Neural circuitry

The hippocampus, a brain region that is part of the limbic system in the medial temporal lobe, is critical to episodic memory, or the memory of autobiographical events. The hippocampus plays an important role in the consolidation of information from short-term memory into more permanent long-term memory and spatial memory which enables navigation. Hippocampal damage in humans has been linked to memory loss, such as in Alzheimer’s disease and other dementias, as well as in amnesia such as in the case of patient H.M. The role of the hippocampus has been well characterized in humans but is less understood in rodents due to contradictory findings. While rodents have served well as model organisms in developing our understanding of the cognitive map that is critical for spatial navigation, there has been substantial contention over the degree to which the rodent hippocampus supports non-spatial memory, specifically the memory for items or objects previously encountered. The overall objective of this research is to gain a better understanding of how neuronal circuits involving the hippocampus and perirhinal cortex function to support object memory in the brain. Chemogenetic technologies such as DREADDs (designer receptor exclusively activated by designer drugs) have proven to be effective tools in remote manipulation of neuronal activity. First, a series of behavioral tasks was used to validate the effects of DREADD inactivation in the CA1 region of dorsal hippocampus in C57BL/6J male mice. DREADD inhibition resulted in significant impairment in the spontaneous object recognition (SOR) task and of spatial memory in the Morris water maze. In conjunction, mice were implanted with bilateral perirhinal cortex guide cannulae to allow for temporary muscimol inactivation during distinct time points in the SOR task to further investigate the nature of its relationship with the hippocampus. The results reveal an unexpected role for the perirhinal cortex in the retrieval of strong object memory. Finally, Arc mRNA expression was quantified in CA1 of dorsal hippocampus and perirhinal cortex following both weak and strong object memory formation. The results indicate that the perirhinal cortex and hippocampus have distinct, yet complementary roles in object recognition memory and that distinction is gated by memory strength. Understanding the neural mechanisms supporting the weak-strong object memory distinction in mice is an important step not only in validating mice as a suitable model system to study episodic memory in humans, but also in developing treatments and understanding the underlying causes of diseases affecting long-term memory such as Alzheimer’s disease.

The physical architecture of neural circuits is thought to underlie the computations that give rise to higher order feature sensitivity in the neocortex. Recent technological breakthroughs have allowed the structural and functional investigation of the basic computational units of neural circuits; individual synaptic connections. However, it remains unclear how cortical neurons sample and integrate the thousands of synaptic inputs, supplied by different brain structures, to achieve feature selectivity. Here, I first describe how visual cortical circuits transform the elementary inputs supplied by the periphery into highly diverse, but well-organized, feature representations. By combining and optimizing newly developed techniques to map the functional synaptic connections with defined sources of inputs, I show that the intersection between columnar architecture and dendritic sampling strategies can lead to the selectivity properties of individual neurons: First, in the canonical feedforward circuit, the basal dendrites of a pyramidal neuron utilize unique strategies to sample ON (light increment) and OFF (light decrement) inputs in orientation columns to create the distinctive receptive field structure that is responsible for basic sensitivity to visual spatial location, orientation, spatial frequency, and phase. Second, for long-range horizontal connections, apical dendrites unbiasedly integrate functionally specialized and spatially targeted inputs in different orientation columns, which generates specific axial surround modulation of the receptive field.

The human brain consists of billions of neurons and these neurons pool together
in groups at different scales. On one hand, these neural entities tend to behave
as single units and on the other hand show collective macroscopic patterns of activity.
The neural units communicate with each other and process information over time.
This communication is through small electrical impulses which at the macroscopic
scale are measurable as brain waves. The electric field that is produced collectively
by macroscopic groups of neurons within the brain can be measured on the surface
of the skull via a brain imaging modality called Electroencephalography (EEG). The
brain as a neural system has variant connection topology, in which an area might not
only be connected to its adjacent neighbors homogeneously but also distant areas can
directly transfer brain activity [16]. Timing of these brain activity communications
between different neural units bring up overall emerging spatiotemporal patterns.
The dynamics of these patterns and formation of neural activities in cortical surface
is influenced by the presence of long-range connections between heterogeneous neural
units. Brain activity at large-scale is thought to be involved in the information processing
and the implementation of cognitive functions of the brain. This research
aims to determine how the spatiotemporal pattern formation phenomena in the brain
depend on its connection topology. This connection topology consists of homogeneous
connections in local cortical areas alongside the couplings between distant functional
units as heterogeneous connections. Homogeneous connectivity or synaptic weight
distribution representing the large-scale anatomy of cortex is assumed to depend on
the Euclidean distance between interacting neural units. Altering characteristics of
inhomogeneous pathways as control parameters guide the brain pattern formation
through phase transitions at critical points. In this research, linear stability analysis
is applied to a macroscopic neural field in a one-dimensional circular and a twodimensional
spherical model of the brain in order to find destabilization mechanism
and subsequently emerging patterns.

It is of interest to understand how new neurons incorporate themselves into the
existing circuitry of certain neuronal populations. One such population of neurons is that
which are born in the subventricular zone (SVZ) and migrate to the olfactory bulb where
they differentiate into granule cells. Another area of interest is the role of brain-derived
neurotrophic factor (BDNF) on the survival and overall health of these neurons. This
study aimed to test whether or not BDNF is a survival factor for adult-born granule cells.
Here were utilized a transgenic mouse model over-expressing BDNF under the α-
calcium/calmodulin-dependent protein kinase II (CAMKIIα) promoter, and tested its
effect on olfactory granule cells under sensory deprived conditions. Results from this
experiment indicated that there was no significant difference in cell death or cell survival when comparing transgenic and wild type animals. We concluded that BDNF is not a
survival factor for adult-born granule cells.

