Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks.

Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura's and van Rossum's spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous synchronized network burst events that propagated between layers and highlight the potential applications of these MEMs devices as a tool for further investigation of structure and functional dynamics among neural populations

Who is that? Brain networks and mechanisms for identifying individuals

Social animals can identify conspecifics by many forms of sensory input. However, whether the neuronal computations that support this ability to identify individuals rely on modality-independent convergence or involve ongoing synergistic interactions along the multiple sensory streams remains controversial. Direct neuronal measurements at relevant brain sites could address such questions, but this requires better bridging the work in humans and animal models. Here, we overview recent studies in nonhuman primates on voice and face identity-sensitive pathways and evaluate the correspondences to relevant findings in humans. This synthesis provides insights into converging sensory streams in the primate anterior temporal lobe (ATL) for identity processing. Furthermore, we advance a model and suggest how alternative neuronal mechanisms could be tested

Laminar-specific cortico-cortical loops in mouse visual cortex

"Muitas teorias propõem interacções recorrentes através da hierarquia cortical, mas não é claro se os circuitos corticais são selectivamente ligados para implementar cálculos em ciclo. Usando o mapeamento de circuitos subcelulares do método de canal de rodopsina 2 assistido no córtex visual do rato, comparamos a entrada sináptica de alimentação direta (feedforward, FF) ou retroalimentação (feedback, FB) cortico-cortical (CC) às células que se projectam de volta à fonte de entrada (neurónios em ciclo) com células que se projectam para uma área cortical ou subcortical diferente.(...)

The laminar profile of spatial attention in macaque V1 and V4

PhD ThesisSpatial attention allows processing to be prioritised for one or more locations in the visual field, even in the presence of other distracting or irrelevant stimuli. Previous work has shown that attention modulates the activity of the brain at the level of spiking activity, local field potentials and coherence between and within neuronal groups. However currently little is known about how these attentional modulations differ between groups of neurons in different cortical layers and areas. We trained two adult male rhesus macaques to perform a covert visuospatial attention task whilst we recorded simultaneously from V1 and V4. Recordings were taken with multichannel laminar electrodes allowing recording from supragranular, granular and infragranular cells within the same cortical microcolumns. We used current source density analysis to align our recording contacts to the cortical laminar profile (layers). The receptive fields of the V1 and V4 cells we recorded from were overlapping which meant they could be driven by the same stimulus in the task. To measure the attentional modulation of information flow between different groups of neurons we calculated field coherence, Granger causality and spike-rate correlations. Attention increased firing rates for all of the cell types, layers and areas in our study. We also show that variability as measured by gain variance and noise correlations is reduced by attention. Although we find differences between the two monkeys regarding LFP power changes and regarding coherence measures within and between the areas investigated, we find that attention consistently increased the Granger causality in the gamma frequency band between V1 and V4. We demonstrate that the flow of information in the alpha/beta and gamma bands follows expected interareal feedback and feedforward patterns between V1 and V4. We also provide evidence that feedforward gamma oscillations are generated, contrary to expectations, in the infragranular layers of V1

Establishing Communication between Neuronal Populations through Competitive Entrainment

The role of gamma frequency oscillation in neuronal interaction, and the relationship between oscillation and information transfer between neurons, has been the focus of much recent research. While the biological mechanisms responsible for gamma oscillation and the properties of resulting networks are well studied, the dynamics of changing phase coherence between oscillating neuronal populations are not well understood. To this end we develop a computational model of competitive selection between multiple stimuli, where the selection and transfer of population-encoded information arises from competition between converging stimuli to entrain a target population of neurons. Oscillation is generated by Pyramidal-Interneuronal Network Gamma through the action of recurrent synaptic connections between a locally connected network of excitatory and inhibitory neurons. Competition between stimuli is driven by differences in coherence of oscillation, while transmission of a single selected stimulus is enabled between generating and receiving neurons via Communication-through-Coherence. We explore the effect of varying synaptic parameters on the competitive transmission of stimuli over different neuron models, and identify a continuous region within the parameter space of the recurrent synaptic loop where inhibition-induced oscillation results in entrainment of target neurons. Within this optimal region we find that competition between stimuli of equal coherence results in model output that alternates between representation of the stimuli, in a manner strongly resembling well-known biological phenomena resulting from competitive stimulus selection such as binocular rivalry

Statistical Evaluation of Waveform Collapse Reveals Scale-Free Properties of Neuronal Avalanches

Neural avalanches are a prominent form of brain activity characterized by network-wide bursts whose statistics follow a power-law distribution with a slope near 3/2. Recent work suggests that avalanches of different durations can be rescaled and thus collapsed together. This collapse mirrors work in statistical physics where it is proposed to form a signature of systems evolving in a critical state. However, no rigorous statistical test has been proposed to examine the degree to which neuronal avalanches collapse together. Here, we describe a statistical test based on functional data analysis, where raw avalanches are first smoothed with a Fourier basis, then rescaled using a time-warping function. Finally, an F ratio test combined with a bootstrap permutation is employed to determine if avalanches collapse together in a statistically reliable fashion. To illustrate this approach, we recorded avalanches from cortical cultures on multielectrode arrays as in previous work. Analyses show that avalanches of various durations can be collapsed together in a statistically robust fashion. However, a principal components analysis revealed that the offset of avalanches resulted in marked variance in the time-warping function, thus arguing for limitations to the strict fractal nature of avalanche dynamics. We compared these results with those obtained from cultures treated with an AMPA/NMDA receptor antagonist (APV/DNQX), which yield a power-law of avalanche durations with a slope greater than 3/2. When collapsed together, these avalanches showed marked misalignments both at onset and offset time-points. In sum, the proposed statistical evaluation suggests the presence of scale-free avalanche waveforms and constitutes an avenue for examining critical dynamics in neuronal systems