Sparse and Specific Coding during Information Transmission between Co-cultured Dentate Gyrus and CA3 Hippocampal Networks

Abstract

To better understand encoding and decoding of stimulus information in two specific hippocampal sub-regions, we isolated and co-cultured rat primary dentate gyrus (DG) and CA3 neurons within a two-chamber device with axonal connectivity via micro-tunnels. We tested the hypothesis that, in these engineered networks, decoding performance of stimulus site information would be more accurate when stimuli and information flow occur in anatomically correct feed-forward DG to CA3 vs. CA3 back to DG. In particular, we characterized the neural code of these sub-regions by measuring sparseness and uniqueness of the responses evoked by specific paired-pulse stimuli. We used the evoked responses in CA3 to decode the stimulation sites in DG (and vice-versa) by means of learning algorithms for classification (support vector machine, SVM). The device was placed over an 8 × 8 grid of extracellular electrodes (micro-electrode array, MEA) in order to provide a platform for monitoring development, self-organization, and improved access to stimulation and recording at multiple sites. The micro-tunnels were designed with dimensions 3 × 10 × 400 μm allowing axonal growth but not migration of cell bodies and long enough to exclude traversal by dendrites. Paired-pulse stimulation (inter-pulse interval 50 ms) was applied at 22 different sites and repeated 25 times in each chamber for each sub-region to evoke time-locked activity. DG-DG and CA3-CA3 networks were used as controls. Stimulation in DG drove signals through the axons in the tunnels to activate a relatively small set of specific electrodes in CA3 (sparse code). CA3-CA3 and DG-DG controls were less sparse in coding than CA3 in DG-CA3 networks. Using all target electrodes with the three highest spike rates (14%), the evoked responses in CA3 specified each stimulation site in DG with optimum uniqueness of 64%. Finally, by SVM learning, these evoked responses in CA3 correctly decoded the stimulation sites in DG for 43% of the trials, significantly higher than the reverse, i.e., how well-recording in DG could predict the stimulation site in CA3. In conclusion, our co-cultured model for the in vivo DG-CA3 hippocampal network showed sparse and specific responses in CA3, selectively evoked by each stimulation site in DG.

Pattern separation and completion of distinct axonal inputs transmitted via micro-tunnels between co-cultured hippocampal dentate, CA3, CA1 and entorhinal cortex networks

“…The MEA60 amplifier recorded electrophysiological activity at constant temperature of 37°C and in a humidified atmosphere of 5% CO 2 and 9% O 2 (custom gas mixture, balance N 2 ; Airgas, Santa Ana, CA). Our previous works with two chambers (Brewer et al 2013; Poli et al 2017a) demonstrated not only excellent neuron survival equivalent to the original cell plating densities, but also strong axonal polarity consistent with the hippocampal anatomy. Polarity was determined from delay times as an axonal spike traveled from one electrode to the next in a micro-tunnel.…”

“…Furthermore, the micro-tunnels allowed robust axonal connection between chambers because of a strong dependence of the network functional strength of connectivity on the number of tunnels inputs (Pan et al 2015, DeMarse et al 2016). We applied paired-pulse stimulation at 22 electrode sites in this model system to promote native feed-forward information transmission, facilitating evoked network responses that correlated between the source and the axons in the tunnels, as well as between the transmitted axonal activity and target subregion (Poli et al 2017a). Pattern separation can be inferred when inputs are more similar than outputs.…”

Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics

“…During the last decade, new methods of neuroengineering have been developed to control the position of cells and direction of axon and dendrite growth (le Feber et al, 2015;Na et al, 2016;Renault et al, 2016). Recently, it has been shown that the main feature of functional network topology as unidirectional synaptic connectivity between cell clusters can also be engineered using microfluidic technology (Gladkov et al, 2017;Poli et al, 2017;Forró et al, 2018). Being implanted in the damaged brain, such tools of network structure manipulation allow one to mimic brain areas, which are involved in reflex activity, pattern retrieval in multilayered unidirectional network (Brewer et al, 2013;Poli et al, 2017) for neural tissue recovery from brain injury (Shimba et al, 2019).…”

“…Recently, it has been shown that the main feature of functional network topology as unidirectional synaptic connectivity between cell clusters can also be engineered using microfluidic technology (Gladkov et al, 2017;Poli et al, 2017;Forró et al, 2018). Being implanted in the damaged brain, such tools of network structure manipulation allow one to mimic brain areas, which are involved in reflex activity, pattern retrieval in multilayered unidirectional network (Brewer et al, 2013;Poli et al, 2017) for neural tissue recovery from brain injury (Shimba et al, 2019). Next, it could be combined with an array of non-invasive planar microelectrodes, which provide spiking activity registration and stimulation of isolated or multiple neurons.…”

The emergence of dynamical instantaneous memory in the spontaneous activity of spatially confined neuronal assemblies in vitro

“…chain-like and feedforward networks) have been experimentally used to investigate how the layout influences the spontaneous activity. Furthermore, these networks have the capability of self-organizing forming spontaneous connections among the modules with the possibility to study how dynamics changes compared to uniform networks and to networks composed of well-known anatomical layouts [59]. Multi-modular networks can be electrically stimulated with the possibility to have a better control over the evoked activity, thanks to the structural and functional confinement ensured by the neuronal circuits.…”