Stimulus Size Dependence of Information Transfer from Retina to Thalamus

Relay cells in the mammalian lateral geniculate nucleus (LGN) are driven primarily by single retinal ganglion cells (RGCs). However, an LGN cell responds typically to less than half of the spikes it receives from the RGC that drives it, and without retinal drive the LGN is silent (Kaplan and Shapley, 1984). Recent studies, which used stimuli restricted to the receptive field (RF) center, show that despite the great loss of spikes, more than half of the information carried by the RGC discharge is typically preserved in the LGN discharge (Sincich et al., 2009), suggesting that the retinal spikes that are deleted by the LGN carry less information than those that are transmitted to the cortex. To determine how LGN relay neurons decide which retinal spikes to respond to, we recorded extracellularly from the cat LGN relay cell spikes together with the slow synaptic (‘S’) potentials that signal the firing of retinal spikes. We investigated the influence of the inhibitory surround of the LGN RF by stimulating the eyes with spots of various sizes, the largest of which covered the center and surround of the LGN relay cell's RF. We found that for stimuli that activated mostly the RF center, each LGN spike delivered more information than the retinal spike, but this difference was reduced as stimulus size increased to cover the RF surround. To evaluate the optimality of the LGN editing of retinal spikes, we created artificial spike trains from the retinal ones by various deletion schemes. We found that single LGN cells transmitted less information than an optimal detector could

Conditions for wave trains in spiking neural networks

Spatiotemporal patterns such as traveling waves are frequently observed in recordings of neural activity. The mechanisms underlying the generation of such patterns are largely unknown. Previous studies have investigated the existence and uniqueness of different types of waves or bumps of activity using neural-field models, phenomenological coarse-grained descriptions of neural-network dynamics. But it remains unclear how these insights can be transferred to more biologically realistic networks of spiking neurons, where individual neurons fire irregularly. Here, we employ mean-field theory to reduce a microscopic model of leaky integrate-and-fire (LIF) neurons with distance-dependent connectivity to an effective neural-field model. In contrast to existing phenomenological descriptions, the dynamics in this neural-field model depends on the mean and the variance in the synaptic input, both determining the amplitude and the temporal structure of the resulting effective coupling kernel. For the neural-field model we employ liner stability analysis to derive conditions for the existence of spatial and temporal oscillations and wave trains, that is, temporally and spatially periodic traveling waves. We first prove that wave trains cannot occur in a single homogeneous population of neurons, irrespective of the form of distance dependence of the connection probability. Compatible with the architecture of cortical neural networks, wave trains emerge in two-population networks of excitatory and inhibitory neurons as a combination of delay-induced temporal oscillations and spatial oscillations due to distance-dependent connectivity profiles. Finally, we demonstrate quantitative agreement between predictions of the analytically tractable neural-field model and numerical simulations of both networks of nonlinear rate-based units and networks of LIF neurons.Comment: 36 pages, 8 figures, 4 table

Interspike Interval Based Filtering of Directional Selective Retinal Ganglion Cells Spike Trains

The information regarding visual stimulus is encoded in spike trains at the output of retina by retinal ganglion cells (RGCs). Among these, the directional selective cells (DSRGC) are signaling the direction of stimulus motion. DSRGCs' spike trains show accentuated periods of short interspike intervals (ISIs) framed by periods of isolated spikes. Here we use two types of visual stimulus, white noise and drifting bars, and show that short ISI spikes of DSRGCs spike trains are more often correlated to their preferred stimulus feature (that is, the direction of stimulus motion) and carry more information than longer ISI spikes. Firstly, our results show that correlation between stimulus and recorded neuronal response is best at short ISI spiking activity and decrease as ISI becomes larger. We then used grating bars stimulus and found that as ISI becomes shorter the directional selectivity is better and information rates are higher. Interestingly, for the less encountered type of DSRGC, known as ON-DSRGC, short ISI distribution and information rates revealed consistent differences when compared with the other directional selective cell type, the ON-OFF DSRGC. However, these findings suggest that ISI-based temporal filtering integrates a mechanism for visual information processing at the output of retina toward higher stages within early visual system

Towards building a more complex view of the lateral geniculate nucleus: Recent advances in understanding its role

The lateral geniculate nucleus (LGN) has often been treated in the past as a linear filter that adds little to retinal processing of visual inputs. Here we review anatomical, neurophysiological, brain imaging, and modeling studies that have in recent years built up a much more complex view of LGN . These include effects related to nonlinear dendritic processing, cortical feedback, synchrony and oscillations across LGN populations, as well as involvement of LGN in higher level cognitive processing. Although recent studies have provided valuable insights into early visual processing including the role of LGN, a unified model of LGN responses to real-world objects has not yet been developed. In the light of recent data, we suggest that the role of LGN deserves more careful consideration in developing models of high-level visual processing

The thalamocortical symphony:How thalamus and cortex play together in schizophrenia and plasticity

