Thomas B. DeMarse

A new approach to neural cell culture for long-term studies

We have developed a new method for culturing cells that maintains their health and sterility for many months. Using conventional techniques, primary neuron cultures seldom survive more than 2 months. Increases in the osmotic strength of media due to evaporation are a large and underappreciated contributor to the gradual decline in the health of these cultures. Because of this and the ever-present likelihood of contamination by airborne pathogens, repeated or extended experiments on any given culture have until now been difficult, if not impossible. We surmounted survival problems by using culture dish lids that form a gas-tight seal, and incorporate a transparent hydrophobic membrane (fluorinated ethylene-propylene) that is selectively permeable to oxygen (O(2)) and carbon dioxide (CO(2)), and relatively impermeable to water vapor. This prevents contamination and greatly reduces evaporation, allowing the use of a non-humidified incubator. We have employed this technique to grow dissociated cortical cultures from rat embryos on multi-electrode arrays. After more than a year in culture, the neurons still exhibit robust spontaneous electrical activity. The combination of sealed culture dishes with extracellular multi-electrode recording and stimulation enables study of development, adaptation, and very long-term plasticity, across months, in cultured neuronal networks. Membrane-sealed dishes will also be useful for the culture of many other cell types susceptible to evaporation and contamination.

The Neurally Controlled Animat: Biological Brains Acting with Simulated Bodies

The brain is perhaps the most advanced and robust computation system known. We are creating a method to study how information is processed and encoded in living cultured neuronal networks by interfacing them to a computer-generated animal, the Neurally-Controlled Animat, within a virtual world. Cortical neurons from rats are dissociated and cultured on a surface containing a grid of electrodes (multi-electrode arrays, or MEAs) capable of both recording and stimulating neural activity. Distributed patterns of neural activity are used to control the behavior of the Animat in a simulated environment. The computer acts as its sensory system providing electrical feedback to the network about the Animats movement within its environment. Changes in the Animats behavior due to interaction with its surroundings are studied in concert with the biological processes (e.g., neural plasticity) that produced those changes, to understand how information is processed and encoded within a living neural network. Thus, we have created a hybrid real-time processing engine and control system that consists of living, electronic, and simulated components. Eventually this approach may be applied to controlling robotic devices, or lead to better real-time silicon-based information processing and control algorithms that are fault tolerant and can repair themselves.

MeaBench: A toolset for multi-electrode data acquisition and on-line analysis

We present a software suite, MeaBench, for data acquisition and online analysis of multi-electrode recordings, especially from micro-electrode arrays. Besides controlling data acquisition hardware, MeaBench includes algorithms for real-time stimulation artifact suppression and spike detection, as well as programs for online display of voltage traces from 60 electrodes and continuously updated spike raster plots. MeaBench features real-time output streaming, allowing easy integration with stimulator systems. We have been able to generate stimulation sequences in response to live neuronal activity with less than 20 ms lag time. MeaBench is open-source software, and is available for free public download at http://www.its.caltech.edu/~pinelab/wagenaar/meabench.html

Removing Some ‘A’ from AI: Embodied Cultured Networks

We embodied networks of cultured biological neurons in simulation and in robotics. This is a new research paradigm to study learning, memory, and information processing in real time: the Neurally-Controlled Animat. Neural activity was subject to detailed electrical and optical observation using multi-electrode arrays and microscopy in order to access the neural correlates of animat behavior. Neurobiology has given inspiration to AI since the advent of the perceptron and consequent artificial neural networks, developed using local properties of individual neurons. We wish to continue this trend by studying the network processing of ensembles of living neurons that lead to higher-level cognition and intelligent behavior.

Causal Measures of Structure and Plasticity in Simulated and Living Neural Networks

A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a networks structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify “causal” relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time.

Closing the Loop: Stimulation Feedback Systems for Embodied MEA Cultures

By combining MEA electrophysiology with long-term time-lapse imaging, it is possible to make correlations between changes in network function and changes in neuronal morphology. By re-embodying dissociated cultured networks, network function can be mapped onto behavior, and in vitro research can now make use of a new kind of behavioral studies that include detailed (submicron) imaging not possible in vivo. By closing the sensory-motor loop around MEA cultures, they are more likely to shed light on the mechanisms of learning, memory, and information processing in animals.

Poly-HEMA as a drug delivery device for in vitro neural networks on micro-electrode arrays.

