328 research outputs found
The Dynamics of Balanced Spiking Neuronal Networks Under Poisson Drive Is Not Chaotic
Some previous studies have shown that chaotic dynamics in the balanced state, i.e., one with balanced excitatory and inhibitory inputs into cortical neurons, is the underlying mechanism for the irregularity of neural activity. In this work, we focus on networks of current-based integrate-and-fire neurons with delta-pulse coupling. While we show that the balanced state robustly persists in this system within a broad range of parameters, we mathematically prove that the largest Lyapunov exponent of this type of neuronal networks is negative. Therefore, the irregular firing activity can exist in the system without the chaotic dynamics. That is the irregularity of balanced neuronal networks need not arise from chaos
Transient Information Flow in a Network of Excitatory and Inhibitory Model Neurons: Role of Noise and Signal Autocorrelation
We investigate the performance of sparsely-connected networks of
integrate-and-fire neurons for ultra-short term information processing. We
exploit the fact that the population activity of networks with balanced
excitation and inhibition can switch from an oscillatory firing regime to a
state of asynchronous irregular firing or quiescence depending on the rate of
external background spikes.
We find that in terms of information buffering the network performs best for
a moderate, non-zero, amount of noise. Analogous to the phenomenon of
stochastic resonance the performance decreases for higher and lower noise
levels. The optimal amount of noise corresponds to the transition zone between
a quiescent state and a regime of stochastic dynamics. This provides a
potential explanation on the role of non-oscillatory population activity in a
simplified model of cortical micro-circuits.Comment: 27 pages, 7 figures, to appear in J. Physiology (Paris) Vol. 9
Balanced neural architecture and the idling brain
A signature feature of cortical spike trains is their trial-to-trial variability. This variability is large in the spontaneous state and is reduced when cortex is driven by a stimulus or task. Models of recurrent cortical networks with unstructured, yet balanced, excitation and inhibition generate variability consistent with evoked conditions. However, these models produce spike trains which lack the long timescale fluctuations and large variability exhibited during spontaneous cortical dynamics. We propose that global network architectures which support a large number of stable states (attractor networks) allow balanced networks to capture key features of neural variability in both spontaneous and evoked conditions. We illustrate this using balanced spiking networks with clustered assembly, feedforward chain, and ring structures. By assuming that global network structure is related to stimulus preference, we show that signal correlations are related to the magnitude of correlations in the spontaneous state. Finally, we contrast the impact of stimulation on the trial-to-trial variability in attractor networks with that of strongly coupled spiking networks with chaotic firing rate instabilities, recently investigated by Ostojic (2014). We find that only attractor networks replicate an experimentally observed stimulus-induced quenching of trial-to-trial variability. In total, the comparison of the trial-variable dynamics of single neurons or neuron pairs during spontaneous and evoked activity can be a window into the global structure of balanced cortical networks. © 2014 Doiron and Litwin-Kumar
Revisiting chaos in stimulus-driven spiking networks: signal encoding and discrimination
Highly connected recurrent neural networks often produce chaotic dynamics,
meaning their precise activity is sensitive to small perturbations. What are
the consequences for how such networks encode streams of temporal stimuli? On
the one hand, chaos is a strong source of randomness, suggesting that small
changes in stimuli will be obscured by intrinsically generated variability. On
the other hand, recent work shows that the type of chaos that occurs in spiking
networks can have a surprisingly low-dimensional structure, suggesting that
there may be "room" for fine stimulus features to be precisely resolved. Here
we show that strongly chaotic networks produce patterned spikes that reliably
encode time-dependent stimuli: using a decoder sensitive to spike times on
timescales of 10's of ms, one can easily distinguish responses to very similar
inputs. Moreover, recurrence serves to distribute signals throughout chaotic
networks so that small groups of cells can encode substantial information about
signals arriving elsewhere. A conclusion is that the presence of strong chaos
in recurrent networks does not prohibit precise stimulus encoding.Comment: 8 figure
Decorrelation of neural-network activity by inhibitory feedback
Correlations in spike-train ensembles can seriously impair the encoding of
information by their spatio-temporal structure. An inevitable source of
correlation in finite neural networks is common presynaptic input to pairs of
neurons. Recent theoretical and experimental studies demonstrate that spike
correlations in recurrent neural networks are considerably smaller than
expected based on the amount of shared presynaptic input. By means of a linear
network model and simulations of networks of leaky integrate-and-fire neurons,
we show that shared-input correlations are efficiently suppressed by inhibitory
feedback. To elucidate the effect of feedback, we compare the responses of the
intact recurrent network and systems where the statistics of the feedback
channel is perturbed. The suppression of spike-train correlations and
population-rate fluctuations by inhibitory feedback can be observed both in
purely inhibitory and in excitatory-inhibitory networks. The effect is fully
understood by a linear theory and becomes already apparent at the macroscopic
level of the population averaged activity. At the microscopic level,
shared-input correlations are suppressed by spike-train correlations: In purely
inhibitory networks, they are canceled by negative spike-train correlations. In
excitatory-inhibitory networks, spike-train correlations are typically
positive. Here, the suppression of input correlations is not a result of the
mere existence of correlations between excitatory (E) and inhibitory (I)
neurons, but a consequence of a particular structure of correlations among the
three possible pairings (EE, EI, II)
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