24 research outputs found

    Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

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    Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

    Encoding of Spatio-Temporal Input Characteristics by a CA1 Pyramidal Neuron Model

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    The in vivo activity of CA1 pyramidal neurons alternates between regular spiking and bursting, but how these changes affect information processing remains unclear. Using a detailed CA1 pyramidal neuron model, we investigate how timing and spatial arrangement variations in synaptic inputs to the distal and proximal dendritic layers influence the information content of model responses. We find that the temporal delay between activation of the two layers acts as a switch between excitability modes: short delays induce bursting while long delays decrease firing. For long delays, the average firing frequency of the model response discriminates spatially clustered from diffused inputs to the distal dendritic tree. For short delays, the onset latency and inter-spike-interval succession of model responses can accurately classify input signals as temporally close or distant and spatially clustered or diffused across different stimulation protocols. These findings suggest that a CA1 pyramidal neuron may be capable of encoding and transmitting presynaptic spatiotemporal information about the activity of the entorhinal cortex-hippocampal network to higher brain regions via the selective use of either a temporal or a rate code

    The energy cost of action potential propagation in dopamine neurons: clues to susceptibility in Parkinson's disease.

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    Dopamine neurons of the substantia nigra pars compacta (SNc) are uniquely sensitive to degeneration in Parkinson's disease (PD) and its models. Although a variety of molecular characteristics have been proposed to underlie this sensitivity, one possible contributory factor is their massive, unmyelinated axonal arbor that is orders of magnitude larger than other neuronal types. We suggest that this puts them under such a high energy demand that any stressor that perturbs energy production leads to energy demand exceeding supply and subsequent cell death. One prediction of this hypothesis is that those dopamine neurons that are selectively vulnerable in PD will have a higher energy cost than those that are less vulnerable. We show here, through the use of a biology-based computational model of the axons of individual dopamine neurons, that the energy cost of axon potential propagation and recovery of the membrane potential increases with the size and complexity of the axonal arbor according to a power law. Thus SNc dopamine neurons, particularly in humans, whose axons we estimate to give rise to more than 1 million synapses and have a total length exceeding 4 m, are at a distinct disadvantage with respect to energy balance which may be a factor in their selective vulnerability in PD

    Living on the edge with too many mouths to feed: why dopamine neurons die.

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    Although genes, protein aggregates, environmental toxins, and other factors associated with Parkinson's disease (PD) are widely distributed in the nervous system and affect many classes of neurons, a consistent feature of PD is the exceptional and selective vulnerability of dopamine (DA) neurons of the SNc. What is it about these neurons, among all other neurons in the brain, that makes them so susceptible in PD? We hypothesize that a major contributory factor is the unique cellular architecture of SNc DA neuron axons. Their large, complex axonal arbour puts them under such a tight energy budget that it makes them particularly susceptible to factors that contribute to cell death, including unique molecular characteristics associated with SNc DA neurons and nonspecific, nervous-system-wide factors

    Distinct dendritic arborization and in vivo firing patterns of parvalbumin-expressing basket cells in the hippocampal area CA3.

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    Hippocampal CA3 area generates temporally structured network activity such as sharp waves and gamma and theta oscillations. Parvalbumin-expressing basket cells, making GABAergic synapses onto cell bodies and proximal dendrites of pyramidal cells, control pyramidal cell activity and participate in network oscillations in slice preparations, but their roles in vivo remain to be tested. We have recorded the spike timing of parvalbumin-expressing basket cells in areas CA2/3 of anesthetized rats in relation to CA3 putative pyramidal cell firing and activity locally and in area CA1. During theta oscillations, CA2/3 basket cells fired on the same phase as putative pyramidal cells, but, surprisingly, significantly later than downstream CA1 basket cells. This indicates a distinct modulation of CA3 and CA1 pyramidal cells by basket cells, which receive different inputs. We observed unexpectedly large dendritic arborization of CA2/3 basket cells in stratum lacunosum moleculare (33% of length, 29% surface, and 24% synaptic input from a total of ∼35,000), different from the dendritic arborizations of CA1 basket cells. Area CA2/3 basket cells fired phase locked to both CA2/3 and CA1 gamma oscillations, and increased firing during CA1 sharp waves, thus supporting the role of CA3 networks in the generation of gamma oscillations and sharp waves. However, during ripples associated with sharp waves, firing of CA2/3 basket cells was phase locked only to local but not CA1 ripples, suggesting the independent generation of fast oscillations by basket cells in CA1 and CA2/3. The distinct spike timing of basket cells during oscillations in CA1 and CA2/3 suggests differences in synaptic inputs paralleled by differences in dendritic arborizations
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