Olfactory bulb tracks breathing rhythms and place in freely behaving mice DOI Creative Commons
Scott C. Sterrett, Teresa M Findley,

Sidney E. Rafilson

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 7, 2024

Abstract Vertebrates sniff to control the odor samples that enter their nose. These can not only help identify odorous objects, but also locations and events. However, there is no receptor for place or time. Therefore, take full advantage of olfactory information, an animal’s brain must contextualize odor-driven activity with information about when, where, how they sniffed. To better understand contextual in system, we captured breathing movements mice while recording from bulb. In stimulus- task-free experiments, structure into persistent rhythmic states which are synchronous statelike ongoing neuronal population activity. reflect a strong dependence individual neuron on variation frequency, display using “sniff fields” quantify generalized linear models. addition, many bulb neurons have “place significant firing allocentric location, were comparable hippocampal recorded under same conditions. At level, mouse’s location be decoded similar accuracy hippocampus. Olfactory sensitivity cannot explained by rhythms scent marks. Taken together, show mouse tracks self-location, may unite internal models self environment as soon enters brain.

Language: Английский

Olfactory bulb tracks breathing rhythms and place in freely behaving mice DOI Open Access
Scott C. Sterrett, Teresa M Findley,

Sidney E Rafilson

et al.

Published: March 11, 2025

Vertebrates sniff to control the odor samples that enter their nose. These can not only help identify odorous objects, but also locations and events. However, there is no receptor for place or time. Therefore, take full advantage of olfactory information, an animal’s brain must contextualize odor-driven activity with information about when, where, how they sniffed. To better understand contextual in system, we captured breathing movements mice while recording from bulb. In stimulus- task-free experiments, structure into persistent rhythmic states which are synchronous statelike ongoing neuronal population activity. reflect a strong dependence individual neuron on variation frequency, display using “sniff fields” quantify generalized linear models. addition, many bulb neurons have “place significant firing allocentric location, were comparable hippocampal recorded under same conditions. At level, mouse’s location be decoded similar accuracy hippocampus. Olfactory sensitivity cannot explained by rhythms scent marks. Taken together, show mouse tracks self-location, may unite internal models self environment as soon enters brain.

Language: Английский

Citations

0
Olfactory bulb tracks breathing rhythms and place in freely behaving mice DOI Open Access
Scott C. Sterrett, Teresa M Findley,

Sidney E Rafilson

et al.

Published: March 11, 2025

Vertebrates sniff to control the odor samples that enter their nose. These can not only help identify odorous objects, but also locations and events. However, there is no receptor for place or time. Therefore, take full advantage of olfactory information, an animal’s brain must contextualize odor-driven activity with information about when, where, how they sniffed. To better understand contextual in system, we captured breathing movements mice while recording from bulb. In stimulus- task-free experiments, structure into persistent rhythmic states which are synchronous statelike ongoing neuronal population activity. reflect a strong dependence individual neuron on variation frequency, display using “sniff fields” quantify generalized linear models. addition, many bulb neurons have “place significant firing allocentric location, were comparable hippocampal recorded under same conditions. At level, mouse’s location be decoded similar accuracy hippocampus. Olfactory sensitivity cannot explained by rhythms scent marks. Taken together, show mouse tracks self-location, may unite internal models self environment as soon enters brain.

Language: Английский

Citations

0
Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences DOI Open Access
Toshitake Asabuki, Tomoki Fukai

Published: Aug. 7, 2024

The brain is thought to construct an optimal internal model representing the probabilistic structure of environment accurately. Evidence suggests that spontaneous activity gives such a by cycling through patterns evoked previous sensory experiences with experienced probabilities. brain’s emerges from internally-driven neural population dynamics. However, how cortical networks encode models into poorly understood. Recent computational and experimental studies suggest neuron can implement complex computations, including predictive responses, soma-dendrite interactions. Here, we show recurrent network spiking neurons subject same learning principle provides novel mechanism learn replay experiences. In this network, rules minimize probability mismatches between stimulus-evoked internally driven activities in all excitatory inhibitory neurons. This paradigm generates stimulus-specific cell assemblies remember their activation probabilities using within-assembly connections. Our contrasts statistical Markovian transition among assemblies. We demonstrate our well replicates behavioral biases monkeys performing perceptual decision making. results interactions intracellular processes dynamics are more crucial for cognitive behaviors than previously thought.

Language: Английский

Citations

0
Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences DOI Open Access
Toshitake Asabuki, Tomoki Fukai

Published: Oct. 14, 2024

The brain is thought to construct an optimal internal model representing the probabilistic structure of environment accurately. Evidence suggests that spontaneous activity gives such a by cycling through patterns evoked previous sensory experiences with experienced probabilities. brain’s emerges from internally-driven neural population dynamics. However, how cortical networks encode models into poorly understood. Recent computational and experimental studies suggest neuron can implement complex computations, including predictive responses, soma-dendrite interactions. Here, we show recurrent network spiking neurons subject same learning principle provides novel mechanism learn replay experiences. In this network, rules minimize probability mismatches between stimulus-evoked internally driven activities in all excitatory inhibitory neurons. This paradigm generates stimulus-specific cell assemblies remember their activation probabilities using within-assembly connections. Our contrasts statistical Markovian transition among assemblies. We demonstrate our well replicates behavioral biases monkeys performing perceptual decision making. results interactions intracellular processes dynamics are more crucial for cognitive behaviors than previously thought.

Language: Английский

Citations

0
Olfactory bulb tracks breathing rhythms and place in freely behaving mice DOI Creative Commons
Scott C. Sterrett, Teresa M Findley,

Sidney E. Rafilson

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 7, 2024

Abstract Vertebrates sniff to control the odor samples that enter their nose. These can not only help identify odorous objects, but also locations and events. However, there is no receptor for place or time. Therefore, take full advantage of olfactory information, an animal’s brain must contextualize odor-driven activity with information about when, where, how they sniffed. To better understand contextual in system, we captured breathing movements mice while recording from bulb. In stimulus- task-free experiments, structure into persistent rhythmic states which are synchronous statelike ongoing neuronal population activity. reflect a strong dependence individual neuron on variation frequency, display using “sniff fields” quantify generalized linear models. addition, many bulb neurons have “place significant firing allocentric location, were comparable hippocampal recorded under same conditions. At level, mouse’s location be decoded similar accuracy hippocampus. Olfactory sensitivity cannot explained by rhythms scent marks. Taken together, show mouse tracks self-location, may unite internal models self environment as soon enters brain.

Language: Английский

Citations

0