Energy use constrains brain information processing

Conrad, Mireille; Engl, Elisabeth; Jolivet, Renaud

doi:10.1109/iedm.2017.8268370

Cited by 15 publications

(24 citation statements)

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“…Figure 6C shows that this relationship (normalized to its peak) has a single well-defined peak close to the physiological gain of the synapse (gain = 1), when quantifying information flow using I or TEsum. In both those cases, this 'energetic efficiency of information flow' curve peaks close to gain = 1 and closely resembles what has been reported earlier, either for experimental data or in computational models [7][8][9][10].…”

Section: Results (3279 Words)supporting

confidence: 87%

“…Finally, we wanted to compare the application of these two information theoretic measures to a biophysically relevant scenario. We have recently demonstrated in computational models and experiments that a number of synaptic features can be explained as a trade-off between information flow and energy consumption: the low release probability of weak central synapses [7,9], the postsynaptic conductance at strong thalamic relay synapses [8], and the postsynaptic conductance at weak cortical synapses [10]. All these studies demonstrate that synapses and neurons, appear…”

Section: Results (3279 Words)mentioning

confidence: 98%

“…The existence of an information flow over energy optimum is not surprising. It stems from the basic principle that energy consumption scales roughly linearly with the biophysical parameters we studied (release probability or postsynaptic conductance), while information flow scales sigmoidally with those parameters, because of the necessity to overcome noise [7,9], or energetic barriers (the threshold for action potential generation) [8,10]. Thus, information flow over energy, dividing a sigmoid by a linear function, leads to a single well-defined optimum.…”

Section: Results (3279 Words)mentioning

confidence: 99%

“…One area of particular interest is the application of information theoretic concepts to spike trains, as they easily lend themselves to a reduction to binary sequences, whereupon action potentials are converted to 1s and the rest of electrophysiological traces to 0s. This reduction of spike trains to binary sequences has been used to measure information flow through synapses and neural networks, and propagation of information from the environment to the cortex through sensory pathways for instance [3][4][5][6][7][8][9][10]. The information theoretic quantity most often encountered in such contexts is the mutual information (I) [4].…”

Section: Introduction (828 Words)mentioning

confidence: 99%

“…We have recently published a series of experimental and computational works on the tradeoff between information flow and concomitant energy consumption in neurons and neural networks, in which we used either I [7][8][9] or TE [10]. This work is part of an emergent interest for energetic questions in neural information processing [13][14][15][16].…”

Section: Introduction (828 Words)mentioning

confidence: 99%

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Comparative performance of mutual information and transfer entropy for analyzing the balance of information flow and energy consumption at synapses

Conrad

Jolivet

2020

Preprint

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words)Information theory has become an essential tool of modern neuroscience. It can however be difficult to apply in experimental contexts when acquisition of very large datasets is prohibitive.Here, we compare the relative performance of two information theoretic measures, mutual information and transfer entropy, for the analysis of information flow and energetic consumption at synapses. We show that transfer entropy outperforms mutual information in terms of reliability of estimates for small datasets. However, we also show that a detailed understanding of the underlying neuronal biophysics is essential for properly interpreting the results obtained with transfer entropy. We conclude that when time and experimental conditions permit, mutual information might provide an easier to interpret alternative. Finally, we apply both measures to the study of energetic optimality of information flow at thalamic relay synapses in the visual pathway. We show that both measures recapitulate the experimental finding that these synapses are tuned to optimally balance information flowing through them with the energetic consumption associated with that synaptic and neuronal activity. Our results highlight the importance of conducting systematic computational studies prior to applying information theoretic tools to experimental data. 2 Author summary (265 words)Information theory has become an essential tool of modern neuroscience. It is being routinely used to evaluate how much information flows from external stimuli to various brain regions or individual neurons. It is also used to evaluate how information flows between brain regions, between neurons, across synapses, or in neural networks. Information theory offers multiple measures to do that. Two of the most popular are mutual information and transfer entropy. While these measures are related to each other, they differ in one important aspect: transfer entropy reports a directional flow of information, as mutual information does not. Here, we proceed to a systematic evaluation of their respective performances and trade-offs from the perspective of an experimentalist looking to apply these measures to binarized spike trains. We show that transfer entropy might be a better choice than mutual information when time for experimental data collection is limited, as it appears less affected by systematic biases induced by a relative lack of data. Transmission delays and integration properties of the output neuron can however complicate this picture, and we provide an example of the effect this has on both measures. We conclude that when time and experimental conditions permit, mutual information -especially when estimated using a method referred to as the 'direct' method -might provide an easier to interpret alternative.Finally, we apply both measures in the biophysical context of evaluating the energetic optimality of information flow at thalamic relay synapses in the visual pathway. We show that both measures capture the original experimental finding that those synapses are tuned...

