In a recent study we found spatio-temporal spike patterns in experimental recordings from monkey motor cortex, and here we study if those could be explained by a synfire chain (SFC) like model. It has been postulated that information processing in the brain is based on precise temporal correlation of neural activity across populations of neurons. In conjunction with perceptual reports, these data suggest that the optimal electrode placement for cortical microstimulation prostheses has electrodes positioned in layers 2/3, and at the top of layer 5. Stimulation in the superficial layers of visual cortex evokes local neural activity with the lowest thresholds, and stimulation in the deep layers evoked the most activity across the cortical column. The laminar spread of evoked activity across cortical depth depended on stimulation depth, in line with anatomical models. Compared to deep sites, superficial stimulation sites responded with higher firing rates at a given current and had lower thresholds. Stimulation elicited elevated neuronal firing rates at all depths of cortex. Microstimulation with currents up to 14 μA (single biphasic pulse, 200 μs per phase) was applied at depths spanning 1600 μm, while simultaneously recording neural activity on all channels within a response window 2.25–11 ms. We investigated how the neural responses evoked by microstimulation in cortex varied with cortical depth, of both stimulation and response.Ī 32-channel single shank electrode array was inserted into the primary visual cortex of anaesthetized rats, such that it spanned all cortical layers.
To optimize electrode placement within the cortex, the neural responses to microstimulation at different cortical depths must first be understood. Our work paves a new path forward in understanding the logic of cortical and thalamic circuits.Ĭortical visual prostheses often use penetrating electrode arrays to deliver microstimulation to the visual cortex. The model also explains several visual phenomena, including the subjective contour effect, and neon-color spreading effect, with circuit-level precision.
The derived model suggests precise functional roles for the feed-forward, feedback, and lateral connections observed in different laminae and columns, assigns a computational role for the path through the thalamus, predicts the interactions between blobs and inter-blobs, and offers an algorithmic explanation for the innate inter-laminar connectivity between clonal neurons within a cortical column. The cortical circuit model is derived by systematically comparing the computational requirements of this model with known anatomical constraints.
Efficient inference and generalization guided the representational choices in the original computational model.
Based on a recent generative model, Recursive Cortical Networks, that demonstrated excellent performance on visual task benchmarks, we derive a family of anatomically instantiated and functional cortical circuit models. Although the theoretical setting of Bayesian inference has been suggested as a framework for understanding cortical computation, making precise and falsifiable biological mappings need models that tackle the challenge of real world tasks. Theory-driven efforts will be required to tease apart the functional logic of cortical circuits from the vast amounts of experimental data on cortical connectivity and physiology. Understanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence.
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