The work presented in this thesis aimed at investigating the function and mechanism of corticothalamic-thalamocortical network in schizophrenia and experience-dependent plasticity, further discussed their possible connection.In Chapter 2, we examined the effects of low-dose ketamine on the corticothalamic circuit (CTC) system. Our findings reveal that ketamine induces abnormal spindle activity and gamma oscillations in the CTC system. Notably, ketamine also leads to a transition in thalamic neurons from burst-firing to tonic action potential mode, which may underlie deficits in spindle oscillations. Chapter 3 addresses sensory perception deficits in schizophrenia, emphasizing disruptions in beta and gamma frequency oscillations due to signal-to-noise ratio imbalances. Chapter 4 explores experience-dependent plasticity, highlighting the role of thalamic synaptic inhibition in ocular dominance plasticity and the influence of cortical feedback. Chapter 5 investigates the involvement of endocannabinoids, particularly CB1 receptors, in inhibitory synaptic maturation and ocular dominance plasticity within the primary visual cortex.The general discussion raises the possibility of a link between neural plasticity and schizophrenia, particularly during the transformative phase of adolescence when the brain undergoes significant changes. An abnormal balance between inhibition and excitation, influenced by GABAergic maturation deficits, connectivity disruptions, and altered perceptual information transfer, may contribute to the development of schizophrenia.This thesis offers valuable insights into the intricate mechanisms underlying schizophrenia, with a particular focus on the CTC circuit, NMDA receptors, and endocannabinoids in the context of neuronal plasticity and cognitive function

The thalamocortical symphony:How thalamus and cortex play together in schizophrenia and plasticity

The work presented in this thesis aimed at investigating the function and mechanism of corticothalamic-thalamocortical network in schizophrenia and experience-dependent plasticity, further discussed their possible connection.In Chapter 2, we examined the effects of low-dose ketamine on the corticothalamic circuit (CTC) system. Our findings reveal that ketamine induces abnormal spindle activity and gamma oscillations in the CTC system. Notably, ketamine also leads to a transition in thalamic neurons from burst-firing to tonic action potential mode, which may underlie deficits in spindle oscillations. Chapter 3 addresses sensory perception deficits in schizophrenia, emphasizing disruptions in beta and gamma frequency oscillations due to signal-to-noise ratio imbalances. Chapter 4 explores experience-dependent plasticity, highlighting the role of thalamic synaptic inhibition in ocular dominance plasticity and the influence of cortical feedback. Chapter 5 investigates the involvement of endocannabinoids, particularly CB1 receptors, in inhibitory synaptic maturation and ocular dominance plasticity within the primary visual cortex.The general discussion raises the possibility of a link between neural plasticity and schizophrenia, particularly during the transformative phase of adolescence when the brain undergoes significant changes. An abnormal balance between inhibition and excitation, influenced by GABAergic maturation deficits, connectivity disruptions, and altered perceptual information transfer, may contribute to the development of schizophrenia.This thesis offers valuable insights into the intricate mechanisms underlying schizophrenia, with a particular focus on the CTC circuit, NMDA receptors, and endocannabinoids in the context of neuronal plasticity and cognitive function

HIERARCHICAL NEURAL COMPUTATION IN THE MAMMALIAN VISUAL SYSTEM

Our visual system can efficiently extract behaviorally relevant information from ambiguous and noisy luminance patterns. Although we know much about the anatomy and physiology of the visual system, it remains obscure how the computation performed by individual visual neurons is constructed from the neural circuits. In this thesis, I designed novel statistical modeling approaches to study hierarchical neural computation, using electrophysiological recordings from several stages of the mammalian visual system. In Chapter 2, I describe a two-stage nonlinear model that characterized both synaptic current and spike response of retinal ganglion cells with unprecedented accuracy. I found that excitatory synaptic currents to ganglion cells are well described by excitatory inputs multiplied by divisive suppression, and that spike responses can be explained with the addition of a second stage of spiking nonlinearity and refractoriness. The structure of the model was inspired by known elements of the retinal circuit, and implies that presynaptic inhibition from amacrine cells is an important mechanism underlying ganglion cell computation. In Chapter 3, I describe a hierarchical stimulus-processing model of MT neurons in the context of a naturalistic optic flow stimulus. The model incorporates relevant nonlinear properties of upstream V1 processing and explained MT neuron responses to complex motion stimuli. MT neuron responses are shown to be best predicted from distinct excitatory and suppressive components. The direction-selective suppression can impart selectivity of MT neurons to complex velocity fields, and contribute to improved estimation of the three-dimensional velocity of moving objects. In Chapter 4, I present an extended model of MT neurons that includes both the stimulus-processing component and network activity reflected in local field potentials (LFPs). A significant fraction of the trial-to-trial variability of MT neuron responses is predictable from the LFPs in both passive fixation and a motion discrimination task. Moreover, the choice-related variability of MT neuron responses can be explained by their phase preferences in low-frequency band LFPs. These results suggest an important role of network activity in cortical function. Together, these results demonstrated that it is possible to infer the nature of neural computation from physiological recordings using statistical modeling approaches