Online research in philosophy

In this commentary, arguments are made for a dendritic code being preferable to a temporal synaptic code as a model of conscious experience. A temporal firing pattern is a product of an ongoing neural computation; hence, it is based on a neural algorithm and an algorithm may not provide the most suitable model for conscious experience. Reiteration of a temporal firing code as suggested in a preceding article (Helekar, 1999) does not necessarily improve the situation. The alternative model presented here is that certain synaptic activity patterns, possibly those possessing universal features as suggested by Helekar, can become encoded in the dendritic structure. Following dendritic encoding, quantum phenomena in those specific dendrite sets could illuminate the static image of that encoded synaptic activity. It is the activation of the static image that would be equivalent to conscious experience; thus, conscious awareness would not be directly affiliated with synaptic activity. This dendrite encoding model may go farther than other models to explain the gestalt nature of consciousness, insofar as quantum entanglement could produce an interconnectedness between specific sets of dendrites-an interconnectedness that need not be based on neural computation or neural connections.

Amidst the many brain events evoked by a visual stimulus, which are specifically associated with conscious perception, and which merely reflect non-conscious processing? Several recent neuroimaging studies have contrasted conscious and non-conscious visual processing, but their results appear inconsistent. Some support a correlation of conscious perception with early occipital events, others with late parieto-frontal activity. Here we attempt to make sense of those dissenting results. On the basis of a minimal neuro-computational model, the global neuronal workspace hypothesis, we propose a taxonomy which distinguishes between vigilance and access to conscious report, as well as between subliminal, preconscious and conscious processing. We suggest that these distinctions map onto different neural mechanisms, and that conscious perception is systematically associated with a sudden surge of parieto-frontal activity causing top-down amplification.

We propose a view of consciousness as a sort of metaphoric “funnel” transforming a fraction of the parallel processes taking place continually in our central nervous system into something intrinsically serial and sequential. A conscious state is the result of this temporary serialization of a set of parallel mental processes. Before this event, everything is parallel, but also after it, everything returns parallel. As a consequence, the simile we propose is that of a “clepsydra” or sandglass. A conscious state coincides with its bottleneck. Here, we explore possible consequences of the adoption of this model.

This paper introduces a compositional semantics of locativeprepositional phrases which is based on a vector space ontology.Model-theoretic properties of prepositions like monotonicity andconservativity are defined in this system in a straightforward way.These notions are shown to describe central inferences with spatialexpressions and to account for the grammaticality of prepositionmodification. Model-theoretic constraints on the set of possibleprepositions in natural language are specified, similar to the semanticuniversals of Generalized Quantifier Theory.

To deal with reactive sequential decision tasks we present a learning model Clarion which is a hybrid connectionist model consisting of both localist and dis tributed representations based on the two level ap proach proposed in Sun The model learns and utilizes procedural and declarative knowledge tapping into the synergy of the two types of processes It uni es neural reinforcement and symbolic methods to perform on line bottom up learning Experiments in various situations are reported that shed light on the working of the model..

To deal with sequential decision tasks we present a learning model Clarion which is a hybrid connectionist model consisting of both localist and distributed represen tations based on the two level approach proposed in Sun The model learns and utilizes procedural and declarative knowledge tapping into the synergy of the two types of processes It uni es neural reinforcement and symbolic methods to perform on line bottom up learning Experiments in various situations are reported that shed light on the working of the model..

A possible relation between Derrida's deconstruction of metaphysics and connectionism is explored by considering diff rance in neural nets terms. First diff rance , as the crossing of Saussurian difference and Freudian deferral, is modeled and then the fuller 'sheaf of diff rance is taken up. The metaphysically conceived brain has two versions: in the traditional computational version the brain processes information like a computer and in the connectionist version the brain computes input vector to output vector transformations non-symbolically. The 'deconstructed brain' neither processes information nor computes functions but is spontaneously economical.

This article discusses how sequential sampling models can be integrated in a cognitive architecture. The new theory Retrieval by Accumulating Evidence in an Architecture (RACE/A) combines the level of detail typically provided by sequential sampling models with the level of task complexity typically provided by cognitive architectures. We will use RACE/A to model data from two variants of a picture–word interference task in a psychological refractory period design. These models will demonstrate how RACE/A enables interactions between sequential sampling and long-term declarative learning, and between sequential sampling and task control. In a traditional sequential sampling model, the onset of the process within the task is unclear, as is the number of sampling processes. RACE/A provides a theoretical basis for estimating the onset of sequential sampling processes during task execution and allows for easy modeling of multiple sequential sampling processes within a task.

The 'Conscious Pilot' is a new model of the neural correlate of consciousness (NCC) consistent with the Orch OR model. The basic idea is that spatiotemporal envelopes of dendritic gamma synchrony move through the brain's neuronal networks. The movement is sideways to neurocomputational flow, occurring via dendritic dendritic gap junction electrical synapses. A conscious pilot moving around an airplane while it flies on auto pilot is used as a metaphor for dendritic synchrony moving through the brain's neurocomputational networks, conveying conscious experience and choice to otherwise non conscious cognitive modes.