Test Statistics for the Identification of Assembly Neurons in Parallel Spike Trains

In recent years numerous improvements have been made in multiple-electrode recordings (i.e., parallel spike-train recordings) and spike sorting to the extent that nowadays it is possible to monitor the activity of up to hundreds of neurons simultaneously. Due to these improvements it is now potentially possible to identify assembly activity (roughly understood as significant synchronous spiking of a group of neurons) from these recordings, which—if it can be demonstrated reliably—would significantly improve our understanding of neural activity and neural coding. However, several methodological problems remain when trying to do so and, among them, a principal one is the combinatorial explosion that one faces when considering all potential neuronal assemblies, since in principle every subset of the recorded neurons constitutes a candidate set for an assembly. We present several statistical tests to identify assembly neurons (i.e., neurons that participate in a neuronal assembly) from parallel spike trains with the aim of reducing the set of neurons to a relevant subset of them and this way ease the task of identifying neuronal assemblies in further analyses. These tests are an improvement of those introduced in the work by Berger et al. (2010) based on additional features like spike weight or pairwise overlap and on alternative ways to identify spike coincidences (e.g., by avoiding time binning, which tends to lose information)

Towards Neuro-Inspired Electronic Oscillators Based on The Dynamical Relaying Mechanism

Electronic oscillators are used for the generation of both continuous and discrete signals, playing a fundamental role in today’s electronics. In both contexts, these systems require stringent performances such as spectral purity, low phase noise, frequency and temperature stability. In state of the art oscillators the preservation of some of these aspects is jeopardized by specific critical issues, e.g., the sensitivity to load capacitance or the component aging over time. This leaves room for the search of new technologies for their realization. On the other hand, in the last decade electronics has been influenced by a growing number of neuro-inspired mechanisms, which allowed for alternative techniques aimed at solving some classical critical issues.In this paper we present an exploratory study for the development of electronic oscillators based on the neuro-inspired mechanism dynamical relaying, which relies on a structure composed of three delay coupled units (as neurons or even neuron populations) able to resonate and self-organise to generate and maintain a given rhythm with great reliability over a considerable parameter range, showing robustness to noise. We used the recent leaky integrated and fire with latency (LIFL) as neuron model. We have initially developed the mathematical model of the neuro-inspired oscillator, and implemented it using Matlab®; then, we have realized the schematic of such system in PSpice®. Finally, the model has been validated to verify whether it observes the fundamental properties of the dynamical relaying mechanisms described in computational neuroscience studies, and if the circuit implementation presents the same behaviour of the mathematical model.Validation results suggest that the dynamical relaying mechanism can be proficuously taken in consideration as alternative strategy for the design of electronic oscillators

On Dynamics of Integrate-and-Fire Neural Networks with Conductance Based Synapses

We present a mathematical analysis of a networks with Integrate-and-Fire neurons and adaptive conductances. Taking into account the realistic fact that the spike time is only known within some \textit{finite} precision, we propose a model where spikes are effective at times multiple of a characteristic time scale $\delta$, where $\delta$ can be \textit{arbitrary} small (in particular, well beyond the numerical precision). We make a complete mathematical characterization of the model-dynamics and obtain the following results. The asymptotic dynamics is composed by finitely many stable periodic orbits, whose number and period can be arbitrary large and can diverge in a region of the synaptic weights space, traditionally called the "edge of chaos", a notion mathematically well defined in the present paper. Furthermore, except at the edge of chaos, there is a one-to-one correspondence between the membrane potential trajectories and the raster plot. This shows that the neural code is entirely "in the spikes" in this case. As a key tool, we introduce an order parameter, easy to compute numerically, and closely related to a natural notion of entropy, providing a relevant characterization of the computational capabilities of the network. This allows us to compare the computational capabilities of leaky and Integrate-and-Fire models and conductance based models. The present study considers networks with constant input, and without time-dependent plasticity, but the framework has been designed for both extensions.Comment: 36 pages, 9 figure

Regulation of spike timing in visual cortical circuits

A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role

Regulation of spike timing in visual cortical circuits

A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role

Computational Study of the Mechanisms Underlying Oscillation in Neuronal Locomotor Circuits

In this thesis we model two very different movement-related neuronal circuits, both of which produce oscillatory patterns of activity. In one case we study oscillatory activity in the basal ganglia under both normal and Parkinsonian conditions. First, we used a detailed Hodgkin-Huxley type spiking model to investigate the activity patterns that arise when oscillatory cortical input is transmitted to the globus pallidus via the subthalamic nucleus. Our model reproduced a result from rodent studies which shows that two anti-phase oscillatory groups of pallidal neurons appear under Parkinsonian conditions. Secondly, we used a population model of the basal ganglia to study whether oscillations could be locally generated. The basal ganglia are thought to be organised into multiple parallel channels. In our model, isolated channels could not generate oscillations, but if the lateral inhibition between channels is sufficiently strong then the network can act as a rhythm-generating ``pacemaker'' circuit. This was particularly true when we used a set of connection strength parameters that represent the basal ganglia under Parkinsonian conditions. Since many things are not known about the anatomy and electrophysiology of the basal ganglia, we also studied oscillatory activity in another, much simpler, movement-related neuronal system: the spinal cord of the Xenopus tadpole. We built a computational model of the spinal cord containing approximately 1,500 biologically realistic Hodgkin-Huxley neurons, with synaptic connectivity derived from a computational model of axon growth. The model produced physiological swimming behaviour and was used to investigate which aspects of axon growth and neuron dynamics are behaviourally important. We found that the oscillatory attractor associated with swimming was remarkably stable, which suggests that, surprisingly, many features of axonal growth and synapse formation are not necessary for swimming to emerge. We also studied how the same spinal cord network can generate a different oscillatory pattern in which neurons on both sides of the body fire synchronously. Our results here suggest that under normal conditions the synchronous state is unstable or weakly stable, but that even small increases in spike transmission delays act to stabilise it. Finally, we found that although the basal ganglia and the tadpole spinal cord are very different systems, the underlying mechanism by which they can produce oscillations may be remarkably similar. Insights from the tadpole model allow us to predict how the basal ganglia model may be capable of producing multiple patterns of oscillatory activity

towards neuro inspired electronic oscillators based on the dynamical relaying mechanism

Electronic oscillators are used for the generation of both continuous and discrete signals, playing a fundamental role in today's electronics. In both contexts, these systems require stringent performances such as spectral purity, low phase noise, frequency and temperature stability. In state of the art oscillators the preservation of some of these aspects is jeopardized by specific critical issues, e.g., the sensitivity to load capacitance or the component aging over time. This leaves room for the search of new technologies for their realization. On the other hand, in the last decade electronics has been influenced by a growing number of neuro-inspired mechanisms, which allowed for alternative techniques aimed at solving some classical critical issues. In this paper we present an exploratory study for the development of electronic oscillators based on the neuro-inspired mechanism dynamical relaying , which relies on a structure composed of three delay coupled units (as neurons or even neuron populations) able to resonate and self-organise to generate and maintain a given rhythm with great reliability over a considerable parameter range, showing robustness to noise . We used the recent leaky integrated and fire with latency (LIFL) as neuron model. We have initially developed the mathematical model of the neuro-inspired oscillator , and implemented it using Matlab®; then, we have realized the schematic of such system in PSpice®. Finally, the model has been validated to verify whether it observes the fundamental properties of the dynamical relaying mechanisms described in computational neuroscience studies, and if the circuit implementation presents the same behaviour of the mathematical model. Validation results suggest that the dynamical relaying mechanism can be proficuously taken in consideration as alternative strategy for the design of electronic oscillators

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