Imaging Neuronal Network Dynamics: Implications for Information Processing and Storage

Lay Summary

Can Two-Photon Imaging Reveal Memory and Learning Processes within Brain Circuits?

Researchers will explore how information is processed and memories are stored at the level of brain circuits—neuronal networks—as viewed through two-photon imaging of mouse brain.

Newly developed cellular imaging technologies can reveal actions of individual brain cells and their interactions. Moreover, using a custom-built two-photon cellular imaging microscope to look at a section of mouse brain in a laboratory tissue culture, University of Chicago investigators are able to visualize the actions of 1,000 neurons in a defined neural network at high speed, with high resolution, as information flows through it.  The investigators will use this technology, employing a “vector scan” technique, to study brain plasticity. Specifically, they will evaluate whether neural circuits are modified by new information. They hypothesize that new information is stored in a neural circuit and later replayed in a patterned way that becomes increasingly cohesive. Ultimately, they hypothesize, memory formation results from the strengthening of specific synaptic connections between cells within the neural circuit.

This hypothesis is consistent with theories that memories are rapidly formed in the hippocampus and transferred to the neocortex during slow-wave sleep for long-term storage that involves memory consolidation. This transfer of memory representation to the neocortex would be expected to require coordinated activation of a local circuit of neurons in the neocortex.

As a first step in exploring this theory at the circuit level, the investigators will use two-photon imaging of a section of the mouse brain which includes both the neocortex and the thalamus in combination with electrophysiological, anatomical and quantitative methods. They will determine whether patterned replay of neuronal circuit activity results in changes in the strength of synaptic connections between neurons in the circuit. Then they will explore whether individual neurons can be functionally incorporated into a circuit by strengthening specific connections during patterned circuit activity. If so, this would suggest that adding or deleting neurons from the circuit is a major way that information is stored or changed in circuits. Lastly, they will determine the rules that govern how the strength of synaptic connections is modified within a circuit.

Abstract

Imaging Neuronal Network Dynamics: Implications for Information Processing and Storage

For decades, it has been postulated that information is represented in the brain by patterned neuronal activations. Single neurons do not act as isolated computational units: rather, neurons act together in a complex circuit, to relay, process and store information. Furthermore, it has been postulated that repeating patterns of neuronal activity are instantiations of recent memory consolidation.

UP states, periods of prolonged depolarization, identify functional networks of interconnected neurons in local patches of neocortex. Recently, I have demonstrated that UP states are synonymous with patterned and stereotyped sequences of neuronal activity. I propose to investigate the role of stereotyped sequences in synaptic plasticity within these identified neocortical networks. My working hypothesis is that patterned circuit activity encodes recent input to the circuit. The pattern can be stored in the circuit and later recalled, as indicated by its replay. The hypothesis predicts that patterns of activity, which arise during UP states, are reinforced by synaptic plasticity mechanisms, becoming increasingly stereotypic or ‘hard wired’. Thus these replayed patterns may serve to consolidate the transient effects of circuit inputs into long-lasting circuit modifications.

To test this hypothesis I will evaluate whether circuit dynamics facilitate synaptic plasticity in a network and further whether this plasticity promotes the addition of neurons to the circuit. Both may be important mechanisms by which information is stored within neocortical circuits. Finally I will evaluate whether the circuit dynamics changes the rules that govern the plasticity of individual synapses. Characterization of neuronal plasticity at the circuit level during naturalistic circuit activity is a crucial step in understanding the neural basis for learning and memory.

These novel datasets will reveal fundamental and mechanistic insights into how memory is stored and information is processed at the circuit level. Cognitive and memory deficits are common to many neurological disorders and current treatment attempts to fix a problem that we do not understand. Thus, revealing the fundamental mechanisms underlying these processes will better inform potential targets for therapeutic intervention.