other. Our method requires no external references for pattern detection, and is tolerant of spike timing jitters and length irregularity in assembly sequences. These virtues enabled simultaneous automatic detections of hippocampal place-cell sequences during locomotion and their time-compressed replays during resting states. Furthermore, our method revealed previously undetected cell-assembly structure in the rat prefrontal cortex during goal-directed behavior. Thus, our method expands the horizon of cell-assembly analysis.

neuroscience↗

bioRxiv2017

Dendritic processing of spontaneous neuronal sequences for one-shot learning

Spontaneous firing sequences colloquially called \"preplay\" are fundamental features of hippocampal network physiology. Preplay sequences have been hypothesized to participate in hippocampal learning and memory, but such functional roles and their potential cellular mechanisms remain unexplored. Here, we report a computational model based on the functional propagation of preplay sequences in the CA3 neuronal network. The model instantiates two synaptic pathways in CA3 neurons, one for proximal dendrite-somatic interactions to generate intrinsic preplay sequences and the other for distal dendritic processing of extrinsic sensory signals. The core dendritic computation is the maximization of matching between patterned activities in the two compartments through nonlinear spike generation. The model performs robust one-shot learning with long-term stability and independence that are modulated by the plasticity of dendrite-targeted inhibition. This model demonstrates that learning models combined with dendritic computations can enable preplay sequences to act as templates for rapid and stable memory formation.

neuroscience↗

bioRxiv2017

Individual Differences In Perceptual Decision Making Reflect Neural Variability In Medial Frontal Cortex

Decision making obeys common neural mechanisms, but there is considerable variability in individuals decision making behavior particularly under uncertainty. How individual differences arise within common decision making brain systems is not known. Here, we explored this question in the medial frontal cortex (MFC) of rats performing a sensory-guided choice task. When rats trained on familiar stimuli were exposed to unfamiliar stimuli, choice responses varied significantly across individuals. We examined how variability in MFC neural processing could mediate this individual difference and constructed a network model to replicate this. Our model suggested that susceptibility of neural trajectories is a crucial determinant of the observed choice variability. The model predicted that trial-by-trial variability of trajectories are correlated with the susceptibility, and hence also correlated with the individual difference. This prediction was confirmed by experiment. Thus, our results suggest that variability in neural dynamics in MFC networks underlies individual differences in decision making.

neuroscience↗

bioRxiv2017

Redundancy in synaptic connections enables neurons to learn optimally

Recent experimental studies suggest that, in cortical microcircuits of the mammalian brain, the majority of neuron-to-neuron connections are realized by multiple synapses. However, it is not known whether such redundant synaptic connections provide any functional benefit. Here, we show that redundant synaptic connections enable near-optimal learning in cooperation with synaptic rewiring. By constructing a simple dendritic neuron model, we demonstrate that with multisynaptic connections, synaptic plasticity approximates a sample-based Bayesian filtering algorithm known as particle filtering, and wiring plasticity implements its resampling process. The derived synaptic plasticity rule accounts for many experimental observations, including the dendritic position dependence of spike-timing-dependent plasticity. The proposed framework is applicable to detailed single neuron models, and also to recurrent circuit models. Our study provides a novel conceptual framework for synaptic plasticity and rewiring.

neuroscience↗