The Energy Homeostasis Principle: Neuronal Energy Regulation Drives Local Network Dynamics Generating Behavior

Abstract

A major goal of neuroscience is understanding how neurons arrange themselves into neural networks that result in behavior. Most theoretical and experimental efforts have focused on a top-down approach which seeks to identify neuronal correlates of behaviors. This has been accomplished by effectively mapping specific behaviors to distinct neural patterns, or by creating computational models that produce a desired behavioral outcome. Nonetheless, these approaches have only implicitly considered the fact that neural tissue, like any other physical system, is subjected to several restrictions and boundaries of operations. Here, we proposed a new, bottom-up conceptual paradigm: The Energy Homeostasis Principle, where the balance between energy income, expenditure, and availability are the key parameters in determining the dynamics of neuronal phenomena found from molecular to behavioral levels. Neurons display high energy consumption relative to other cells, with metabolic consumption of the brain representing 20% of the whole-body oxygen uptake, contrasting with this organ representing only 2% of the body weight. Also, neurons have specialized surrounding tissue providing the necessary energy which, in the case of the brain, is provided by astrocytes. Moreover, and unlike other cell types with high energy demands such as muscle cells, neurons have strict aerobic metabolism. These facts indicate that neurons are highly sensitive to energy limitations, with Gibb's free energy dictating the direction of all cellular metabolic processes. From this activity, the largest energy, by far, is expended by action potentials and post-synaptic potentials; therefore, plasticity can be reinterpreted in terms of their energy context. Consequently, neurons, through their synapses, impose energy demands over post-synaptic neurons in a close loop-manner, modulating the dynamics of local circuits. Subsequently, the energy dynamics end up impacting the homeostatic mechanisms of neuronal networks. Furthermore, local energy management also emerges as a neural population property, where most of the energy expenses are triggered by sensory or other modulatory inputs. Local energy management in neurons may be sufficient to explain the emergence of behavior, enabling the assessment of which properties arise in neural circuits and how. Essentially, the proposal of the Energy Homeostasis Principle is also readily testable for simple neuronal networks.

Keywords: behavior; emergent properties; energy; homeostasis; neuronal networks.

Figure 1

Homeostasis requires a balance between ATP production (metabolism) and ATP consumption (synaptic activity): (A) Production of energy molecules by metabolism supports neuron activity, in addition to cell maintenance processes, most notably by ATP [A(t)]. Neuronal homeostasis depends on a balance between production and consumption of high-energy molecules. (B) Synaptic activity has been estimated to amount for half of total ATP consumption [diagram redrawn from (Harris et al., 2012)]. For cellular homeostasis to be achieved, neurons must regulate their activity and metabolism in response to changing external perturbations. We propose that regulatory mechanisms, responsible for changes synaptic plasticity, reflect the requirement for maintaining a constant level of energy resources available for neurons to function.

Figure 2

Neuron activity induces changes in metabolism and synaptic activity to maintain homeostatic energy levels: Schematic illustration of neuronal response to an increase in activity. Above, in scale of colors, ATP concentration indicating from low to high A, with A_H at the center. Below, step 1 depicts a resting state with A(t₀) = A_H. In time step 2, the increase in activity would result in additional ATP consumption, and therefore reduce ATP concentration. We propose that neurons would respond to this perturbation by decreasing ATP consumption [C(t)] and increasing its ATP production [P(t)], as represented in time step 3. Finally, neurons would return to homeostatic levels of ATP (A_H), which is illustrated in time step 4. During all these steps, A(t) is colored following the above ATP concentration color scale. Some factors that contribute to ATP consumption are as follows: synaptic post-excitatory currents (excitatory inputs), firing rate (excitatory outputs), ion channel density, size of soma, dendrite arborization and axonal length, neurotransmitter recycling and release, and cytoarchitecture adaptations. Whereas, factors contributing to ATP production are: glycolysis supported by glucose or glycogen breakdown, oxidative phosphorylation supported by neuronal pyruvate, astrocyte-derived lactate, and ketone bodies. Neurons possess regulatory mechanisms that sense current energy levels (here represented by a rotating wheel with a floating balloon), and control production and consumption, to maintain homeostasis. Examples of these control mechanisms would include ATP-Sensitive K+ channels, and AMPK signaling.

Figure 3

Hypothetical experiment to evaluate the influence of energy availability on synaptic scaling: The influence of energy availability in synaptic plasticity could be evaluated empirically. Here we propose a simple hypothetical scenario and explain what outcomes we predict under each condition. (A) Cultured neurons are stimulated with bicuculine (Bic) for 48 h (denoted by a black bar), which presumably induces transient changes in ATP concentration [A(t)]. Neurons respond by increasing ATP production [P(t)] and reducing ATP consumption [C(t)] by reducing its firing rate, which leads to the reestablishment of homeostatic ATP concentration within the time window enclosed by dotted lines. (B) During stimulation, cultured neurons can be pharmacologically treated to partially inhibit oxidative phosphorylation (i.e., reducing ATP synthesis) denoted by a black arrow. Following the Energy Homeostasis Principle, we propose this will result in a further reduction of ATP concentration, which will induce an accelerated reduction in ATP consumption through the reduction of synapse firing rates. Thus, we propose that in this scenario the time window required to return to homeostasis is shortened. (C) Almost an identical protocol to (B) is applied to neurons; however, using an ATP mimetic molecule (denoted by the black arrow). We assume that ATP mimetic molecules would delay the reduction of synapsis firing rate by allosterically inhibiting AMPK, resulting in an enlarged period of energy consumption. Thus, we propose a wider time window before reaching A_H. All graphics follow Equations 4 and 5, with the additional assumption that the magnitude of the adjustment of P(t) and C(t) are proportional to the distance of ATP levels A(t) to homeostatic levels A_H. Results from these kinds of experiments could advance the understanding (and potentially manipulate) of the mechanisms responsible for neural adaptations, uncovering the relevant role of metabolic elements, such as metabolic sensors and/or nutrient availability.

Figure 4

Energy homeostasis: An integrated view of neurons, networks and behavior schematic of the three nested levels of the energy homeostasis system. Each level represents one unit and its proximal operations. Neuron refers to one neuron which must manage its energy consumption, which will trigger neuron-plastic changes. Many of these neurons will build a network, which has connectivity properties and population energy demands. Many networks working together will deploy behavior through motor actions, while also receive the sensory input. All levels present a two-way energy interaction between behavior, neural networks, and neurons. The figure intends to present how sensory input can be considered an energy demand at network and neuron levels, while motor output through behavior gives room to control part of the sensory input.

Figure 5

Depiction of Braitenberg's vehicle behavior as a controlled platform to study learning and network energy adaptation: In this figure we hypothesize, based on the Energy Homeostasis Principle, how a hybrot would learn. (A) Different behaviors observed at different learning levels of the Braitenberg's vehicle (top panel), and the corresponding sensory input (bottom panel) from an obstacle (circle centered to x symbol) is detected by a sensor (dashed area), while the vehicle explores the environment. (B) Learning curve of the vehicle when learning to avoid the obstacles, passing from frequent collisions to full avoidance behavior. (C) Network energy adaptation triggered by the sensory input while minimizing the energy stress.