Biology subjects

Haga, T.

Publications and source records attributed to Haga, T..

3 recordsLinked to original sources

bioRxiv2017

Recurrent network model for learning goal-directed sequences through reverse replay

Reverse replay of hippocampal place cells occurs frequently at rewarded locations, suggesting its contribution to goal-directed pathway learning. Symmetric spike-timing dependent plasticity (STDP) in CA3 likely potentiates recurrent synapses for both forward (start to goal) and reverse (goal to start) replays during sequential activation of place cells. However, how reverse replay selectively strengthens forward pathway is unclear. Here, we show computationally that firing sequences bias synaptic transmissions to the opposite direction of propagation under symmetric, but not asymmetric, STDP in the co-presence of short-term synaptic plasticity. We demonstrate that a significant bias can be created in biologically realistic simulation settings, and that this bias enables reverse replay to enhance goal-directed spatial memory on a T-maze. Our model for the first time provides the mechanistic account for the way reverse replay contributes to hippocampal sequence learning for reward-seeking spatial navigation.

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↗