Recording Neural Network Activity

Before any determination of neural connectivity or neural data encoding scheme can be attempted it is first necessary to record the activity of a neural network. This section examines the physical challenges associated with the recording of neural network activity.

The typical human brain cerebral cortex contains some 200 billion individual neurons that have established 125 trillion synapses (Micheva KD et al., 2010). For all practical purposes it is impossible to record the activity of the entire cerebral cortex given the limits of current technology. Despite this limitation modern technology does allow for the examination of the activity in the neural network on a smaller scale. If useful information is to be derived it is important to record the activity of as many neurons as possible at the same time. This has given rise to neural network recordings known as “Multiple simultaneously recorded neural spike trains”.

3.5.1 The Multi Electrode Array (MEA’s)

The spiking activity of a neural network can be detected using electrodes inserted into the tissue of the neural network. Electrodes are usually arranged into an “array” that records activity in a particular area of the network (see Figure 3-5 and Figure 3-6). The electrodes when placed close to a neuron’s soma or axon transduce the electrical charge of an action potential (spike event) recording the firing of the neuron. Multi electrode arrays generally come in two classes.

i. The in vitro (Latin: within glass) array and ii. The in vivo (Latin: within the living) array.

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The in vitro array (Figure 3-5) is used to study neural network samples that have been removed from their usual biological setting such as brain slices. The primary benefit of in vitro work is that the system under study is greatly simplified being detached from the immense complexity of a living organism. This allows for detailed study of a small number of components (Vignais & Vignais, 2010). The primary drawback to such studies is that it is difficult to extrapolate its results back to the intact organism (Rothman, 2002).

The in vivo array (Figure 3-6) is implanted into a living organism and permits study of the activity within the neural network in its natural setting.

Figure 3-5: An in vitro MEA array. (Potter, 2010) Figure 3-6: An in vivo MEA array. (Normann, 1993)

The simultaneous recording of multiple neurons is not, however, as simple as inserting an array into a tissue sample or live animal. Each electrode has the potential, on average, to record spiking events from over a 1000 neurons situated within 140 µm of the electrode (Buzsaki, 2004). A recording of the electrical activity from the electrode will, therefore, represent the sum of all spiking events from an unknown number of neurons within ~140 µm of the electrode. To be useful the recorded signal must be analysed, individual neurons identified and the recorded spiking events assigned to neurons. This task is complicated by the fact that all spiking events appear in the recording to have the same characteristics. The process is termed spike sorting with each spiking event be identified and then assigned to a neuron that produced it.

The use of an array of electrodes or sometimes the division of the electrode tip into multiple tips (a tetrode) allows this to occur through a process of triangulation with different electrodes (or tips) receiving the same signal at different strengths. The varying signal strength provides the basis for measuring the distance to the pre- synaptic neuron while the physical location of the receiving electrode in the array will provide the basis for triangulation. Figure 3-7 illustrates the principle. Despite the application of these techniques there remains no universally

Figure 3-7: MEA electrode recording from a neural network. Source (Buzsaki, 2004)

Chapter 3: Neuroscience

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agreed algorithm for spike sorting with different algorithms’ pre-synaptic different results from the same input data (Brown, Kass & Mitra, 2004). Development of an effective spike sorting techniques remains a challenge for neuroscience that places considerable constraints on recording multiple simultaneously generated spike trains. The practical impact of this is that the typical number of reliably recorded spike trains per an electrode of the recording array is markedly below the theoretical limit of ~1000 neurons. Nevertheless work continues in this field and the last few years have seen a marked growth in the number of simultaneously recordable spike trains. It is a reasonable expectation that this trend will continue in the near future as the number and sensitivity of electrodes in recording arrays increases and superior spike sorting algorithms are developed.

3.5.2 The Data Explosion in Neural Science

Given the historical limitations on spike sorting algorithms’ discussed above the number of simultaneously recorded neural spike trains has been small, typically in the hundreds of neurons range. However as technology has advanced more modern recording equipment is now able to identify thousands of neurons in a typical recording session (Taketani & Baudry, 2006). While even this is significantly below the theoretical maximum it still represents a flood of data that requires significant processing power to analyse. While the computer provides the power to record this mass of data the development of software to analyse it has not kept pace with the ability to record the data. This is a trend that has been seen in many areas of science due to the fast pace of technological change in the information technology field (Ward & Barker, 2013). In addition to this Brown et al, believe that “Multiple spike trains are multivariate point processes, yet research in statistics and signal processing on multivariate point process models has not been nearly as extensive as research on models of multivariate continuous-valued processes” (Brown, Kass & Mitra, 2004). Brown also observes that such analysis techniques as are available tend to restrict themselves to analysing neuron pairs rather than considering the wider connection network. As a further analysis failing Brown identifies that neural plasticity “makes non-stationarity in neural data a rule rather than an exception”. Despite this there is a lack of “explicit adaptive estimation algorithms to track these dynamics for multivariate point processes”. This lack of proven analysis methods serves to hold back progress despite the wealth of data now being recorded.