In medicine, brain-computer interfaces are used for the development of neuroprostheses comparable to healthy organs in their responsiveness to user's mental commands [1, 2, 3]. Data can also be transmitted in the reverse direction, from the computer to the brain; for example, lost sensory functions, such as auditory [4] and visual [5], can be recovered using electrical stimulation. However, what’s more challenging for a researcher is transmission of information as such, i. e., knowledge and experience. It has been shown that using multi-input/multi-output non-linear dynamic model allows for transmitting a certain spatiotemporal pattern detected in the hippocampus of one rat to the hippocampus of another rat, which leads to the statistically reliable alterations in the behavior of the second animal [6].

It is known that neuronal networks that form a basis for cognitive activity do not have a distinct location in brain structures, but are distributed throughout them [7, 8]. From that, a need to reprogram neuronal networks ensues. Publications on the patterns patterns of neuronal network spontaneous activity in vitro and on the methods of its external modification [9, 10, 11, 12, 13] prove that networks of dissociated primary neuronal cultures of cortical and hippocampal cells on multi-electrode arrays (MEAs) can control external stimulation by changing their activity on the selected experimental electrode. Such neuronal network learning was first demonstrated by Shahaf and Marom [14] and was successfully reproduced by other researchers thereafter [15, 16, 17, 18].

As the network is gradually developing from the dissociated neuronal culture, it starts to exhibit spontaneous bioelectrical activity recorded by array electrodes. Thus, in the absence of external stimulation in the first days of culture growth, only single action potentials are registered, but after a while they cluster into bursts [19]. In this work we hypothesize that spontaneous burst activity comes down to a small number of stereotypical patterns, and cluster analysis can help identify one or several dominating patterns. Since the external stimulation breaks the existing activation sequence, the network changes its activity pattern to switch off stimulation and go back to the typical pattern. Then, cluster analysis performed after the stimulation (training) can detect the resumption of the initial activity pattern.

METHODS

Cell cultures

Primary cell culture was prepared from the hippocampal tissue of newborn rats of C57BL/6 breed. Experimental animals used in the study were managed according to the guidelines specified in Order no. 267 of the Ministry of the Russian Federation, dated June 19, 2003, “On the approval of rules for good laboratory practice”. The experiment was approved by the local Ethics Committee for Biomedical Research of the National Research Center “Kurchatov Institute” (protocol no. 1, dated July 9, 2015).

Cells were cultured on 60-channel multi-electrode arrays 60StimMEA200/30-ITO (Multichannel Systems, Germany). Prior to the experiment, plates were coated with poly-L-lysine for better cell adhesion. The initial culture density was 300,000 cells per mm³. Their dissociation was achieved by using 0.25 % trypsin (Invitrogen 25200-056, USA). Neuronal viability was maintained in NeurobasalTM culture medium (Invitrogen 21103-049) in the complex with the bioactive additive В27 (Invitrogen 17504-044), glutamin (Invitrogen 25030-024) and penicillin-streptomycin (Life Technologies 15140122, USA)

in GALAXY 170S incubator (New Brunswick Scientific, USA) under stable conditions: temperature of 37  ̊C, humidity of 100 % and 5 % CO₂ air concentration. Glial cell growth was not inhibited, because glial cells were necessary to ensure long-term culture viability in vitro. Half of the medium volume was replaced every three days.

Bioelectrical cell activity was recorded with MEA1060- Up-BC-Standard system (Multichannel Systems). For data acquisition, the bundled software was used.

Dynamics of spontaneous burst activity were observed in two cultures. Recording was performed from the 4th day in vitro (DIV) until culture death. For further processing, we used data obtained in the interval between the onset and offset of burst activity. For training, one 24-DIV culture was used.

Protocol of neuronal culture training in vitro

1) 1-hour background recording of culture bioelectrical activity.

2) Stimulation by single bipolar rectangular pulses of ± 300 mV on each electrode (one at a time) to select the electrode that evoked the most intense culture response.

3) 5 stimulation cycles on the electrode selected in step 2. Every cycle consisted of a 5-minute series of rectangular pulses with 2-minute breaks. In every experiment, the pause between the pulses was adjusted so that every pulse could induce burst activity. Based on stimulation results, a recording electrode was chosen, for which electrical activity within 30–80 ms after the signal was the lowest.

4) 1-hour recording of culture spontaneous bioelectrical activity.

5) 20 training cycles. Training was considered successful if a twofold increase in the probability to record spike activity within the preset time interval on the electrode chosen in step 3 was observed. Training consisted of stimulation described in paragraph 3, given that as soon as the success criterion had been reached, stimulation was discontinued and a 2-minute break was provided.

6) 1-hour recording of culture spontaneous bioelectrical activity.

Detection of spikes and burst events

The initial signal was digitally processed by a second-order high-pass Butterworth filter with a passband of over 200 Hz, which allowed for the exclusion of low-frequency noise. Action potentials were detected if signal amplitude exceeded 4 standard deviations. In that case, the maximum amplitude was considered time of spike onset.

A burst event (burst) occurs on one electrode and is characterized by a short-term explosion-like impulse generation (0.1–3.0 s depending on culture age and its stocking density), and is usually accompanied by the low-frequency (1–5 Hz) signal component. Detection of bursts was based on the identification of the low-frequency component in a given interval and on spike detection in the vicinity of the component. Time when the first and the last spikes were generated was considered the burst onset and offset, respectively.

Population burst events are bursts that are observed simultaneously (with small delays of about 0.002–0.05 s) on more than a half of all active electrodes. The onset of the population burst is time of the first burst event onset.