Sparse and Specific Coding during Information Transmission between Co-cultured Dentate Gyrus and CA3 Hippocampal Networks.
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 thein vivoDG-CA3 hippocampal network showed sparse and specific responses in CA3, selectively evoked by each stimulation site in DG.
Department of Biomedical Engineering, University of California Irvine, CA, USA.
Daniele
D
Poli
Department of Biomedical Engineering, University of California Irvine, CA, USA.
Srikanth
S
Thiagarajan
Department of Neurology, University of North CarolinaChapel Hill, NC, USA; Department of Biomedical Engineering, University of FloridaGainesville, FL, USA.
Thomas B
TB
DeMarse
Department of Biomedical Engineering, University of FloridaGainesville, FL, USA; Department of Bioengineering, University of CaliforniaSan Diego, CA, USA.
Bruce C
BC
Wheeler
Department of Biomedical Engineering, University of CaliforniaIrvine, CA, USA; Memory Impairments and Neurological Disorders (MIND) Institute, University of CaliforniaIrvine, CA, USA.
Gregory J
GJ
Brewer
D000818:Animals; D056654:CA3 Region, Hippocampal / Q000502:physiology*; D002478:Cells, Cultured; D018891:Dentate Gyrus / Q000502:physiology*; D005071:Evoked Potentials / Q000502:physiology*; D000069550:Machine Learning; D008839:Microelectrodes; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D051381:Rats
CA3; DG; decoding; hippocampus; machine learning; micro-electrode array; network; neural code