A wide range of environmental stimuli and brain rhythms, generate precise mental maps of space. The mechanism by which this happens remain to be understood. A noninvasive virtual reality system reveals that there are multiple neural maps of space that compete with each other.
Our research has focused on computational and experimental investigations of learning and memory, seeking to understand how the brain learns and remembers how to navigate unfamiliar environments, research aimed at paving the way to better understanding mechanisms of learning and memory in neural networks.
UCLA researchers have for the first time measured the activity of a brain region known to be involved in learning, memory and Alzheimer's disease during sleep. They discovered that this region, called the entorhinal cortex, behaves as if it is remembering something, even during anesthesia-induced sleep.
In a discovery that challenges conventional wisdom on the brain mechanisms of learning, UCLA neuro-physicists have found there is an optimal brain "rhythm," or frequency, for changing synaptic strength. And further, like stations on a radio dial, each synapse is tuned to a different optimal frequency for learning.
Rhythms in the brain that are associated with learning become stronger as the body moves faster. Our research team found that the strength of the gamma rhythm grew substantially as running speed increased, bringing scientists a step closer to understanding the brain functions essential for learning and navigation.
Memories are stored in both the neocortex and the hippocampus. Then, during sleep, the hippocampus, acting as a temporary storage system, is cleared for another day of learning, while the memories are retained in the neocortex, which provides permanent storage much like a computer hard disk.
Neurons are tree-like cells with connections between neurons made on branch-like processes called dendrites. A novel, biophysical 'Hebbian' learning rule is proposed that explains the dendritic contribution to learning.
Neuronal communication is crucial for learning and this requires high temporal precision. A mechanism is proposed where brain rhythms make neuronal communication more precise and facilitate learning.
It is thought that learning from experience occurs via changes in connections between neurons. Here we demonstrate how neural circuits change as a result of just a short walk in a maze.
This mathematical theory shows that there must be at least two extra space-time dimensions attached to our 'normal' space-time for quantum field theories to be mathematically well-defined.
The mind is thought to be the emergent property of the activities of ensembles of neurons. The nature of these emergent properties and how they arise are unknown. This is the focus of our research. In particular, our current research addresses the following fundamental questions in Neurophysics:
How is information about the physical world represented by ensembles of neurons? In particular, what are the neural mechanisms of perceiving space-time?
How do these neural representations evolve with learning?
What is the role of brain rhythms in learning and memory?
How does sleep influence learning?
To address these questions we use both experimental and theoretical approaches as follows:
- Develop hardware to measure and manipulate neural activity and behavior.
- Measure the activity of ensembles of well isolated neurons from many hippocampal and neocortical areas simultaneously during learning and during sleep.
- Develop data analysis tools to decipher the patterns of neural activity and field potentials, and their relationship to behavior.
- Develop biophysical theories of synapses, neurons and neuronal networks that can explain these experimental findings, relate them to the underlying cellular mechanisms, and make experimentally testable predictions.
The results would not only provide fundamental understanding of neural ensemble dynamics but also point to novel ways of treating learning and memory disorders.
During real-world (RW) exploration, rodent hippocampal activity shows robust spatial selectivity, which is hypothesized to be governed largely by distal visual cues, although other sensory-motor cues also contribute. To determine the contribution of distal visual cues only, we measured hippocampal activity from body-fixed rodents exploring a two-dimensional virtual reality (VR).
These findings indicate that rats can navigate in virtual space with only distal visual cues, without significant vestibular or other sensory inputs. Furthermore, they reveal the simultaneous dissociation between two reward-driven behaviors.
The hippocampal cognitive map is thought to be driven by distal visual cues and self-motion cues. However, other sensory cues also influence place cells. Hence, we measured rat hippocampal activity in virtual reality (VR), where only distal visual and nonvestibular self-motion cues provided spatial information, and in the real world (RW).
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