Abstract not found

The computer metaphor for the mind or brain has long outlived its usefulness, being based on Cartesian ideas. Connectionism has not broken free from this metaphor, and this has stunted the directions connectionist research has taken. The subordinate role of timing in computations has resulted in networks with real-value timelags on signals passing between nodes being ignored. The notion of representation in connectionism is generally confused; this can be clarified when at all times it is made explicit who or what Q and S are in the formula "P is used by Q to represent R to S". Frequently they may be layers or modules within a network, but the typical confusion is symptomatic of the computer metaphor which in practice favours feedforward and militates against arbitrarily connected networks. Rejecting this metaphor, an alternative paradigm is suggested of a brain as a complex dynamical system; investigating the dynamics of arbitrarily connected networks with real-valued timelags, speci...

For the integrate-and-fire model with or without reversal potentials, we consider how correlated inputs affect the variability of cellular output. For both models the variability of efferent spike trains measured by coefficient of variation of the interspike interval (abbreviated to CV in the remainder of the paper) is a nondecreasing function of input correlation. When the correlation coefficient is greater than 0.09, the CV of the integrate-and-fire model without reversal potentials is always above 0.5, no matter how strong the inhibitory inputs. When the correlation coefficient is greater than 0.05, CV for the integrate-and-fire model with reversal potentials is always above 0.5, independent of the strength of the inhibitory inputs. Under a given condition on correlation coefficients we find that correlated Poisson processes can be decomposed into independent Poisson processes. We also develop a novel method to estimate the distribution density of the first passage time of the integ...

The availability of efficient and reliable simulation tools is one of the mission-critical technologies in the fast-moving field of computational neuroscience. Research indicates that higher brain functions emerge from large and complex cortical networks and their interactions. The large number of elements (neurons) combined with the high connectivity (synapses) of the biological network and the specific type of interactions impose severe constraints on the explorable system size that previously have been hard to overcome. Here we present a collection of new techniques combined to a coherent simulation tool removing the fundamental obstacle in the computational study of biological neural networks: the enormous number of synaptic contacts per neuron. Distributing an individual simulation over multiple computers enables the investigation of networks orders of magnitude larger than previously possible. The

We studied the simultaneous activity of pairs of neurons recorded with a single electrode in visual cortical area MT while monkeys performed a direction discrimination task. Previously, we reported the strength of interneuronal correlation of spike count on the time scale of the behavioral epoch (2 sec) and noted its potential impact on signal pooling (Zohary et al., 1994). We have now examined correlation at longer and shorter time scales and found that pair-wise cross-correlation was predominantly short term (10–100 msec). Narrow, central peaks in the spike train cross-correlograms were largely responsible for correlated spike counts on the time scale of the behavioral epoch. Longer-term (many seconds to minutes) changes in the responsiveness of single neurons were observed in auto-correlations; however, these slow changes in time were on average uncorrelated between neurons. Knowledge of the limited time A fundamental problem in sensory neuroscience is to understand how psychophysical performance is related to the signaling capacities of single sensory neurons. It is now widely recognized that no satisfactory solution to this problem can be achieved in the absence of detailed knowledge concerning correlated firing within the pool of sensory neurons contributing to a particular psychophysical judgment (Johnson et al., 1973; Johnson, 1980;

Most cognitive functions are based on highly parallel and distributed information processing by the brain. A paradigmatic example is provided by the vertebrate visual system where numerous cortical areas have been described which analyse different types of visual information. At present, it is unclear how information can be integrated and how coherent representational states can be established in such distributed systems. We suggest that this so-called ‘binding problem ’ may be solved in the temporal domain. The hypothesis is that synchronization of neuronal discharges can serve for the integration of distributed neurons into cell assemblies and that this process may underlie the selection of perceptually and behaviourally relevant information. We review experimental results, mainly obtained in the visual system, which support this temporal binding hypothesis.

We present a mathematical analysis of a networks with Integrate-and-Fire neurons with conductance based synapses. Taking into account the realistic fact that the spike time is only known within some finite precision, we propose a model where spikes are effective at times multiple of a characteristic time scale δ, where δ can be 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.

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The design of autonomous robots has an intimate relationship with the study of autonomous animals and humans | robots provide a convenient puppet show for illustrating current myths about cognition. Like it or not, any approach to the design of autonomous robots is underpinned by some philosophical position in the designer. Whereas a philosophical position normally has to survive in debate, in a project of building situated robots one's philosophical position a ects design decisions and is then tested in the real world | \doing philosophy of mind with a screwdriver". Traditional Good Old Fashioned Arti cial Intelligence (GOFAI) approaches have been based on what is commonly called a Cartesian split between body and mind | though the division goes back at least to Plato. The Dynamical Systems approach to cognition, and to robot design, draws on other philosophical paradigms. We shall discuss how such varied philosophers as Heidegger, Merleau-Ponty or Wittgenstein, in the improbable event of them wanting to build robots, might betemptedtosetaboutthetask. 1