Microelectronics, bioinformatics and neurocomputation for massive neuronal recordings in brain circuits with large scale multielectrode array probes

Maccione, Alessandro; Gandolfo, Mauro; Zordan, Stefano; Amin, Hayder; Marco, Stefano Di; Nieus, Thierry; Angotzi, Gian Nicola; Berdondini, Luca

doi:10.1016/j.brainresbull.2015.07.008

Cited by 24 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Electrical imaging refers to the recording of electrical signals at a spatial resolution matching the size of individual electrogenic cells and at a temporal resolution matching the frequency response of these cells . From a physical perspective electrical imaging comprises the recording and visualization of extracellular potential changes caused by the activity of electrogenic cells (or compartments thereof) using appropriate electrode arrays.…”

Section: Biotechnological Constrains Of “Electrical Imaging”mentioning

confidence: 99%

Electrical Imaging: Investigating Cellular Function at High Resolution

et al. 2017

View full text Add to dashboard Cite

recently membrane currents from skeletal myotubes, [7] from glia or from glia-derived cancerogenic glioma cells [8] or from pancreatic beta cells [9] have been reported. In brain or muscle tissues different cell types interact. As a consequence the generated electrical signals comprise a rich frequency content ranging from few Hertz to several kilohertz. [10] Electrical signaling on sub-millisecond time scale occurs during so-called action potentials, when the transmembrane potential changes rapidly due to the opening and closing of voltagegated sodium channels.In this review we will discuss biotechnological aspects relevant for the "electrical imaging" technique and highlight applications in neuroscience. Related recent reviews discussed some of the technological backgrounds of microelectrode arrays [11] and some of their applications. [6] To estimate the requirements needed to image the electrical activity in neurons and networks, we summarize important scales and numbers. A typical neuron comprises a cell body, which integrates the presynaptic signals using an elaborate dendritic network (dendritic tree) and sends an action potential (stereotyped transmembrane voltage signal) activity to synaptically connected neurons using a thin axonal cable-like fiber. Mammalian cell bodies are typically about 10-20 µm in diameter, while the cable-like dendritic and axonal structures have diameters ranging between a few tens of nanometers up to several micrometers. [12] The entire dendritic tree of one neuron or of a synaptically connected neuronal network may cover an area ranging between few tens of micrometers up to tens of square millimeter. [12] Within the retinal ganglion cell layer or hippocampal cell layers the neuronal density may be as high as 5000-10 000 cells mm −2 (mouse retinal ganglion cells, [13] granule cells), [14] to name just two prominent neural tissues studied so far using electrical imaging. These numbers highlight the spatial range and resolution to be achieved for imaging the electrically activity generated and propagating therein at (sub) cellular resolution.Optical imaging-the most common imaging modality in life sciences-achieves high-resolution digital imaging by magnifying and recording the light signal using a complementary metal-oxide semiconductor (CMOS) camera chip. Relevant parameters for optical imaging comprise (i) a large field of view, (ii) a high signal-to-noise ratio (SNR), and (iii) high spatial and (iv) high temporal resolution. The ratio between the sensitivity of the CMOS sensors (i.e., minimal number of detected photons) and the corresponding pixel noise sets the sensitivity limit.Electrical imaging-potentially a complementary or alternative technique of optical imaging-deals with the same constrains. Here, instead of a light-detecting CMOS camera chip, the changing Electrical imaging of extracellular potentials reveals the activity of electrogenic cells and of networks thereof over several orders of magnitude, both in space and time. On a spatial scale, electrical activity pro...

show abstract

Section: Biotechnological Constrains Of “Electrical Imaging”mentioning

confidence: 99%

Electrical Imaging: Investigating Cellular Function at High Resolution

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Light stimuli were projected onto the retina as described previously 27,47 by exploiting the temporal and spatial resolution of the experimental platform and were attenuated using neutral density filters to high mesopic light levels (mean luminance 11 cd/m 2 ).…”

Section: Light Stimulimentioning

confidence: 99%

“…The platform records at a sampling rate of 7.1 kHz/electrode when using the full 64×64 array and recordings were stored at 12 bits resolution per channel with a 8 kHz low-pass filter/0.8 Khz high-pass filter using 3Brain's BrainWave software application. Data management and analysis for large-scale, high-density APS-MEA recording is particular difficult (as reviewed in47 ). To reliably extract spikes from the raw traces we used a quantile-based event detection26,48 and single-unit spikes were sorted using the T-Distribution Expectation-Maximisation algorithm in Offline Sorter (Plexon Inc, Dallas, USA).…”

mentioning

confidence: 99%

Pan-Retinal Characterisation of Light Responses from Ganglion Cells in the Developing Mouse Retina

