bioRxiv Subject Collection: Neuroscience's Journal
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Saturday, January 6th, 2024
| Time |
Event |
| 1:16a |
The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks
Thin pancake-like neuronal networks cultured on top of a planar microelectrode array have been extensively tried out in neuroengineering, as a substrate for the mobile robots control unit, i.e., as a cyborgs brain. Most of these attempts failed due to intricate self-organizing dynamics in the neuronal systems. In particular, the networks may exhibit an emergent spatial map of steady nucleation sites ("n-sites") of spontaneous population spikes. Being unpredictable and independent of the surface electrode locations, the n-sites drastically change local ability of the network to generate spikes. Here, using a spiking neuronal network model with generative spatially-embedded connectome, we systematically show in simulations that the number, location, and relative activity of spontaneously formed n-sites ("the vitals") crucially depend on the samplings of three distributions: 1) the network distribution of neuronal excitability, 2) the distribution of connections between neurons of the network, and 3) the distribution of maximal amplitudes of a single synaptic current pulse. Moreover, blocking the dynamics of a small fraction (about 4%) of non-pacemaker neurons having the highest excitability was enough to completely suppress the occurrence of population spikes and their n-sites. This key result is explained theoretically. Remarkably, the n-sites occur taking into account only short-term synaptic plasticity, i.e., without a Hebbian-type plasticity. As the spiking network model used in this study is strictly deterministic, all simulation results can be accurately reproduced. The model, which has already demonstrated a very high richness-to-complexity ratio, can also be directly extended into the three-dimensional case, e.g., for targeting peculiarities of spiking dynamics in cerebral (or brain) organoids. We recommend the model as an excellent illustrative tool for teaching network-level computational neuroscience, complementing a few benchmark models. | | 6:20a |
Temporal information of subsecond sensory stimuli in primary visual cortex is encoded via high dimensional population vectors.
Whether in music, language, baking, or memory, our experience of the world is fundamentally linked to time. However, it is unclear how temporal information is encoded, particularly in the range of milliseconds to seconds. Temporal processing at this scale is critical to prediction and survival, such as in a prey anticipating not only where a charging predator will go but also when the predator will arrive at that location. Several models of timing have been proposed that suggest that either time is encoded intrinsically in the dynamics of a network or that time is encoded by mechanisms that are explicitly dedicated to temporal processing. To determine how temporal information is encoded, we recorded neural activity in primary visual cortex (V1) as mice (male and female) performed a goal directed sensory discrimination task, in which patterns of subsecond stimuli differed only in their temporal profiles. We found that temporal information was encoded in the changing population vector of the network and that the space between these vectors was maximized in learned sessions. Our results suggest that temporal information in the subsecond range is encoded intrinsically and does not rely upon specialized timing mechanisms.
SIGNIFICANCE STATEMENTOur experience of the world is fundamentally linked to time, but it is unclear how temporal information is encoded, particularly in the range of milliseconds to seconds. Using a sensory discrimination task in which patterns of subsecond stimuli differed in their temporal profiles, we found that primary visual cortex encodes temporal information via the changing population vector of the network. As temporal processing via population encoding has been shown to rely on inhibitory activity in computational models, our results may provide insight into temporal processing deficits in disorders such as autism spectrum disorder in which there is inhibitory-excitatory imbalance. Furthermore, our results may underlie processing of higher-order sensory stimuli, such as language, that are characterized by complex temporal sequences. |
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