Why Neural Correlates Of Consciousness Are Fine, \ud But Not Enough\ud

\ud The existence of neural correlates of consciousness (NCC) is not enough for philosophical purposes. On the other hand, there's more to NCC than meets the sceptic's eye.\ud (I) NCC are useful for a better understanding of conscious experience, for instance: (1) NCC are helpful to explain phenomenological features of consciousness – e.g., dreaming. (2) NCC can account for phenomenological opaque facts – e.g., the temporal structure of consciousness. (3) NCC reveal properties and functions of consciousness which cannot be elucidated either by introspective phenomenology or by psychological experiments alone – e.g., vision.\ud (II) There are crucial problems and shortcomings of NCC: (1) Correlation implies neither causation nor identity. (2) There are limitations of empirical access due to the problem of other minds and the problem of self-deception, and (3) due to the restrictions provided by inter- and intraindividual variations. (4) NCC cannot be catched by neuroscience alone because of the externalistic content of representations. Therefore, NCC are not sufficient for a naturalistic theory of mind, (5) nor are they necessary because of the possibility of multiple realization.\ud (III) Nevertheless, NCC are relevant and important for the mind-body problem: (1) NCC reveal features that are necessary at least for behavioral manifestations of human consciousness. (2) But NCC are compatible with very different proposals for a solution of the mind-body problem. This seems to be both advantageous and detrimental. (3) NCC restrict nomological identity accounts. (4) The investigation of NCC can refute empirical arguments for interactionism as a case study of John Eccles' dualistic proposals will show. (5) The discoveries of NCC cannot establish a naturalistic theory of mind alone, for which, e.g., a principle of supervenience and a further condition – and therefore philosophical arguments – are required.\u

Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rules

Most elementary behaviors such as moving the arm to grasp an object or walking into the next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal action potentials occur on the time scale of a few milliseconds. Learning rules of the brain must therefore bridge the gap between these two different time scales. Modern theories of synaptic plasticity have postulated that the co-activation of pre- and postsynaptic neurons sets a flag at the synapse, called an eligibility trace, that leads to a weight change only if an additional factor is present while the flag is set. This third factor, signaling reward, punishment, surprise, or novelty, could be implemented by the phasic activity of neuromodulators or specific neuronal inputs signaling special events. While the theoretical framework has been developed over the last decades, experimental evidence in support of eligibility traces on the time scale of seconds has been collected only during the last few years. Here we review, in the context of three-factor rules of synaptic plasticity, four key experiments that support the role of synaptic eligibility traces in combination with a third factor as a biological implementation of neoHebbian three-factor learning rules

Effect of dilution in asymmetric recurrent neural networks

We study with numerical simulation the possible limit behaviors of synchronous discrete-time deterministic recurrent neural networks composed of N binary neurons as a function of a network's level of dilution and asymmetry. The network dilution measures the fraction of neuron couples that are connected, and the network asymmetry measures to what extent the underlying connectivity matrix is asymmetric. For each given neural network, we study the dynamical evolution of all the different initial conditions, thus characterizing the full dynamical landscape without imposing any learning rule. Because of the deterministic dynamics, each trajectory converges to an attractor, that can be either a fixed point or a limit cycle. These attractors form the set of all the possible limit behaviors of the neural network. For each network, we then determine the convergence times, the limit cycles' length, the number of attractors, and the sizes of the attractors' basin. We show that there are two network structures that maximize the number of possible limit behaviors. The first optimal network structure is fully-connected and symmetric. On the contrary, the second optimal network structure is highly sparse and asymmetric. The latter optimal is similar to what observed in different biological neuronal circuits. These observations lead us to hypothesize that independently from any given learning model, an efficient and effective biologic network that stores a number of limit behaviors close to its maximum capacity tends to develop a connectivity structure similar to one of the optimal networks we found.Comment: 31 pages, 5 figure

Modelling etiopathogenesis of the FOXG1-duplication-linked variant of West syndrome

ABSTRACT Here we describe the characterization of a novel Foxg1-GOF model we created to dissect the role of Foxg1 in postmitotic neuronal differentiation and reconstruct pathogenetic mechanisms which underlie the FOXG1 duplication-linked West syndrome. This is a devastating neurological disorder, triggered by a complex variety of pathogenic conditions. It is characterized by infantile spasms, abnormal EEG with hypsarrhytmia and seizures and dramatic cognitive impairment. For it only symptomatic treatments are presently available. As expected, these Foxg1-GOF mice showed increased neuronal activity in baseline conditions and were more prone to limbic motor seizures upon kainic acid administration. A preliminary developmental profiling of their cerebral cortex unveiled four major histogenetic anomalies, likely contributing to their hyperexcitability. These anomalies were: (1) an altered neocortical laminar blueprint with impaired layer VI/layer V segregation and defective activation of layer IV-II programs; (2) a substantial reduction of PV+ interneurons; (3) a patterned, area- and lamina-specific astrocyte deprivation; (4) a defective expression of the Gabra1 receptor subunit. Similar phenomena might concur to neurological anomalies of West syndrome patients harboring FOXG1 duplications. A parallel in vitro study, run on dissociated cortico-cerebral cultures, revealed that a substantial Foxg1 upregulation occurred upon delivery of depolarizing stimuli. Neuronal, activity-linked Foxg1 elevation required the presence of astrocytes. Activity-linked Foxg1 fluctuations were inter-twinned with immediate-early genes fluctuations and depended on them, according to distinct, neuron- and astrocyte-specific patterns. In West syndrome patients with augmented FOXG1 dosage, a FOXG1-mRNA increase evoked by depolarizing stimuli might ignite a vicious circle, exacerbating neuronal hyperactivity and contributing to interictal EEG anomalies and seizures

Robust synchronization of an array of coupled stochastic discrete-time delayed neural networks

Copyright [2008] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper is concerned with the robust synchronization problem for an array of coupled stochastic discrete-time neural networks with time-varying delay. The individual neural network is subject to parameter uncertainty, stochastic disturbance, and time-varying delay, where the norm-bounded parameter uncertainties exist in both the state and weight matrices, the stochastic disturbance is in the form of a scalar Wiener process, and the time delay enters into the activation function. For the array of coupled neural networks, the constant coupling and delayed coupling are simultaneously considered. We aim to establish easy-to-verify conditions under which the addressed neural networks are synchronized. By using the Kronecker product as an effective tool, a linear matrix inequality (LMI) approach is developed to derive several sufficient criteria ensuring the coupled delayed neural networks to be globally, robustly, exponentially synchronized in the mean square. The LMI-based conditions obtained are dependent not only on the lower bound but also on the upper bound of the time-varying delay, and can be solved efficiently via the Matlab LMI Toolbox. Two numerical examples are given to demonstrate the usefulness of the proposed synchronization scheme