In this study, we present a 4-channel intracortical glassy carbon (GC) microelectrode array on a flexible substrate for the simultaneous in vivo neural activity recording and dopamine (DA) concentration measurement at four different brain locations (220μm vertical spacing). The ability of GC microelectrodes to detect DA was firstly assessed in vitro in phosphate-buffered saline solution and then validated in vivo measuring spontaneous DA concentration in the Striatum of European Starling songbird through fast scan cyclic voltammetry (FSCV). The capability of GC microelectrode arrays and commercial penetrating metal microelectrode arrays to record neural activity from the Caudomedial Neostriatum of European starling songbird was compared. Preliminary results demonstrated the ability of GC microelectrodes in detecting neurotransmitters release and recording neural activity in vivo. GC microelectrodes array may, therefore, offer a new opportunity to understand the intimate relations linking electrophysiological parameters with neurotransmitters release.
Objective. In this study, we demonstrate practical applications of a novel 3-dimensional neural probe for simultaneous electrophysiological recordings from the surface of the brain as well as deep intra-cortical tissue. We used this 3D probe to investigate signal propagation mechanisms between neuronal cells and their responses to stimuli in a 3D fashion. Approach. This novel probe leverage 2D thin-film microfabrication technique to combine an epi-cortical (surface) and an intra-cortical (depth) microelectrode arrays (Epi-Intra), that unfold into an origami 3D-like probe during brain implantation. The flexible epi-cortical component conforms to the brain surface while the intra-cortical array is reinforced with stiffer durimide polymer layer for ease of tissue penetration. The microelectrodes are made of glassy carbon material that is biocompatible and has low electrochemical impedance that is important for high fidelity neuronal recordings. These recordings were performed on the auditory region of anesthetized European starling songbirds during playback of conspecific songs as auditory stimuli. Main results. The Epi-Intra probe recorded broadband activity including local field potentials (LFPs) signals as well as single-unit activity and multi-unit activity from both surface and deep brain. The majority of recorded cellular activities were stimulus-locked and exhibited low noise. Notably, while LFPs recorded on surface and depth electrodes did not exhibit strong correlation, composite receptive fields (CRFs)—extracted from individual neuron cells through a non-linear model and that are cell-dependent—were correlated. Significance. These findings demonstrate that CRFs extracted from Epi-Intra recordings are excellent candidates for neural coding and for understanding the relationship between sensory neuronal responses and their stimuli (stimulus encoding). Beyond CRFs, this novel neural probe may enable new spatiotemporal 3D volumetric mapping to address, with cellular resolution, how the brain coordinates function.
Vocal communication in both songbirds and humans relies on categorical perception of smoothly varying acoustic spaces. Vocal perception can be biased by expectation and context, but the mechanisms of this bias are not well understood. We developed a behavioral task in which songbirds, European starlings, are trained to to classify smoothly varying song syllables in the context of predictive syllable sequences. We find that syllable-sequence predictability biases perceptual categorization following a Bayesian model of probabilistic information integration. We then recorded from populations of neurons in the auditory forebrain while birds actively categorized song syllables, observing large proportions of neurons that track the smoothly varying natural feature space of syllable categories. We observe that predictive information in the syllable sequences dynamically modulates sensory neural representations. These results support a Bayesian model of perception where predictive information acts to dynamically reallocate sensory neural resources, sharpening acuity (i.e. the likelihood) in high-probability regions of stimulus space.
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