Delivery of pharmacological agents in vitro can often be a difficult, time consuming and costly process. In this paper, we describe an economical method for in vitro delivery using a hydrogel of poly hydroxyethyl methacrylate (PHEMA) that can absorb up to 50% of its weight of any water-solubilized pharmacological agent. This agent will then passively diffuse into surrounding media upon application in vitro. An in vitro test of PHEMA as a drug delivery device was conducted using dissociated rat-cortical neurons cultured on micro-electrode arrays. These micro-electrode arrays permit the real-time measurement of neural activity at 60 different sites across a network of neurons. Neural activity was compared during the application of PHEMA saturated with cell culture media and PHEMA saturated with bicuculline, a widely used pharmacological agent with stereotypical effects on neural activity patterns. Application of PHEMA saturated with bicuculline produced a gradual increase in concentration in vitro. When the minimum effective concentration of bicuculline was reached, which was found to be 0.59 microM using the diffusion properties of PHEMA, it produced the rapid almost periodic synchronized bursting characteristically associated with this agent. In contrast, the application of PHEMA saturated in culture media alone had no effect on neural activity reinforcing its inherent inert properties. Since PHEMA is nontoxic, can be molded into a variety of shapes, quickly manufactured in any laboratory and is inexpensive to produce, the material represents a promising alternative to drug delivery systems on the market today.

Granger causality relationships between local field potentials in an animal model of temporal lobe epilepsy.

An understanding of the in vivo spatial emergence of abnormal brain activity during spontaneous seizure onset is critical to future early seizure detection and closed-loop seizure prevention therapies. In this study, we use Granger causality (GC) to determine the strength and direction of relationships between local field potentials (LFPs) recorded from bilateral microelectrode arrays in an intermittent spontaneous seizure model of chronic temporal lobe epilepsy before, during, and after Racine grade partial onset generalized seizures. Our results indicate distinct patterns of directional GC relationships within the hippocampus, specifically from the CA1 subfield to the dentate gyrus, prior to and during seizure onset. Our results suggest sequential and hierarchical temporal relationships between the CA1 and dentate gyrus within and across hippocampal hemispheres during seizure. Additionally, our analysis suggests a reversal in the direction of GC relationships during seizure, from an abnormal pattern to more anatomically expected pattern. This reversal correlates well with the observed behavioral transition from tonic to clonic seizure in time-locked video. These findings highlight the utility of GC to reveal dynamic directional temporal relationships between multichannel LFP recordings from multiple brain regions during unprovoked spontaneous seizures.

Liquid state machines and cultured cortical networks: the separation property.

In vitro neural networks of cortical neurons interfaced to a computer via multichannel microelectrode arrays (MEA) provide a unique paradigm to create a hybrid neural computer. Unfortunately, only rudimentary information about these in vitro networks computational properties or the extent of their abilities are known. To study those properties, a liquid state machine (LSM) approach was employed in which the liquid (typically an artificial neural network) was replaced with a living cortical network and the input and readout functions were replaced by the MEA-computer interface. A key requirement of the LSM architecture is that inputs into the liquid state must result in separable outputs based on the liquids response (separation property). In this paper, high and low frequency multi-site stimulation patterns were applied to the living cortical networks. Two template-based classifiers, one based on Euclidean distance and a second based on a cross-correlation were then applied to measure the separation of the input-output relationship. The result was over a 95% (99.8% when nonstationarity is compensated) input reconstruction accuracy for the high and low frequency patterns, confirming the existence of the separation property in these biological networks.

Toward a self-wired active reconstruction of the hippocampal trisynaptic loop: DG-CA3

The mammalian hippocampus functions to encode and retrieve memories by transiently changing synaptic strengths, yet encoding in individual subregions for transmission between regions remains poorly understood. Toward the goal of better understanding the coding in the trisynaptic pathway from the dentate gyrus (DG) to the CA3 and CA1, we report a novel microfabricated device that divides a micro-electrode array into two compartments of separate hippocampal network subregions connected by axons that grow through 3 × 10 × 400 μm tunnels. Gene expression by qPCR demonstrated selective enrichment of separate DG, CA3, and CA1 subregions. Reconnection of DG to CA3 altered burst dynamics associated with marked enrichment of GAD67 in DG and GFAP in CA3. Surprisingly, DG axon spike propagation was preferentially unidirectional to the CA3 region at 0.5 m/s with little reverse transmission. Therefore, select hippocampal subregions intrinsically self-wire in anatomically appropriate patterns and maintain their distinct subregion phenotype without external inputs.