Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks.

Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura's and van Rossum's spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous synchronized network burst events that propagated between layers and highlight the potential applications of these MEMs devices as a tool for further investigation of structure and functional dynamics among neural populations

Spatio-temporally efficient coding: A computational principle of biological neural networks

Department of Biomedical Engineering (Human Factors Engineering)One of the major goals of neuroscience is to understand how the external world is represented in the brain. This is a neural coding problem: the coding from the external world to its neural representations. There are two different kinds of problems with neural coding. One is to study the types of neuronal activity that represent the external world. Representative examples here are rate coding and temporal coding. In this study, we will present the spike distance method that reads temporal coding-related information from neural data. Another is to study what principles make such neural representations possible. This is an approach to the computational principle and the main topic of the present study. The brain sensory system has hierarchical structures. It is important to find the principles assigning functions to the hierarchical structures. On the one hand, the hierarchical structures of the brain sensory system contain both bottom-up and top-down pathways. In this bidirectional hierarchical structure, two types of neuronal noise are generated. One of them is noise generated as neural information fluctuates across the hierarchy according to the initial condition of the neural response, even if the external sensory input is static. Another is noise, precisely error, caused by coding different information in each hierarchy because of the transmission delay of information when external sensory input is dynamic. Despite these noise problems, it seems that sensory information processing is performed without any major problems in the sensory system of the real brain. Therefore, a neural coding principle that can overcome these noise problems is neededHow can the brain overcome these noise problems? Efficient coding is one of representative neural coding principles, however, existing efficient coding does not take into account these noise problems. To treat these noise problems, as one of efficient coding principles, we devised spatio-temporal efficient coding, which was inspired by the efficient use of given space and time resources, to optimize bidirectional information transmission on the hierarchical structures. This optimization is to learn smooth neural responses on time domain. In simulations, we showed spatio-temporal efficient coding was able to solve above two noise problems. We expect that spatio-temporal efficient coding helps us to understand how the brain computes.ope

Discrimination of behaviorally relevant signals by auditory receptor neurons

In hearing insects, discrimination of auditory signals is vital to mate selection. We tested the capability of single auditory receptor neurons to discriminate calling songs from di!erent individual grasshoppers of the same species. Spike trains elicited from di!erent songs were studied using the metric-space analysis of Victor and Purpura (Networks: Comput. Neural Syst. 8 (1997) 127}164). Our data show that the natural songs can be distinguished perfectly after a mere 100 msec if spike trains are evaluated on time scales of about 10 msec. This time scale is well matched to the temporal structure of the grasshopper songs. � 2001 Elsevier Science B.V. All rights reserved. Keywords: Stimulus discrimination; Neural code; Auditory receptor; Metric-space analysi

New statistical methods to derive functional connectivity from multiple spike trains

Analysis of functional connectivity of simultaneously recorded multiple spike trains is one of the major issues in the neuroscience. The progress of the statistical methods to the analysis of functional connectivity of multiple spike trains is relatively slow. In this thesis two statistical techniques are presented to the analysis of functional connectivity of multiple spike trains. The first method is known as the modified correlation grid (MCG). This method is based on the calculation of cross-correlation function of all possible pair-wise spike trains. The second technique is known as the Cox method. This method is based on the modulated renewal process (MRP). The original paper on the application of the Cox method (Borisyuk et al., 1985) to neuroscience data was used to analyse only pairs and triplets of spike trains. This method is further developed in this thesis to support simultaneously recorded of any possible set of multiple spike trains. A probabilistic model is developed to test the Cox method. This probabilistic model is based on the MRP. Due to the common probabilistic basis of the probabilistic model and the Cox method, the probabilistic model is a convenient technique to test the Cox method. A new technique based on a pair-wise analysis of Cox method known as the Cox metric is presented to find the groups of coupled spike trains. Another new technique known as motif analysis is introduced which is useful in identifying interconnections among the spike trains. This technique is based on the triplet-wise analysis of the Cox method. All these methods are applied to several sets of spike trains generated by the Enhanced Leaky and Integrate Fire (ELIF) model. The results suggest that these methods are successful for analysing functional connectivity of simultaneously recorded multiple spike trains. These methods are also applied to an experimental data recorded from cat’s visual cortex. The connection matrix derived from the experimental data by the Cox method is further applied to the graph theoretical methods