Alex D. Reyes

Neural Networks in Cortex

The overall goal of the laboratory is to understand how sensory information is represented in neural networks. A major stumbling block is that there is an extraordinarily large number of variables that could potentially impact network behavior. A typical cortical network contains several classes of excitatory and inhibitory neurons. Our strategy is to characterize with experiments the cellular properties and connectivity patterns of neurons in auditory cortex and then to use computational and theoretical techniques to elucidate general principles that govern signal processing.

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Experiments:We use an in vitro slice preparation that contains the primary auditory cortex, thalamus, and the interconnecting fibers.  The preparation has a complete circuit that can be used to trace neural signals that enter, go through, and exit the auditory cortex. We perform simultaneous whole-cell recordings from up to 5 neurons to measure: 1) the intrinsic firing of individual neurons; 2) the dynamics of synapses between neurons; and 3) the patterns of connections between specific cell types. (see: Oviedo H & Reyes AD (2002) Nat. Neurosci. 5:261; Oswald AM & Reyes AD (2008) J Neurophys. 99:2998)

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Simulations: We perform simulations both with standard computer models of neurons and with live neurons using an iterative procedure developed in the lab. Our current goal is to understand the cellular mechanisms that underlie acoustically-driven firing behavior and receptive field properties of auditory neurons. We use the experimental data to construct network models with realistic architecture. (see:Chance FS, Abbott LF, Reyes AD (2002) Neuron 35:773;Reyes AD (2003) Nat. Neurosci. 6:593;de la Rocha J et al (2008) J. Neurosci. 28:9151)

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Theory: Idealized networks, though highly simplified, contain the essential circuit elements found in cortex. These networks are conducive to formal mathematical treatment and can therefore be used ferret out general features that are common to all networks. We use methods from non-equilibrium statistical mechanics to examine the transformation of signals within and across cortical networks. (see:Cateau H & Reyes AD (2006) Phys. Rev. Letters 96;Doiron B, Rinzel J, Reyes AD (2006) Phys. Rev. E 74;de la Rocha J & Doiron B et al (2007) Nature 448: 802)

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E-mail: reyes@cns.nyu.edu

Representative Publications

Reyes AD B (2001) Influence of dendritic conductances on the input-output properties of neurons. Annual Review of Neuroscience 24: 653-675

Oviedo H, Reyes AD (2002) Boosting of neuronal firing evoked with asynchronous and synchronous inputs to the dendrite. Nature Neuroscience 5:261-266

Chance FS, Abbott LF, Reyes AD (2002) Gain modulation from background synaptic input. Neuron 35:773-782. (see also Priebe NJ & Ferster D, Neuron 15:773-782; Comment)

Reyes AD (2003) Synchrony-dependent propagation of firing rate in iteratively constructed networks in vitro. Nature Neuroscience 6:593-599 (see also Segev, Nature Neuroscience 6:543-544; News & Views).

Paninski L, Lau B, Reyes AD (2003). Noise-driven adaptation: in vitro and mathematical analysis. Neurocomputing 52: 877-883.

Chance FS & Reyes AD (2004) Controlling neuronal sensitivity to synchronous input. Neurocomputing 58-60: 27-31.

Gutkin B, Ermentrout GB, Reyes AD (2005) Phase response curves determine the responses of neurons to transient inputs J. Neurophys. 94: 1623-1635

Oviedo H & Reyes AD (2005) Variation of input-output properties along the somatodendritic axis of pyramidal neurons J. Neurosci 25: 4985-95

Levy RB, Reyes AD & Aoki C (2006) Nicotinic and muscarinic reduction of unitary excitatory postsynaptic potentials in sensory cortex: dual intracellular recording in vitro. J. Neurophysiol. 95: 2155-2166

Cateau H & Reyes AD (2006) Relation between single neuron and population spiking statistics and effects on network activity. Phys. Rev. Letters 96.

Doiron B, Rinzel J, Reyes AD (2006) Stochastic synchronization in finite size spiking networks. Phys. Rev. E 74.

Oswald AM, Schiff, ML, Reyes AD (2006) Synaptic mechanisms underlying auditory processing. Curr. Opin. Neurobio. 16:371-376.

Reyes AD (2007) Experimental and theoretical analyses of synchrony in feedforward networks. Computational Neuroscience in Epilepsy , ed. Soltesz and Staley, Elsevier Press

de la Rocha J, Doiron B, Shea-Brown E, Josic K, Reyes AD (2007) Correlation between neural spike trains increases with firing rate. Nature 448: 802-806

Oswald AM, Reyes AD (2008) Maturation of intrinsic and synaptic properties of layer 2/3 pyramidal neurons in mouse auditory cortex. J Neurophysiol 99:2998-3008

de la Rocha J, Marchetti C, Schiff M, Reyes AD (2008) Linking the response properties of cells in auditory cortex with network architecture: co-tuning vs. lateral inhibition. J. Neurosci. 28:9151-63

Oswald AM, Doiron B, Rinzel J, Reyes AD (2009) Spatial profile and differential recruitment of GABA-B modulate oscillatory activity in auditory cortex. J. Neurosci. 29:10321-34