Increasing the Resolution of Neural Connectivity to Infer Neural Encoding
Abstract
1. Apply the recurrent neural network configuration, long short term memory (LSTM), as a universal learning core that can predict connectivity maps across independent neuronal networks, classify neurological conditions and transmit synthetic memory traces. Recordings from multiple electrodes in intact animals are commonly used to train a generalized linear model (GLM) and the model is validated by how effectively the GLM filters predict a behavior. We have developed a system that allows us to directly test the predictions derived from a trained GLM. We built a Neural Circuit Probe that is positioned above a multi- electrode array (MEA) and is capable of identifying the specific neuron from which an MEA signal arises and deliver a chemical reagent such as TTX to that specific neuron. In this manner, if the GLM predicts an inhibitory connection to another neuron we can directly validate the prediction. This level of neuronal connectivity prediction has not been previously accomplished. However, the GLM has inherent limitations mainly related to the assumptions required in by its computation. Among the most limiting assumptions is the necessity to set the amount of history used for the predictions. The use of Long short-term memory (LSTM) architecture represents a promising and incompletely explored approach for prediction of spikes. By training a deep neural network with many neural traces, the ÒmachineÓ will learn to recognize the patterns as has been demonstrated for other complex problems such as the board game GO that entail numerous instantiations inaccessible to simple rule-based analyses and storage of all possible configurations. LSTM is particularly wellÐsuited to classify, process and predict time series when, in contrast to the GLM, the time lags between events are very long and of unknown size. 2. Build a patterned neuronal network in which a discrete identified signal from one neuronal ensemble induces bursting and synchronization of a second neuronal ensemble. The input signal serves as a memory trigger analogous to a sensory input and the induced firing considered a memory trace or a perception. We will determine whether these traces will be useful as information packets capable of representing synthetic memories or perceptions in comparison to simulated traces in terms of total information content, energy consumption, efficiency and robustness. Machine performance whether it is a recognition task, or a robotic motor task or a game are all limited by their brittleness or the ability to perform within the very narrow confines of the specific task for which the machine was designed. A synthetic memory or perception or behavior is encoded as a set of waveforms. In an engineered system, distinct waveforms corresponding to a set of physical parametersÑacceleration, speed, and forceÑcan convey the kinetic aspect of touch. Neurons spontaneously connect in a manner that generates waveforms used for computational purposes according to a still obscure logic. Neural computation is energy efficient and robust; however, the basis for these properties remains inaccessible in part to due to methodological limitations for measuring these parameters in a living neural system.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 11, 2018
- Source ID
- W911NF1710093
Entities
People
- Ken Kosik
Organizations
- Army Contracting Command
- Office of the Secretary of Defense
- University of California, Santa Barbara