Rats with lesions of the ventromedial aspect of the internal
capsule in the vicinty of the entopeduncular nucleus (EP) showed a loss
of forelimb placing (chin, contact and visual) in the contralateral
limb. Spreading depression induced by instillation of KCl (25%) to the
cortex contralateral to the lesion brought back placing in the affected
limb and abolished placing in the normal limb. Within 24 hours the
pre-spreading depression state returned and the impaired limb no longer
placed while the normal limb recovered function. In contrast, KCl on
the ipsilateral cortex did not reinstate placing. These results
suggest that the loss of placing following lesions of the EP are due to
tonic inhibition from the cortex contralateral to the lesion. Sensory
summation was evident during the early recovery period when placing was
accomplished only if two kinds of stimuli were provided
simultaneously. Forelimb placing recovered to its pre-lesion state.

Recently, Artificial Neural Network (ANN) computing systems have become one of the most active and challenging areas of information processing. The successes of experimental neural computing systems in the fields of pattern recognition, process control, robotics, signal processing, expert system, and functional analysis are most promising. However due to a number of serious problems, only small size fully connected neural networks have been implemented to run in real-time. The primary problem is that the execution time of neural networks increases exponentially as the neural network's size increases. This is because of the exponential increase in the number of multiplications and interconnections which makes it extremely difficult to implement medium or large scale ANNs in hardware. The Modular Grouped Weight Quantization (MGWQ) presented in this dissertation is an ANN design which assures that the number of multiplications and interconnections increase linearly as the neural network's size increases. The secondary problems are related to scale-up capability, modularity, memory requirements, flexibility, performance, fault tolerance, technological feasibility, and cost. The MGWQ architecture also resolves these problems. In this dissertation, neural network characteristics and existing implementations using different technologies are described. Their shortcomings and problems are addressed, and solutions to these problems using the MGWQ approach are illustrated. The theoretical and experimental justifications for MGWQ are presented. Performance calculations for the MGWQ architecture are given. The mappings of the most popular neural network models to the proposed architecture are demonstrated. System level architecture considerations are discussed. The proposed ANN computing system is a flexible and a realistic way to implement large fully connected networks. It offers very high performance using currently available technology. The performance of ANNs is measured in terms of interconnections per second (IC/S); the performance of the proposed system changes between 10^11 to 10^14 IC/S. In comparison, SAIC's DELTA II ANN system achieves 10^7. A Cray X-MP achieves 5*10^7 IC/S.

Single unit activity from the anterior thalamus (AT) was recorded in order to investigate discharge profiles during desynchronized (large amplitude irregular activity (LIA)), and synchronized (theta rhythm) patterns of the hippocampal EEG. Units were recorded in urethane-anesthetized rats in the anteroventral (AV, n = 96), the anterodorsal (AD, n = 44) and the anteromedial (AM, n = 48) thalamic nuclei. The majority of the units (n = 164, 87%) were theta-on and a small group (n = 24, 13%) was theta-off. Theta-off cells were found in AD and AM nuclei but not in AV. Theta-on cells increased their discharge in presence of hippocampal theta. Mean discharge rate was 6.0 +/- 0.52 Hz and 14.48 +/- 0.96 Hz for AV theta-on cells during control and theta states, 4.43 +/- 0.52 Hz and 10.05 +/- 1.28 Hz for AD theta-on cells, and 2.60 +/- 0.3 Hz and 6.42 +/- 0.9 Hz for AM theta-on cells, respectively. We found that 40% of AV cells showed a rhythmic pattern that peaked significantly at 250--270 ms during theta, 21.9% of AD units and only 5.7% for AM units showed a rhythmic pattern. The majority of AT cells showed unit-theta phase-locked EEG oscillations in the crosscorrelogram, indicating that in spite of low rhythmicity most units firing were modulated at theta frequency. The coherence measured by spectral analysis between unit firing and hippocampal theta was statistically significant in 75% of cases. The anatomical distribution of the cells shows that coherence values were widely distributed across the anterior thalamus. In addition, the particular contribution of this diencephalic structure during theta was determined by applying measures of information flow in the neural circuit of Papez. Partial coherence (PC) analysis together with the computation of causality measures (DTF and DC) was used to study such interaction among AV, retrosplenial cortex and hippocampus. PC analysis revealed hippocampus as the synchronizing structure for rhythmic AV cells and retrosplenial cortex. A link between hippocampus and retrosplenial cortex was found for the non-rhythmic AV group. The DTF analysis showed flow of propagation from AV to hippocampus, hippocampus to retrosplenial cortex and AV to retrosplenial cortex for both groups. The strength of connection changed depending on the state of the animal. Behaviors that have been particularly related to the hippocampal theta activity refer mainly to learning and memory. Activation of large numbers of septo-hippocampal neurons during the generation of the theta rhythm has been proposed as a 'natural tetanizer'. Numerous cellular studies have linked long-term potentiation (LTP) and the hippocampal theta rhythm. The role of theta in memory has been evidenced through lesion studies in animals. Some observations in humans have proposed the anterior thalamus as pivotal for spatial memory. Perhaps the cellular theta activity found in AV plays an important role in the generation and control of the hippocampal theta rhythm and hence in memory and learning.