Focus on neural computation and theory

Efficient codes and balanced networks

Journal name:
Nature Neuroscience
Year published:
Published online


Recent years have seen a growing interest in inhibitory interneurons and their circuits. A striking property of cortical inhibition is how tightly it balances excitation. Inhibitory currents not only match excitatory currents on average, but track them on a millisecond time scale, whether they are caused by external stimuli or spontaneous fluctuations. We review, together with experimental evidence, recent theoretical approaches that investigate the advantages of such tight balance for coding and computation. These studies suggest a possible revision of the dominant view that neurons represent information with firing rates corrupted by Poisson noise. Instead, tight excitatory/inhibitory balance may be a signature of a highly cooperative code, orders of magnitude more precise than a Poisson rate code. Moreover, tight balance may provide a template that allows cortical neurons to construct high-dimensional population codes and learn complex functions of their inputs.

At a glance


  1. The conundrum of Poisson rate codes and E/I balance.
    Figure 1: The conundrum of Poisson rate codes and E/I balance.

    (a) When seeking to produce an average rate of 4 spikes per second, the number of spikes in each trial will fluctuate for a Poisson rate code (left), yet remain constant for a perfect, regular rate code (right). (b) As the average rate or number of spikes per second needed grows, the variability of the Poisson rate code (s.d.; black bars) grows as well. (c) When encoding an analog number, any rate code will cause coding errors due to the unavoidable discretization of the spike count. While these coding errors drop as 1/M for M spikes (green), the Poisson rate coding errors drop only as owing to the additional problems caused by unreliability (black). (d) In a neuron receiving both excitatory and inhibitory currents, even a small imbalance toward excitation will cause a spike train output that is far more regular than a Poisson spike train. (e) If excitatory and inhibitory currents are balanced on a slow time scale yet exhibit fast and uncorrelated (loose) fluctuations, then the net input current will depend on these faster fluctuations only, causing a random walk to threshold in the membrane potentials. (f) If the faster fluctuations are strongly correlated but shifted in time—for example, by inhibition trailing excitation (see inset)—then the system moves into a regime of tight E/I balance.

  2. Schematic illustration of key experimental findings.
    Figure 2: Schematic illustration of key experimental findings.

    (a) Co-tuning of excitatory (red) and inhibitory (blue) inputs into sensory neurons. Black represents the tuning curve of the neuron. We distinguish two types of observations. In one set of observations (left), excitatory and inhibitory inputs are balanced for all conditions or stimuli; firing occurs essentially because of temporal mismatch between excitation and inhibition (see b). In another set of observations (right), they are balanced only at the preferred stimuli of a neuron (right), and otherwise inhibition dominates, causing a sharpening of the tuning curve. (b) During stimulus presentations, inhibitory inputs match excitatory inputs in size and trail them by a few milliseconds only. (c,d) During spontaneous activity, inhibition still matches and trails excitation by a few milliseconds. Shown are the time course of inhibitory and excitatory currents and a magnification in time. (c) Switching between UP and DOWN states. (d) Gamma oscillations in the hippocampus. (e) Membrane and spike train correlations between nearby or similar pairs of neurons. Neurons with similar inputs usually exhibit strong co-fluctuations in the membrane voltage and much weaker or nonexistent correlations in their spike times (right). Panel e reproduced from ref. 54, Elsevier.

  3. Coding with tightly balanced networks.
    Figure 3: Coding with tightly balanced networks.

    (a) Using balance for coding. Here the network receives an excitatory input signal (red), which for technical reasons combines x(t) and its derivative (refs. 16, 17, 18). The excitatory input signal in turn is balanced by an inhibitory feedback loop (blue). The feedback loop subtracts the network's representation of the input from the actual input, so that only the momentary representation or decoding error is fed into the network. In the tightly balanced regime, the inhibitory loop cancels the excitatory input, and therefore . (b) This schema can be implemented in an architecture of excitatory (red) and inhibitory (blue) neurons. Here the signal x(t) is fed into the dendritic tree of the excitatory neurons. The spiking output of these neurons represents the readout , which is fed back through an inhibitory loop to cancel the input signals. (c) Toy example with four identical neurons. Shown are the input signal x(t), its estimate , and the voltages Vi, and spikes (black dots) of the four neurons. The neuron's voltages track the error . The neurons take turns firing whenever this error exceeds a threshold. The fast mutual inhibition prevents more than one neuron from firing in each integration cycle. In turn, the input signal is well approximated by the population of four neurons. (d) Error space plot. In larger networks, the voltages of the neurons track projections (small green dots) of the reconstruction error (large green dot). Every neuron (index i) represents one specific direction in this error space, given by its feedforward weights Di. Whenever the error becomes too large in this direction, the neuron's voltage Vi exceeds its threshold Ti and the neuron spikes, thereby updating the readout and minimizing the reconstruction error . (e) In larger networks—that is, ones with hundreds or thousands of neurons that represent many variables—the voltages and spike trains no longer look as cartoonish as in c. Shown here are the membrane potentials of two neurons with similar tuning. The membrane potentials are strongly correlated, while the spike trains are only weakly correlated.

  4. Computations with tightly balanced networks.
    Figure 4: Computations with tightly balanced networks.

    (a) Using balance for computation. Here the network balances an excitatory input signal c(t) and excitatory recurrent feedback with an inhibitory feedback loop (compare Fig. 3a). In the tightly balanced regime, the inhibitory and excitatory inputs cancel, and therefore , so that the network implements a particular differential equation. (b) This schema can likewise be implemented in a network of excitatory and inhibitory neurons. Just as in Figure 3b, some of the inhibitory neurons will mediate fast inhibition to construct the efficient population code. In addition to those found in Figure 3b, a second set of recurrent connections, which may include additional inhibitory or excitatory neurons (not shown), provides slower feedback and allows the system to perform computations. (c) A neural integrator as a specific implementation of this type of architecture. Here the balanced condition dictates that the recurrent input, –dx/dt, cancels the feedforward input c. The resulting computation, dx/dt = c(t), implements an integrator. Negative derivative feedback is implemented through a fast inhibitory and a slower excitatory loop. (d) Implementation of a harmonic oscillator in a network of N = 100 neurons, using the network architecture in b, adapted from ref. 16. Shown are the self-generated oscillations of the readout variables [] and []. The inset highlights the discrete nature of the representation. (e) Spike raster of all 100 neurons in the network during a single oscillation phase. (f) Trial-averaged, time-varying firing rates of selected neurons.


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  1. Laboratoire de Neurosciences Cognitives, École Normale Supérieure, Paris, France.

    • Sophie Denève
  2. Champalimaud Centre for the Unknown, Lisbon, Portugal.

    • Christian K Machens

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