Hilgen

Pirmoradian

Pamplona

et al. 2016

Preprint

View full text Add to dashboard Cite

We have investigated the ontogeny of light-driven responses in mouse retinal ganglion cells (RGCs).Using a large-scale, high-density multielectrode array, we recorded from hundreds to thousands of RGCs simultaneously at pan-retinal level, including dorsal and ventral locations. Responses to different contrasts not only revealed a complex developmental profile for ON, OFF and ON-OFF responses, but also unveiled differences between dorsal and ventral RGC responses. At eye-opening, dorsal RGCs of all types were more responsive to light, perhaps indicating an environmental priority to nest viewing for pre-weaning pups. The developmental profile of ON and OFF responses exhibited antagonistic behaviour, with the strongest ON responses shortly after eye-opening, followed by an increase in the strength of OFF responses later on. Further, we found that with maturation receptive field (RF) center sizes decrease, spike-triggered averaged responses to white noise become stronger, and centers become more circular while maintaining differences between RGC types. We conclude that the maturation of retinal functionality is not spatially homogeneous, likely reflecting ecological requirements that favour earlier maturation of the dorsal retina.The onset of visual experience in mouse occurs around postnatal day (P) 12, at eye opening. Although the retina cannot experience patterned vision beforehand, it is remarkable that RGCs are already capable of encoding information originating from photoreceptors and transmit it to retinal central targets as soon as eyes open. However, these early light responses are far from mature, and they progressively acquire their adult features while the retina develops [1][2][3][4] . In mouse, RGC dendritic stratification in the ON and OFF layers of the inner plexiform layer matures after eye opening 5 and light-driven activity guides the refinement of synaptic connectivity 6,7 . Consequently, RF sizes 8,9 and complex RF properties such as direction and orientation selectivity 10-12 keep maturing after the onset of visual experience. Yet, despite ongoing maturation after eye opening, longitudinal studies of RF properties have never been fully documented 3 . One important often neglected issue is that the retina is not uniformly organised from a functional perspective. Indeed, dorsal, ventral, nasal and temporal domains have evolved to enable optimal encoding of specific features in the visual scene. For example, mouse cones co-express medium wavelength and short wavelength opsins (M-opsin and S-opsin), with a dorsal-to-ventral increasing gradient in S-opsin (and opposite for M-opsin) [13][14][15][16][17][18][19] . These dorso-ventral gradients affect RGC responses in adult animals with respect to their spectral tuning [20][21][22] , improving encoding of achromatic contrasts 20,21 and providing evolutionary advantages for visual tasks 23,24 . The topographical organisation of some RGC subtypes also exhibits dorsal, ventral, nasal, and temporal non-uniformity [25][26][27][28] . However, nothing is know...

show abstract

“…Recent developments in recording techniques are shedding light on the dynamics of cortical neural networks in higher and higher spatio-temporal detail [1]. There are different scientific ways to investigate and understand such large amount of data.…”

Section: Introductionmentioning

confidence: 99%

State-dependent mean-field formalism to model different activity states in conductance-based networks of spiking neurons

et al. 2019

View full text Add to dashboard Cite

Higher and higher interest has been shown in the recent years to large scale spiking simulations of cerebral neuronal networks, coming both from the presence of high performance computers and increasing details in the experimental observations. In this context it is important to understand how population dynamics are generated by the designed parameters of the networks, that is the question addressed by mean field theories. Despite analytic solutions for the mean field dynamics has already been proposed for current based neurons (CUBA), a complete analytic description has not been achieved yet for more realistic neural properties, such as conductance based (COBA) network of adaptive exponential neurons (AdEx). Here, we propose a novel principled approach to map a COBA on a CUBA. Such approach provides a state-dependent approximation capable to reliably predict the firing rate properties of an AdEx neuron with non-instantaneous COBA integration. We also applied our theory to population dynamics, predicting the dynamical properties of the network in very different regimes, such as asynchronous irregular (AI) and synchronous irregular (SI) (slow oscillations, SO). This results show that a state-dependent approximation can be successfully introduced in order to take into account the subtle effects of COBA integration and to deal with a theory capable to correctly predict the activity in regimes of alternating states like slow oscillations. I.

show abstract

Microelectronics, bioinformatics and neurocomputation for massive neuronal recordings in brain circuits with large scale multielectrode array probes

Cited by 24 publications

References 27 publications

Electrical Imaging: Investigating Cellular Function at High Resolution

Electrical Imaging: Investigating Cellular Function at High Resolution

Pan-Retinal Characterisation of Light Responses from Ganglion Cells in the Developing Mouse Retina

State-dependent mean-field formalism to model different activity states in conductance-based networks of spiking neurons

Contact Info

Product

Resources

About