Conditioned responses in the reticular formation

Unit activity was recorded from the midbrain and pons of 40 freely moving rats in an appetitive classical conditioning situation. Responses to auditory stimuli were observed from 100 units before and during a conditioning procedure in which presentation of food occurred 1 sec after the onset of the auditory stimulus. Conditioned unit responses (i.e., spike rate accelerations or decelerations) were considered to be positive when 1) no similar responses appeared prior to conditioning, and 2) latencies were equal to or less than those of sensory responses derived from the inferior colliculus. Such short latency conditioned unit responses were recorded from 11 probes located in the mid-lateral pert of the ventral region of the brain stem. This region was differentiated from paramedian, far lateral and dorsal parts of the brain stem reticular formation. Conditioned unit responses of considerably longer latencies were recorded from 76 probe located in these other regions. Among the longer latency responses interesting differences appeared in experiments conducted after the first conditioning series was completed. With additional training, units in the "reticular activating system" of midbrain and pons tended to yield stabilized responses in the early portion of the CS-US interval closely related in time to the orientation responses evoked by the CS. In contrast, the responses of units in the limbic midbrain tended to stabilize in the later part of the CS-US interval closely related in time to preparatory responses tied to the US. During extinction when the auditory stimulus was no longer followed by presentation of food, many of the responses were reduced to their pre-conditioning levels. However, there was a tendency for units which had displayed short latency responses on the first conditioning day to be more resistant to extinction than units which had displayed longer latency conditioned responses. The data were interpreted as indicating a local correlate of learning in the reticular formation of midbrain end pons and a separation of the midbrain system into at least two areas: 1) the classical "reticular activating system" related to orienting reactions, and 2) the limbic midbrain areas related to drives and rewards. Because the ventral and mid-lateral area with very short latency conditioned responses was not clearly tied to either of these; it was considered as possibly representing a third division.

The Physiology and Computation of Pyramidal Neurons

A variety of neural signals have been measured as correlates to consciousness. In particular, late current sinks in layer 1, distributed activity across the cortex, and feedback processing have all been implicated. What are the physiological underpinnings of these signals? What computational role do they play in the brain? Why do they correlate to consciousness? This thesis begins to answer these questions by focusing on the pyramidal neuron. As the primary communicator of long-range feedforward and feedback signals in the cortex, the pyramidal neuron is set up to play an important role in establishing distributed representations. Additionally, the dendritic extent, reaching layer 1, is well situated to receive feedback inputs and contribute to current sinks in the upper layers. An investigation of pyramidal neuron physiology is therefore necessary to understand how the brain creates, and potentially uses, the neural correlates of consciousness. An important part of this thesis will be in establishing the computational role that dendritic physiology plays. In order to do this, a combined experimental and modeling approach is used.

This thesis beings with single-cell experiments in layer 5 and layer 2/3 pyramidal neurons. In both cases, dendritic nonlinearities are characterized and found to be integral regulators of neural output. Particular attention is paid to calcium spikes and NMDA spikes, which both exist in the apical dendrites, considerable distances from the spike initiation zone. These experiments are then used to create detailed multicompartmental models. These models are used to test hypothesis regarding spatial distribution of membrane channels, to quantify the effects of certain experimental manipulations, and to establish the computational properties of the single cell. We find that the pyramidal neuron physiology can carry out a coincidence detection mechanism. Further abstraction of these models reveals potential mechanisms for spike time control, frequency modulation, and tuning. Finally, a set of experiments are carried out to establish the effect of long-range feedback inputs onto the pyramidal neuron. A final discussion then explores a potential way in which the physiology of pyramidal neurons can establish distributed representations, and contribute to consciousness.

Functional, clustered, feedforward, and mesoscale brain networks

The brain is a network spanning multiple scales from subcellular to macroscopic. In this thesis I present four projects studying brain networks at different levels of abstraction. The first involves determining a functional connectivity network based on neural spike trains and using a graph theoretical method to cluster groups of neurons into putative cell assemblies. In the second project I model neural networks at a microscopic level. Using diferent clustered wiring schemes, I show that almost identical spatiotemporal activity patterns can be observed, demonstrating that there is a broad neuro-architectural basis to attain structured spatiotemporal dynamics. Remarkably, irrespective of the precise topological mechanism, this behavior can be predicted by examining the spectral properties of the synaptic weight matrix. The third project introduces, via two circuit architectures, a new paradigm for feedforward processing in which inhibitory neurons have the complex and pivotal role in governing information flow in cortical network models. Finally, I analyze axonal projections in sleep deprived mice using data collected as part of the Allen Institute's Mesoscopic Connectivity Atlas. After normalizing for experimental variability, the results indicate there is no single explanatory difference in the mesoscale network between control and sleep deprived mice. Using machine learning techniques, however, animal classification could be done at levels significantly above chance. This reveals that intricate changes in connectivity do occur due to chronic sleep deprivation.