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Section: Results (3279 Words)supporting

confidence: 87%

Section: Results (3279 Words)mentioning

confidence: 98%

Section: Results (3279 Words)mentioning

confidence: 99%

Section: Introduction (828 Words)mentioning

confidence: 99%

Section: Introduction (828 Words)mentioning

confidence: 99%

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Comparative performance of mutual information and transfer entropy for analyzing the balance of information flow and energy consumption at synapses

Conrad

Jolivet

2020

Preprint

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Modelling Neuromodulated Information Flow and Energetic Consumption at Thalamic Relay Synapses

Conrad

Jolivet

2020

Artificial Neural Networks and Machine Learning – ICANN 2020

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Recent experimental and theoretical work has shown that synapses in the visual pathway balance information flow with their energetic needs, maximising not the information flow from the retina to the primary visual cortex (bits per second), but instead maximising information flow per concomitant energy consumption (bits of information transferred per number of adenosine triphosphate molecules necessary to power the corresponding synaptic and neuronal activities) [10,5,11]. We have previously developed a biophysical Hodgkin-Huxley-type model for thalamic relay cells, calibrated on experimental data, and that recapitulates those experimental findings [10]. Here, we introduce an improved version of that model to include neuromodulation of thalamic relay synapses' transmission properties by serotonin. We show how significantly neuromodulation affects the output of thalamic relay cells, and discuss the implications of that mechanism in the context of energetically optimal information transfer at those synapses.

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Artificial neurovascular network (ANVN) to study the accuracy vs. efficiency trade-off in an energy dependent neural network

Kumar

Mayakkannan

Manojna

et al. 2021

Sci Rep

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Artificial feedforward neural networks perform a wide variety of classification and function approximation tasks with high accuracy. Unlike their artificial counterparts, biological neural networks require a supply of adequate energy delivered to single neurons by a network of cerebral microvessels. Since energy is a limited resource, a natural question is whether the cerebrovascular network is capable of ensuring maximum performance of the neural network while consuming minimum energy? Should the cerebrovascular network also be trained, along with the neural network, to achieve such an optimum? In order to answer the above questions in a simplified modeling setting, we constructed an Artificial Neurovascular Network (ANVN) comprising a multilayered perceptron (MLP) connected to a vascular tree structure. The root node of the vascular tree structure is connected to an energy source, and the terminal nodes of the vascular tree supply energy to the hidden neurons of the MLP. The energy delivered by the terminal vascular nodes to the hidden neurons determines the biases of the hidden neurons. The “weights” on the branches of the vascular tree depict the energy distribution from the parent node to the child nodes. The vascular weights are updated by a kind of “backpropagation” of the energy demand error generated by the hidden neurons. We observed that higher performance was achieved at lower energy levels when the vascular network was also trained along with the neural network. This indicates that the vascular network needs to be trained to ensure efficient neural performance. We observed that below a certain network size, the energetic dynamics of the network in the per capita energy consumption vs. classification accuracy space approaches a fixed-point attractor for various initial conditions. Once the number of hidden neurons increases beyond a threshold, the fixed point appears to vanish, giving place to a line of attractors. The model also showed that when there is a limited resource, the energy consumption of neurons is strongly correlated to their individual contribution to the network’s performance.

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Energy use constrains brain information processing

Cited by 15 publications

References 9 publications

Comparative performance of mutual information and transfer entropy for analyzing the balance of information flow and energy consumption at synapses

Comparative performance of mutual information and transfer entropy for analyzing the balance of information flow and energy consumption at synapses

Modelling Neuromodulated Information Flow and Energetic Consumption at Thalamic Relay Synapses

Artificial neurovascular network (ANVN) to study the accuracy vs. efficiency trade-off in an energy dependent neural network

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