Learning in Multi-Layer Networks of the Brain
By: Salomon Muller
Publisher: [publisher not identified]
Published: 2021
Place of publication: [New York, N.Y.?]
Language: eng
Description: Simple circuits perform simple tasks. Complex circuits can perform more complicated tasks. This is true for artificial circuits and for brain circuits. As is known from artificial networks, a complexity that makes circuits substantially more powerful is distributing learning across multiple layers. In fact, most brain circuits in vertebrate systems are multi-layer circuits (but for few that perform simple reflexes) in which learning is distributed across layers. Despite the crucial contribution of learning in middle layer neurons to the output of the circuits they are embedded in, there is little understanding of the principles defining this contribution. A very common feature in brain circuits is that middle layer neurons generate two types of signals, known as spikes. These middle layer neurons commonly have long dendrites where they generate dendritic spikes. As well, like most neurons, they generate axonal spikes near the cell body. Neurons exhibiting these two spike types include pyramidal cells in the neo-cortex and the hippocampus, the Purkinje cells in the cerebellum and many more.

In this thesis I study another circuit that contains middle layer neurons, the electrosensory lateral lobe (ELL) of the mormyrid fish. The ELL is a tractable brain circuit in which the middle layer neurons generate dendritic and axonal spikes. In this thesis I show that these spike types are not two different expressions of the same inputs. Rather, they have a symbiotic relationship. Instead of all inputs triggering both spikes, some inputs can selectively drive dendritic spikes. The dendritic spikes in return modify the synaptic strength of another set of inputs. The modified inputs are then transmitted to downstream neurons via the axonal spikes, which contributes a desired signal to the output of the circuits. Effectively there is a separation of learning and signaling in the middle layer neurons through the two spike types. Having two types of spikes in the same neuron doing different computations enormously expands the computational power of the neuron. But, being in the same neuron means the separation of function is constrained and needs to be supported by biophysical principles.

I have thus built a biophysical model to understand the biophysical principles underlying the separation of function. I show that in the middle layer neurons of the ELL, the axonal spikes are strongly reduced in amplitude as they backpropagate to the apical dendrites, yet they remain crucial in driving dendritic spikes. Critically, modulation of inhibitory inputs can selectively dial up or down the ability of the backpropagating axonal spikes to drive dendritic spikes. Thus, a set of inhibitory modulating inputs can selectively modulate dendritic spikes. Having learning in different layers contributing to the outcome of the circuit, naturally leads to asking how the work is divided across layers and neuron types within the circuit. In this thesis I answer this question in the context of the outcome of the ELL circuit. Finally, another signature of a complex circuit is the ability to integrate many different inputs, usually in middle layer neurons, to generate sophisticated outputs. A goal for scientists studying systems neuroscience is to understand how this integration works.

In this thesis I provide a coherent model of a learning behavior called vestibulo occular reflex (VOR) adaptation, that depends on the integration of separate inputs to yield a learned behavior. The VOR is a simple reflex generated in the brain stem. Inputs from the brain stem are also sent to an area in the cerebellar cortex called the flocculus. The flocculus also receives another set of inputs that generate a different behavior, called smooth-pursuit. The integration of VOR inputs with smooth-pursuit inputs in the flocculus generate VOR adaptation. Understanding complex circuits is one of the greatest challenges for today's neuroscientists. In this thesis I tackle two such circuits and hope that through a better understandings of these circuits we gain principles that apply to other circuits and thereby advance our understanding of the brain.
Notes: Department: Biological Sciences.

Thesis advisor: Laurence F. Abbott.

Thesis (Ph.D.)--Columbia University, 2021.